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Determinants of Technological Innovation:Current Research Trends and Future

ProspectsVangelis Souitaris

The Entrepreneurship Centre, Imperial College, London, U.K.

Abstract: This chapter is a review of several methodologies, which have been used to identify thedistinctive characteristics of innovative firms (determinants of technological innovation). Some ofthe problems affecting this research field are the diverse nature and non-standardised definitionand measurement of innovation itself, non-standardised measurements of the determinants,interrelated variables, different characteristics of firms targeted and finally different economicregions where the surveys take place. The chapter presents a portfolio model, which synthesisesprevious research results and may be used for country or industry specific studies.

Keywords: Innovation; Technological innovation; Determinants of technological innovation;Portfolio model.

Introduction

The evidence from the literature strongly supports theview that technological innovation is a major influenceon industrial competitiveness and national develop-ment (Tidd, 2001; Zaltman et al., 1973). Some firmsare more technologically innovative than others, andthe factors affecting their ability to innovate areimportant to management scholars, practising manag-ers, consultants, and technology policy-makers.

The author adopted the OECD definitions of tech-nology and technological innovation. Technology canbe interpreted broadly as the whole complex ofknowledge, skills, routines, competence, equipmentand engineering practice which are necessary toproduce a product or service. A new product requires achange in this underlying technology. Technologicalinnovation occurs when a new or changed product isintroduced to the market, or when a new or changedprocess is used in commercial production. The innova-tion process is the combination of activities—such asdesign, research, market investigation, tooling up andmanagement—which are necessary to develop aninnovative product or production process (OECD,1992).

The factors that affect a firm’s innovation1 rate arecalled ‘determinants of innovation’. They derive from awide range of aspects of the company such as internaland external communications, managerial beliefs, thefinancial situation, size, structure, quality of personnel,R&D effort, technical capabilities and market condi-tions (Souitaris, 1999).

This chapter examines the methodologies and toolsresearchers have used in order to identify the determi-nants of technological innovation, within organisations.The results of a large number of studies are sum-marised and a framework is extracted. Afterwards,some current research trends are presented. Finally aview of what should be done in the future is proposed,in order to expand upon our current knowledge.

On the Methodology of Studies on Determinants ofInnovationFrom as early as the late 1950s, much literature hasbeen published on the determinants of technologicalinnovation. To illustrate the amount of academicresearch in this area, Rogers (1983) refers to 3,085

1 Wherever the word ‘innovation’ is used in the text, it alwaysrefers to technological innovation.

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The International Handbook on InnovationEdited by Larisa V. Shavinina© 2003 Elsevier Science Ltd. All rights reserved

publications about the diffusion of innovation, ofwhich 2,297 are empirical research reports. He alsoadds that the number of publications was almost eighttimes more in 1983 than in 1962 (Rogers, 1983,p. xv).

In order to present better the objectives and themethodology of these studies, we can use the followingcategorisations:

(1) Categorisation according to the approach.

(a) Studies researching at the project level, look-ing for the determinants of success or failure ofinnovative projects.

The main characteristic of these studies is thatthe sample comprises new technological pro-jects. The objective is to correlate the successrate of the projects to a number of predefinedpossible determinants. This kind of research isknown as the ‘innovation or decision design’(Downs & Mohr, 1976) or the ‘objectapproach’ (Archibugi et al., 1994). Examplesof the object approach are the followingresearch works: Rubenstein et al. (1976),Rothwell (1977, 1992), Maidique & Zinger(1984) and Cooper (1979, 1999, 2002).

(b) Studies researching at the firm level, lookingfor the determinants of the firms’ ability toinnovate.

The unit of analysis in these studies is the firm,and this kind of research is known as the‘multiple innovation research’ (Downs &Mohr, 1976) or the ‘subject approach’ (Archi-bugi et al., 1994). There are two possibilities inthis approach: the variables can determineeither the firm’s rate of innovation, or itsability to succeed and to benefit from itsinnovative technology. The interest in the rateof innovation as a dependent variable stemsfrom the implicit hypothesis that firms intro-ducing innovation regularly are more likely tosustain a large number of successful innovativeproducts and processes (even if some of theprojects fail). Examples of the subjectiveapproach are the following research works:Mohr (1969), Miller (1983), Ettlie et al.(1984), Khan & Manopichetwattana (1989a)and Hajihoseini & de la Mare (1995).

(2) Categorisation according to the number of testeddeterminants of innovation.

(a) Studies testing a large set of factors. Thesestudies try to identify important determinantsof innovation, testing integrated models with awide range of variables. They usually have theintention, using regression equations with thedeterminants as independent variables, of pre-

dicting the highest possible proportion of thevariation of the dependent variable (innovationrate). Examples of research works of this kindare: Duchesneau et al. (1979), Miller & Friesen(1984), and Swan & Newell (1994).

(b) Studies that test one or a few specific factorslike, for example, participation in professionalassociations, or formalisation of structure.Usually those studies use more sophisticatedand detailed measures of the variable(s),intended to identify a possible correlationbetween the tested variable(s) and the depend-ent variable (innovation). However, they areonly able to explain a portion of the variance inthe rate of innovation. Examples of researchworks of this kind are: Mansfield (1963) onsize and structure, Sapolsky (1967), and Hage& Dewar (1973) also on structure, Kets deVries (1977) on personality of the entrepre-neur, Tushman & Scandel (1981) ontechnology gatekeepers, Miller et al. (1982) ontop executive locus of control, Chon & Turin(1984) on structure and decision-making pro-cedures, Newell & Swan (1995) and Swan &Newell (1995) on membership in professionalassociations (for detailed lists of studies seeChiesa et al. (1996) and Brown & Eisenhardt(1995)).

The Problem of the Inconsistency of the ResultsTo date, the research carried out has been unable toconclude on the relevant variables or their exact impacton innovation. Although similar variables have beentested by different researchers, the results have showndiffering degrees of impact on innovation. Most often,from the tested set of determinants, different ones werefound to be significantly correlated to the innovationrate in each empirical survey. In some cases there waseven disagreement as to whether a factor actuallycorrelated positively or negatively to the rate ofinnovation (Downs & Mohr, 1976; Wolfe, 1994). Forexample, firm size is a highly disputed variable (Khan,1990). The instability of the determinants from case tocase frustrates integrated theory-building efforts.(Downs & Mohr, 1976; Tidd, 2001; Wolfe, 1994).

Duchesneau et al. (1979) demonstrated this incon-sistency of results, duplicating a large number ofprevious studies. They deliberately used the samemeasures of determinants, but the sample was from onespecific industrial sector (footwear industry). Theirresults were different from the original studies, mainlyconcerning the relative extent to which differentvariables correlate to innovation.2

2 We should mention here Damanpour’s (1991) objection tothe various assertions about instability of results. His view isbased on a meta-analysis of previous results and is worthreading.

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Vangelis Souitaris Part VII

Having reviewed a large number of studies, theauthor has identified a number of possible reasons forthe inconsistency of these results (Souitaris, 1999).Determinants of innovation, and in particular theirdegree of correlation to the rate of technologicalinnovation, are dependent upon the following factors:

(1) Nature, definition and measurement of innovationitself.

The important determinants can be differentiatedby the nature of innovation, for example high-costvs. low-cost innovation, simple vs. complex inno-vation and incremental vs. radical innovation (e.g.Dewar & Dutton, 1986; Tornatzky & Klein, 1982).Determinants of high-cost innovation appear verydifferent to those of low-cost innovation. Downs &Mohr (1976) found that a wealth of resourceswould predict the former very differently to thelatter. Ettlie et al. (1984) found that, while‘incremental’ innovation may be enhanced by adecentralised structure, ‘radical’ innovationrequires a more centralised structure with partic-ular emphasis on decision-making and a higherlevel of support and involvement from top man-agement.

More recenty innovation typologies are pro-duced by Clark & Wheelwright (1992) (research oradvanced development; breakthrough develop-ment; platform or generational; derivative orincremental) and Christensen (1997) (‘sustaining’innovation vs. ‘disruptive’ innovation).

An additional problem is the lack of a standarddefinition of technological innovation (Garcia &Calantone, 2002). What is included in, or excludedfrom, the definition of technological innovation isan important issue which needs to be addressed.Should aesthetic improvements in the matters ofstyle, design or re-packaging be included astechnological innovation? What degree of changeis required for a product or process to beconsidered as technological innovation? There is adifficulty in interpreting the terms used in defini-tions such as ‘significant’ or ‘considerable’ (Smith,1988 outlines the variations in the definition ofinnovation). In addition, should the definitiondistinguish between product and process innova-tions or between the development of completelynew products and the incremental modification ofexisting products in a systematic way? The differ-ent definitions and interpretations of technologicalinnovation have led to variations in the identifieddeterminants—hence the ongoing research interestinto this subject (for good and current discussionsof this problem see Garcia & Calantone, 2002;Souitaris, 1999; Tidd, 2001).

Also, there is no standard measurement oftechnological innovation. There are two levels ofinnovation measurement referred to in literature

(Duchesneau et al., 1979). Firstly, at the micro-level—where the adoption of a number of industryspecific innovations is measured—innovations areselected as being representative, by a group ofindustrial experts or by the researchers readingindustry specific magazines. Second, there is theaggregated level where the rate of innovation of afirm is measured in various ways such as thenumber of new products and processes, thepercentage of sales due to new products or thenumber of patents filed. Whatever decision is takenon the measurement of innovation to be used in aparticular study can influence the results on theinnovation determinants.

(2) Measurement of the determinants of innovation.

In the literature, there are two types of determi-nants of innovation. The first type includesvariables measuring facts such as the size of thefirm, number of graduates, size of innovationbudget, etc. The above are straightforward andeasy-to-measure variables, with highly reliablemeasurements. They are also easily transferableover different studies. For instance, the number (orthe logarithm) of personnel is a generally standar-dised measure of a firm’s size (see, for example,Kimberly & Evanisko, 1981).

The second type of variables includes percep-tions and attitudes of the respondents (such asperceptions of the intensity of competition orattitudes towards risk-taking), as well as generaland usually subjective concepts (like centralisationof power,3 complexity of knowledge4 and aware-ness of strategy). Many of those variables are thenbroken down into a number of items and aremeasured using scales (see section ‘The PortfolioModel of Starting Variables’ for references onindividual constructs and measures).

This second type of variables is no less impor-tant a predictor of innovation performance than thehard measures of the first type. However, thedefinitions of the soft variables are subjective anddepend upon the author’s perception. There are noconsistent definitions and measures of concepts asgeneral as the ‘scanning of the environment’ or‘environmental heterogeneity’ (Miller & Friesen,1984), or ‘formalisation’5 and ‘centralisation’(Hage & Aiken, 1970). The concepts may besimilar but the way in which they are actuallybroken down into variables and measured usingscales differs. The different definitions and

3 Degree to which power and control in a system areconcentrated in the hands of relatively few individuals.4 Degree to which an organisations’ members possess arelatively high level of knowledge and expertise.5 Degree to which an organisation emphasises following rulesand procedures in the role performance of its members.

Determinants of Technological InnovationChapter 7

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measurement of determinants referring to similarconcepts makes the comparison of results moredifficult.

For example, it is difficult to compare a generalvariable called “scanning of the environment”(Miller, 1983)—which is measured using a largenumber of scale items including many forms ofcommunication with the external environment—with specialised variables like “the number ofcontacts made each year with representatives ofmachinery suppliers” and “the number of tradejournals read or scanned by innovation decisionmakers” (Webster, 1970). In both cases the varia-bles refer to the same concept of collectinginformation but they are not directly comparable,due to the different scope of the definitions.

Another problem for someone trying to compareprevious results is that not all studies define themeasurements of each variable clearly. Manyof them (especially journal papers) give justlists of determinants, underestimating the impor-tance of their actual measurement (for example, inthe seminal work of Miller (1983), the measure-ment of the variables is not clear).

In addition to this, it is worth mentioning thatmost of the variables are interrelated, and thiscreates problems in the interpretation of the results.For instance, size is probably a surrogate measureof several dimensions that lead to innovation suchas total resources, slack resources and organisa-tional structure (Rogers, 1983). These relationshipsbetween variables and their effect on the finalresults are not easy to understand clearly due to thecomplexity of the issue.

(3) Effect of different stages of innovation process oninnovation rate.

Inconsistent results and low correlations of organi-sational structure variables with innovation canalso be caused by some of the variables beingrelated to innovation in one direction duringinitiation of innovation, and in the oppositedirection during implementation of innovation. Ithas been argued that low centralisation, highcomplexity and low formalisation can facilitateinitiation in the innovation process and that thesame structural characteristics can also hinder theimplementation of an innovation within an organi-sation (Sapolsky, 1967; Zaltman et al., 1973).

(4) Different kind of firms used as sample.

Miller (1983), Khan & Manopichetwattana(1989b) and Damanpour (1991), among others,found that different types of firms show differentdeterminants of technological innovation. Forexample, Rothwell, (1974, 1977) showed that thefactors associated with innovation were signifi-cantly different, or at least showed a different order

of importance, in different industrial sectors. Forexample, within the chemical industry, technicalfactors were most important, while in the scientificinstruments industry, market factors dominated(Rothwell, 1974). Mohr (1969) has also referred toa moderating effect of the size of the firm on therelative importance of its determinants of innova-tion. For example, top management characteristicsand attitudes were found to be more importantinnovation determinants for small firms, due to themore active involvement of top managers in theinnovation process (also see Carrier, 1994;Lefebvre et al., 1997).

These and similar findings indicated that studieswhich only look at the industry as a whole cannotbe generalised. They cannot be directly comparedto studies, which deal only with one industrialsector, or one particular type of organisation (e.g.multinational). The fact that different results areachieved, according to the type of firm, illustratesthe problem involved in trying to achieve a unifiedtheory of determinants of innovation, which can beapplied to all situations.

(5) Different geographical regions in which theempirical surveys take place.

Much of the available literature in this area isbiased towards investigating determinants of inno-vation in the U.S. or other industrialised Westerncountries. Often the importance of the region in theinterpretation of the results is overlooked (Boyaci-giller & Adler, 1991; Drazin & Schoonhoven,1996).

White (1988) and Souitaris (1999), amongothers, indicated that the characteristics of innova-tive firms are strongly influenced by economicdevelopment and the management culture in theregion.

In conclusion, the fact that there seemed to be nounified theory concerning the determinants ofinnovation has reduced the amount of publishedstudies and the effort devoted to the subject afterthe late 1970s. Rogers (1983) argued that afterseveral hundred studies of organisational innova-tiveness were completed in the 1950s, 1960s and1970s, this approach to innovation in organisationsbecame passé. However, the problem was notresolved, and the crucial question of what thedeterminants of innovation are still remains open.

The Portfolio Model of Starting VariablesForrest (1991) and Tidd et al. (1997), among others,argued that there is no one best way of managing theinnovation process as it depends on firm specificcircumstances. Nelson & Winter (1977) introduced theconcept of ‘routines’, which are particular ways ofbehaviour which emerge as a result of repeatedexperiments and experience around what appears to be

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good practice. Different firms use different routineswith various degrees of success. There are generalrecipes from which general suggestions for effectiveroutines can be derived, but they must be customised toparticular organisations and related to particular tech-nologies and products (Tidd et al., 1997).

It is difficult to produce a universally applicablemodel of the determinants of technological innovation.Differences in the industrial sectors and geographicalregions all have an effect, which is very hard toquantify or exclude. Taking this into account, theauthor has developed a working ‘portfolio model’ ofpotential determining variables (Souitaris, 1999, 2002).The full list of determinants in the model is not alwaysapplicable—there are different sets of important deter-minants, depending upon certain environmentaldimensions that underlie the analysis (such as eco-nomic development and managerial culture). Thestudy’s model is intended to be a starting point forempirical research, in order to explore the con-tingencies.

The routines associated with innovation are exten-sive, and their strength of association is specific toparticular conditions for reasons explored in theprevious section. However, collectively the determi-nants of innovation tend to cluster around key themes(Tidd et al., 1997) presented in Table 1. The tabledemonstrates a comparative presentation of models inthe literature that attempt to integrate the determinantsof innovation. Common classes of factors appearthroughout the different models focusing on ‘context’(external environment and firm’s profile), ‘strategy’,

‘scanning external information’ and ‘organisationalstructure’.

Innovation textbooks (such as Ettlie, 2000; Tidd etal., 2001; Trott, 1998) and papers with practicalorientation (Bessant, 2003; Cooper, 1999, 2002) advisestudents and practitioners drawing from this generallyacceptable body of knowledge. However, despite theapparent similarity of integrative models of determi-nants of innovation at the aggregated level, there ismore variety when it comes to operationalisation andempirical testing. The literature includes a largenumber of individual indicators falling into the abovegeneral variable categories;6 The variables in ourportfolio model were categorised in four classes, in linewith the integrative models of determinants of innova-tion reviewed previously (see Table 1 and Fig. 1).

The presentation of the portfolio model whichfollows covers two types of sources: (1) conceptualworks that introduced the general themes and proposedtheir relationship with firm innovation; and (2) studies(mainly empirical) that associated innovation rate withspecific indicators, within the general themes.

(1) Contextual Variables.

Organisations are viewed by several theoreticalperspectives as adaptive systems, and this suggeststhat contextual variables may have a causalinfluence on strategy and structure. Examples ofsuch theoretical perspectives are the contingency

6 Chiesa et al. (1996) and Souitaris (1999) offered detailedliterature-based frameworks of operational indicators.

Table 1. A comparison of ‘integrated’ models of determinants of innovation.

Miller &Friesen(1984)

Khan &Manopichet-

wattana(1989)

R. Miller& Blais(1992)

Rothwell(1992)

Tidd, Bessant &Pavitt (1997)

Souitaris(1999)

This Portfolio

Environment Competitiveenvironment

Context Economicvariables

Context

Firm’s profile Corporate

Decision-making

Strategy Strategy conditions Strategy* Strategicvariables

Strategy

Entrepreneurialattitudes

Functions Process Implementationmechanisms

Information-processing Tactical

Externalcommunications



Structure Structure Structure variables Organisationalcontext

Internalcapabilities

Organisationalcontext

* Contextual variables included in the ‘strategy’ theme.


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Figure 1. The portfolio model of determinants of innovation.

Source: Souitaris (2002a).

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theory (Burns & Stalker, 1961; Donaldson, 1996),institutional theory (Parsons, 1966), resourcedependence (Aldrich, 1979; Barney, 2001), pop-ulation ecology (Hannan & Freeman, 1977), andindustrial economics (Freeman, 1982). The lit-erature also includes interesting discussions on theimpact of environmental variables (Miller & Blais,1992). There are those who consider the impact ofthe environment on the firm’s strategy and behav-iour to be highly important (Weber, 1947), andothers who claim that it is the organisations whichselect and even structure their environment (Miller,1989). For an excellent review and critique of allthe above main theories in the ‘environmentalschool’ of the management literature see Min-tzberg et al. (1998).

The current study has used two types ofcontextual variables in the portfolio model:

(a) Firm’s profile: Literature in this area connectsinnovation with factors such as the age of thefirm (Nejad, 1997), growth rate (Smith, 1974),profitability (Mansfield, 1971) and earningsfrom exports (Calvert et al., 1996);

(b) Competitive Environment: Evidence in theliterature points to the fact that the high rate ofchange of customer needs and intense com-petition are closely associated with a highinnovation rate (Khan & Manopichetwattana,1989a; Miller & Friesen, 1984).

(2) Strategy-Related Variables.

A firm’s strategy can be viewed as a network ofdecisions, which need to be made in order toposition the firm within its environment and tocreate the organisational structure and processes.Since the 1960s, when the idea of corporatestrategy was first noted, there has been muchdebate between the two main schools of thought:the ‘rationalist’ school (Ansoff, 1965) and the‘incrementalist’ school (Mintzberg, 1987). Porter(1980) explicitly linked technology to ‘five forces’which drive competition within the industry (bar-gaining power of suppliers, threat of new entrants,bargaining power of buyers, threat of substitutesand intensity of rivalry). Porter’s ‘rationalist’approach suggests that managers need to analysethe external environment and, based on thisanalysis, they must define a course of action.However, the ‘incrementalists’ Teece & Pisano(1994) suggested a different approach to corporatestrategy, that of ‘dynamic capabilities’ underliningthe importance of dynamic change and corporatelearning.

Cooper (1984) was one of the first of theempirical scholars to identify an associationbetween corporate strategy and innovation per-formance of firms (see also Cooper’s chapter in

this volume). Our model incorporates four subsetsof strategy-related indicators:

(a) Innovation budget. Literature showed thatwhere there is a budget for innovation and inparticular when this budget is consistent overtime, the rate of innovation will be increased(Khan, 1990; Twiss, 1992);

(b) Business strategy. In firms with a well-definedbusiness strategy, including plans for newtechnology, the rate of innovation was found tobe higher (Rothwell, 1992; Swan & Newell,1995). Moreover, those firms, which had astrategy with a long-term horizon and couldcommunicate it to their employees, showed ahigher rate of innovation (Khan & Man-opichetwattana, 1989a);

(c) Management attitude. Literature also indicatesthat top managers of the more innovativecompanies have an internal ‘locus of control’.They consider that the performance of theirfirm depends on manageable practices ratherthan the influence of external environmentalfactors which they cannot control. (Miller etal., 1982). In addition, the top managers of themost innovative firms appear to have less fearof risk-taking (Khan & Manopichetwattana,1989b) and recognise that in a shorter-than-expected time scale, the new technology costscan be recovered (Eurostat, 1996). Finally,these managers consider that there is a ‘per-formance gap’ between how the firm currentlyperforms and how it could perform in an idealsituation (Duchesneau et al., 1979);

(d) CEO’s profile. This particularly relates to theage and status of the CEO—i.e. whether he/sheis also the owner or an appointed executive.The literature implies that a younger CEO whois also the owner will be more receptive toinnovation (Khan & Manopichetwattana,1989b).

(3) External Communications.

Another positive influence on the rate of innova-tion identified in the literature is the acquisitionand scanning of information (Tidd et al., 1997).Therefore, three subsets of innovation-related com-munications variables have been incorporated inthe model.

The first subset comprises the factors related tocommunication with the firms’ stakeholders. Theseare:

(a) Customers: personal meetings (Chiesa et al.,1996; Maidique & Zinger, 1984; Rochford &Rudelius, 1992), panel discussions (Chiesa etal., 1996), postal or telephone feedback(Chiesa et al., 1996), or quantitative market


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research for a broader customer profile (Khan& Manopichetwattana, 1989b);

(b) Suppliers of machinery and equipment: (Duch-esneau et al., 1979; Rothwell, 1992).

The second subset incorporates the factors relatedto the collection and scanning of informationwhich can be from sources such as public agencies(Carrara & Duhamel, 1995) or other firms (Alter &Hage, 1993; Bidault & Fiscer 1994; Trott, 2003).The membership of professional associations,(Swan & Newell, 1995), subscription to scientificand trade journals (Khan & Manopichetwattana,1989b), attendance at trade fairs (Duchesneau etal., 1979), access to and use of the Internet, and useof electronic patent and research databases tosearch for new technology are other ways ofcollecting information on innovation, albeit lessdirect. A ‘technology gatekeeper’—i.e. someonewhose role is specifically to search for informationon new technology—is another determining varia-ble according to some literature (Allen, 1986;Rothwell, 1992). Finally, simply by monitoringone’s competitor’s activities, a great deal of usefuland critical information can be discovered (Chiesaet al., 1996).

The third subset refers to the co-operation of thefirm with third parties such as universities andresearch institutions (Bonaccorsi & Piccaluga,1994; Lopez-Martinez et al., 1994); public andprivate consultants (Bessant & Rush, 1995; Pilog-ret, 1993); other firms in the form of joint ventures(Alter & Hage, 1993; Rothwell, 1992; Swan &Newell, 1995) or licensing (Lowe & Crawford,1984); and financial institutions as a source ofventure capital (EUROSTAT, 1996). The absorp-tion of public technology funds, where they areavailable, can be another determinant of innova-tion. (Smith & Vidvei, 1992).

(4) Variables Related to the ‘Organisational Context’.

Bureaucracy theory (Weber, 1947), classical man-agement (Gulick & Ulrick, 1938) andorganisational sociology (Blay & Schoenherr,1971) all emphasise the dominant influence of thestructural attributes of an organisation on itsbehaviour. However, this appears to work bothways—while predefined structural factors mayeither hinder or encourage innovation, yet othersinsist that structure can be modified as a functionof strategy to enhance the innovative potential offirms (Miller & Blais, 1992).

The organisational competencies incorporated inthe portfolio model are classified into six subsetsand are all based on the empirical literature:

(a) Technical competencies. Both the intensity ofR&D (Ducheneau et al., 1979; Ettlie et al.,1984) and the intensity of quality control

(Rothwell, 1992; Zairi, 1996) are associatedwith innovation;

(b) Market competencies. Cooper (1984) andMaidique & Zinger (1984) and Veryzer (2003)associated an effective marketing programmeand a broad distribution system with innova-tion.

(c) Education of personnel. In firms which had ahigher number of educated and technicallyqualified staff, there appeared to be a moreresponsive attitude to innovation (Carter &Williams, 1957; Nejad, 1997). Miller & Frie-sen (1984) suggested that ‘technocrats’ cameup with more than average innovative ideas.

(d) Breadth of experience of personnel. Thebroader the base of employees within a firmwho had managerial responsibilities, thehigher the rate of adoption of innovations(Becker & Stafford, 1967). Organisations inwhich the staff have more varied backgrounds,for example working experience in othercompanies and/or abroad will generally have amore positive attitude towards innovation(Carroll, 1967). Such employees can oftensuggest and implement ideas for innovation.

(e) Training. Hage & Aiken (1970) and Dewar &Dutton (1986) associated innovation with‘knowledge depth’, measured by the level ofprofessional training. On-the-job training hasalso been linked to the rate of innovation bymore recent authors (Nejad, 1997; Swan &Newell, 1995)—this training refers to bothprofessional training for engineers and manag-ers and technical training offered to theproduction employees.

(f) Internal ‘process’ variables. Innovative com-panies are less formalised than non-innovativeones (Cohn & Turin, 1984). The businessinnovative performance can be also enhancedby introducing thinking (or ‘slack’) time forengineers and management (EUROSTAT,1994) and by using cross-functional inter-disciplinary teams (Clark & Fujimoto, 1991;Cooper, 1990; Hise et al., 1990).

Another critical factor influencing innovation is theexistence of a ‘project champion’ (Cooper, 1979;Hauschildt, 2003; Rothwell, 1992). The ‘projectchampion’ is an individual who dedicates herselfto an innovation project and will give a personalcommitment to fulfilling that project (Scon, 1973).Burns & Stalker (1961), Rogers & Shoemaker(1971) and Rothwell (1992), have identified anassociation between internal communication andtechnological innovation. Finally, authors such asFelberg & DeMarco (1992), Twiss (1992) andChiesa et al. (1996) made a case that a firm’sinnovation potential can be enhanced by allowing

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employees to generate their own new ideas, byencouraging the circulation and communication ofsuch ideas, and by offering incentives of someform to the employees.

It is worth stressing again that the model of thisstudy was not intended to be exhaustive. Thefactors that can be related to innovation arenumerous and possibly change over time asmanagement practice is a dynamic process.

More Recent Issues and Considerations

Measuring Innovation—Using Portfolios of IndicatorsOne of the major problems facing innovation researchis the absence of common ground or definition. Themost commonly used indicators at the ‘aggregatedlevel’ are technology-based ones, including capitalexpenditure, expenditure on research and developmentand patent activity (OECD, 1982; Tidd et al., 1996) andthese have been used for the longest time. Thestrengths and weaknesses of technological indicatorshave long been recognised (see Pavitt & Patel, 1988;Smith, 1992). Although the definitions of these indica-tors are relatively consistent and data are collected ona routine basis, it can be argued that they measureinnovation input (effort towards innovation) rather thaninnovation output (actual results from the innovationeffort).

More recently, there has been a tendency for thoseundertaking innovation surveys to use innovationoutput or ‘market’ indicators, such as the number ofnew products and new processes adopted during aspecific time period (for good reviews of innovationsurveys see Archibugi et al., 1994 and Smith, 1992).These ‘innovation-count’ indicators have the drawbackthat products and processes are not directly comparableacross different industries. Neither can they account forthe economic significance of the innovations (Smith,1992).

As a response to these disadvantages, some research-ers have used ‘impact’ indicators, which attempt tocollect data on the proportion of sales directly relatedto new products over a particular time period (see forexample Meyer-Krahmer, 1984). These indicatorsshow the rate at which a firm changes its product linesand vary across different industries and probably overtime. However, impact indicators are good measures ofboth technological newness and economic significance(Smith, 1992).

Empirical literature seems to have suffered frominconsistent results over the years because of thedifficulty in capturing the complexity of innovationwith a simple, accurate measure (Duchesneau et al.,1979). Saviotti & Metcalfe (1984) and Tidd (2001)suggested that multi-indicators of innovation can offera more complete picture of innovation performance,since the issue could be investigated from severaldifferent points of view and the problem of incomplete-

ness of each one of the individual measures could beminimised. Hence, I propose a portfolio of sevenwidely used innovation indicators:

(1) Number of incrementally innovative productsintroduced in the past 3 years;

(2) Number of radically innovative products intro-duced in the past 3 years;

(3) Number of innovative manufacturing processesintroduced in the past 3 years;

(4) Percentage of current sales due to incrementallyinnovative products introduced in the past 3 years;

(5) Percentage of current sales due to radically innova-tive products introduced in the past 3 years;

(6) Expenditure for innovation in the past 3 years overcurrent sales. This includes R&D expenditure aswell as a wide set of other expenditures related toinnovation, such as the acquisition of technologyand know-how, tooling up, industrial engineering,industrial design, production start-up, traininglinked to innovation activities and marketing ofnew products;

(7) Number of patents acquired in the past 3 years.

Three types of indicators are used in the aboveportfolio:

(1) ‘Input’ measures (variables 6 and 7) indicating theeffort made towards innovation;

(2) ‘output’ measures (variables 1, 2 and 3) capturingthe rate of implementation of innovation; and

(3) ‘impact’ measures (variables 4 and 5) indicatingthe impact of the company’s innovative products.

Each type of measure is in itself incomplete (seeHansen, 1992; Smith, 1992; Souitaris, 1999), butcollectively they can be used to measure innovationactivity. They have now been accepted by the OECD asstandardised tools for future innovation surveys(OECD, 1992). One of the limitations of this indicator-portfolio model is its inability to capture innovationfailure and therefore to reveal the project success vs.failure ratio (Smith, 1989). This is a limitation whichhas to be acknowledged, because the level of analysisis the firm as a whole and not the individual project.

Two more ‘composite’ indicators that future readersmight want to consult before selecting their innovationmeasures are presented by Hollenstein (1996) andCoombs et al. (1996).

Narrowing the Scope—Taxonomies of Firms withSimilar Determinants of InnovationAs a response to the inconsistency of the innovationdeterminants, the contingency school of thoughtemerged (see Burns & Stalker, 1961; Downs & Mohr,1976; Tidd et al., 1997, 2001), suggesting that there isno universal ‘best’ way to manage innovation as thephenomenon is context-specific. In order to make theresults more meaningful and comparable, Wolfe (1994)urged future researchers to define clearly the contextual


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settings of their surveys (i.e. the stage of the innovationprocess, the innovation attributes and the organisationalcontext).

Many researchers have realised that innovationdeterminants can vary in different contexts and havenarrowed down the scope of their work. Some havedecided to concentrate on a narrow range of firms—byselecting similar firm size (for example small andmedium-sized enterprises) or firms of the same indus-try. For instance, Khan & Manopichetwattana (1989a)and Rothwell (1978) focused only on small firmsand Duchesneau et al. (1979) on the footwear indus-try.

Some authors proposed taxonomies of firms withdifferent determinants of technological innovation. Thefirst taxonomy was proposed by Burns & Stalker(1961). They distinguished between ‘mechanistic’ and‘organic’ organisations. Mechanistic organisationshave a lower complexity, higher formalisation andcentralisation, and lower internal and external commu-nication than organic organisations. In the 1980s,Miller & Friesen (1984) identified two types of firmconfigurations7 with different innovation determinants.These were ‘conservative’ firms with positive andsignificant correlation of innovation with information-processing, decision-making and structural variables,and ‘entrepreneurial’ firms with negative correlation ofinnovation with information-processing, decision-making and structural integration variables. The struc-ture of the firm took a back seat while the goals andstrategies of the company were viewed as the moreimportant driver for innovation. Khan & Mano-pichetwattana (1989b) developed five clusters of smallfirms with different strategy, structure and managerialattitudes. Each of these was shown to have its ownspecific factors determining innovation.

These taxonomies (for good discussions on innova-tion taxonomies see Souitaris (2002a) and Tidd (2001)had an unquestionable and novel value in the innova-tion management literature, accepting that thecharacteristics of highly innovative firms are specific toparticular conditions and trying to identify clusters offirms with common important determinants of innova-tion. However, the proposed classifications wereweighted towards perceptual criteria (such as the risk-taking, proactiveness, entrepreneurial strength andbelief in luck) rather than the factual measures such assize, industrial sector and common innovation type,hence the ongoing requirement for development ofmore precise and factual taxonomies. These could helpto clarify conflicting research results on determinantsof innovation (see section ‘The Problem of theInconsistency of the Results’).

The author of this chapter tested the applicability ofPavitt’s (1984) taxonomy (which derived from theeconomic school of thought) as an effective factualclassification that could benefit the management lit-erature searching for the determinants of innovation.Pavitt suggested that industrial sectors differ greatly inthe sources of technology they adopt, the users of thetechnology they develop, and the methods used bysuccessful innovators to appropriate the benefits oftheir activities. He produced a simple and practicalclassification with four categories of firms:

(1) ‘Supplier-dominated firms’. These firms are usu-ally small, they do not place much emphasis onR&D, and they have lower engineering capabil-ities. They take the majority of their innovativeideas from firms, which supply them with equip-ment or materials;

(2) ‘Large-scale producers’. These firms tend to bemuch bigger and instigate their own processtechnologies. They concentrate their resources inthis area, and usually diversify vertically intotechnological equipment, which is related to theirown technology. As a result they contribute to alarge extent to innovation in all sectors of theiractivity;

(3) ‘Specialised suppliers’. These firms tend to besmaller, perhaps mechanical or instrumental engi-neering firms. They also produce a high proportionof their own process technologies but focus moreof their innovative activities on new products foruse in other sectors. There is little diversification oftechnology and a relatively small contribution toinnovations produced in their principal sector ofactivity. Their end users and other firms outside thesector make a more significant contribution;

(4) ‘Science-based firms’. These companies are usu-ally firms in the chemical, pharmaceutical andelectrical and electronic engineering sectors,whose main source of technology is internal R&D.They produce a relatively high proportion of theirown process technology and of product innova-tions used in other sectors of the industry. Usuallylarge, most of their technological diversificationis within the corporation, and they produce arelatively high proportion of all the innovationsmade in their principal sector of activity.

Pavitt’s taxonomy was selected for the test because itproduced firm classes with a similar size, industrialsector and innovation type (three important moderatorscausing result instability in the management literature).The author expected that a simultaneous ‘control’ of allthe three moderators would reduce the variation of theinnovation determinants within classes and increase thevariation across classes.

An empirical test in a sample of 105 Greekcompanies showed that firms in different trajectories(categories of firms) of Pavitt’s taxonomy showed

7 Readers that would like to know more about the configura-tion school in strategic management (supporting the idea oftaxonomies), should refer to Miller (1986 and 1996) and Desset al. (1993).

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differences in the rate of technological innovation (forthe detailed results of the study, see Souitaris (2002a)).Innovation for ‘supplier-dominated’ firms was relatedto the competitive environment, acquisition of informa-tion, technology strategy, risk attitude and internalco-ordination. For ‘scale-intensive’ firms the importantdeterminants were related to the ability to raise fundingas well as the education and experience of personnel.For ‘specialised suppliers’ innovation was associatedwith high growth rate and exporting, as well as trainingand incentives offered to the employees to contributetowards innovation. ‘Science-based’ firms dependedupon technology-related variables, education andexperience of personnel, growth in profitability andpanel discussions with lead customers.

Using Pavitt’s taxonomy management scholars cansimplify the problem of multi-dimensional moderation.Size, industrial sector and type of innovation arecombined into a single dimension: the ‘technologicaltrajectory’.

On the basis of my own research described above, Ipropose a ‘two-step’ methodology to identifying thedistinguishing characteristics of innovative firms:

(1) A classification of firms according to ‘industrial-level’ moderators. Pavitt’s taxonomy has a highpractical value at this level. It conveniently aggre-gates ‘industrial-level’ factors, producing foursectoral firm classes, rather than a long list ofsectors;

(2) Identification of a set of management-relateddeterminants of innovation specific to each sectoralclass. In practice, this method offers the opportu-nity to customise innovation questionnaires andmeasure the right ‘type’ of variables according tothe firm’s class.

The International Dimension

Most of the empirical research on the determinants ofinnovation has been carried out in industrialiseddeveloped countries. Recently, there has also beensome interest in the particular conditions in AsianNewly Industrialised Countries (NICs) (see Hobday,1995; Kim et al., 1993), in developing countries suchas Iran (Nejad, 1997) and in transition economies inEastern Europe (Inzelt, 2003). Several authors sug-gested that using the findings of innovation studies intechnologically advanced countries to explain theinnovative behaviour in countries with a less developedtechnological base is likely to be inappropriate (Drazin& Schoonhover, 1996; Mishra et al., 1996; Nejad,1997; Souitaris, 1999).

A number of research paradigms have attempted toexplain the international differences in technologicaldevelopment and innovation at a conceptual level. Neo-classical economic theorists have placed emphasis onthe importance of a local supply of skills, specific localdemands, openness of communication, pressure from

competition and market structure (Nabseth & Ray,1974; Porter, 1990). The ‘national innovation systems’paradigm underlined the important role of deliberateintangible investment in technological learning activ-ities (involving institutions such as other firms,universities and governments and the links amongthem). Innovation systems theorists also stressed thenational incentive structures of temporary monopolyprofit from innovation and the firm-specific competen-cies (Lundvall, 1998; Patel & Pavitt, 1994). Theneo-contingency school of thought put forward thecase for the way in which the diffusion and utilisationof innovation in different countries could be affectedby systematic differences in business strategies, organi-sational forms and specific social processes, all ofwhich are mutually dependent (Slappendel, 1996;Sorge, 1991). The neo-institutional theorists placedmore importance on the prevailing national institu-tional frameworks and networks (e.g. professionalassociations). These could create standards of bestpractice which would encourage some technologies tobe diffused more widely than others (Di Maggio &Powell, 1983; Swan et al., 1999).

In spite of all the research into the nationaldifferences in the patterns of technological innovation,there is a need for more empirical research in order tofully understand the complexity of the issue (Moenaertet al., 1994; Patel & Pavitt, 1994; Swan et al., 1999).Moenaert et al. (1994) proposed an operational frame-work for future empirical research, which combines theelements of most of the conceptual paradigms. Accord-ing to Moenaert et al. the innovation process indifferent countries depends upon four ‘socio-eco-nomic’ dimensions: technological heritage,administrative heritage, market structure and regionalentrepreneurship with additional influence of thenational ‘cultural context’.

I have attempted to use this framework in order toempirically identify the determinants of innovation inGreece (an example of a European newly industrialisednation with a less developed technological base).8 The‘Greek studies’ (Souitaris, 2001a, 2001b, 2002b) arebased on a sample of 105 manufacturing companies inGreece, and the results are briefly summarised below.

Major-importance ‘organisational competencies’determining innovation were found to be the intensityof R&D, strength in marketing, proportion of uni-versity graduates and engineers in the staff, proportionof staff with managerial responsibility, proportion ofprofessional staff with previous experience in anothercompany and incentives offered to the employees tocontribute to innovation (Souitaris, 2002b). Regarding

8 The average GDP per person is $11,739 per annum, whichindicates a medium-level development compared for instanceto $23,478 per annum for a large Western European countrylike the U.K. and $1,352 per annum for a developing countrylike Iran (Economist, 1998).


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‘strategic variables’ important determinants of innova-tion included incorporation of technology plans in thebusiness strategy, managerial attitude towards risk,perceived intensity of competition and rate of changeof customer needs and finally status of the CEO(owner-CEOs were associated with a higher innovationrate than appointed CEOs). In general, top-manage-ment characteristics proved to be more important‘strategic’ determinants of innovation for the Greekfirms than corporate practices (Souitaris, 2001b).Regarding ‘external communications’ the empiricalresults supported two hypotheses for industrialisingcountries (proposed by Souitaris, 2001a): (1) searchingfor product-specific information is more important forinnovation than scanning more general market andtechnological information; (2) the co-operation withpartnering organisations (such as investing firms andjoint venture partners) is more important for innovationthan the co-operation with assisting organisations (suchas universities, consultants or government agencies)(Souitaris, 2001a).

A common observation which emerged from the‘Greek studies’ is that the ‘major importance’ determi-nants were generally scarce in the country’s context.For example, the Greek national culture is generallyrisk-averse (Hofstede, 1991), but the attitude towardsrisk was a highly important variable (Souitaris, 2001b).In Greece there is a low indigenous production andsupply of technology (Giannitsis & Mavri, 1993), butthe R&D intensity and the incorporation of technologyplanning into the business strategy were importantpredictors of a high innovation rate (Souitaris, 2001b,2002b). The Greek market has a traditionally low levelof competition because of protectionism measures, butthe perception of intense competition and demandingcustomers was strongly associated with a high innova-tion rate (Souitaris, 2001b). Despite the fact thatGreece suffers from an outdated educational systemwhich does not consider the needs of the industry(Tsipouri, 1991), education-related variables provedimportant determinants of innovation (Souitaris,2002).

The findings of the Greek studies put forward thehypothesis that the most important determinants ofinnovation in newly industrialised countries are thosewhich are generally absent in the country-specificinstitutional market and social context. In other words,the most innovative companies are those which canovercome the traditional rigidities of the context oftheir countries and incorporate rare attitudes andpractices for the local business environment. Thishypothesis requires further testing in innovationresearch.

Where are We Going from Now?As time passes by and management styles evolve, newdetermining variables appear, and the relative impor-tance of the old ones changes. Hence, it is recom-

mended that holistic empirical surveys be carried outperiodically, to act as yardsticks of our currentknowledge. Qualitative methodologies such as obser-vation and case studies would be useful from time totime in order to explore the perceptions of practisingmanagers, to capture emerging determinants and toidentify new lines of thinking for further quantitativeresearch.

The fact that the results show different patternsdepending on the region and/or the sectoral class,should be accepted and lived with. Hence, instead ofdevoting time and resources to the search for a unifiedtheory of innovation, we can use portfolio models suchas that presented in this chapter as a starting point andthen identify the determining variables with the highestpredictive power for the particular context. Using theset of important determinants as a base, auditingsystems can then be developed putting the researchresults into practice.

In my view, the most fruitful direction for furtherresearch would be to untangle the ‘black box’ of thecontingency theory. Contingency theory has beenaccused of having rather abstract and vague dimen-sions of the environment (Mintzberg et al., 1998). Weneed to map what determinants work under what exactenvironmental circumstances. Despite the fact that thisis a highly complex problem due to the number ofintervening variables, I propose work in two direc-tions.

(1) Empirical research on the important determinantsof innovation in countries and regions with differ-ent managerial cultures and stages of economicdevelopment. International surveys carried outunder exactly the same conditions (same industriesand same measurements for innovation and itsdeterminants) would be particularly useful;

(2) Empirical research in order to confirm and estab-lish the use of taxonomies, such as Pavitt’s‘technological trajectories’. The creation of taxon-omies of firms is encouraged in theorydevelopment, as it allows large amounts of com-plex information to be collapsed into moreconvenient categories, which are easier to compre-hend (Carper & Snizek, 1980).

We always have to keep in mind that research on thedeterminants of innovation can have immediate usableand practical outcomes. The results of these studieswill be valuable for: (1) company managers andconsultants who want to identify the keys to high rateof innovation and (2) public policy-makers, who cansee the impact of general ‘infrastructure’ variables likeeducation, training, venture capital and information onthe company’s innovation potential.

AcknowledgmentsThe author would like to thank Dr. R. F. de la Mare forhis contribution to these ideas and particularly Deborah

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Salmon for her valuable language-related editing of thetext.

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Innovation in Financial ServicesInfrastructure

Paul Nightingale

Complex Product System Innovation Centre, Science Policy Research Unit (SPRU), Universityof Sussex, U.K.

Abstract: Financial infrastructure is essential to the world economy yet is largely neglectedwithin the academic innovation literature. This chapter provides an overview of innovation infinancial infrastructure. It shows how external infrastructure technologies between institutionsimprove market liquidity by increasing the reach of markets. Internal infrastructure technologieswithin institutions are used to co-ordinate the profitable allocation of resources. The heavyregulation of the industry, the software intensity of modern infrastructure technologies, the wayin which they have multiple users and their increasing complexity create extra uncertainties intheir design and development. As a consequence, they have very different patterns of innovationfrom traditional consumer goods.

Keywords: Innovation; Capital goods; Infrastructure; Technology; Financial services.

There have been, since the world began three greatinventions. . . . The first is the invention of writing,which alone gives human nature the power oftransmitting, . . . its laws, . . . contracts, . . . annals,and . . . discoveries. The second is the invention ofmoney, which binds together . . . civilised societies.The third is the Oeconomical Table . . . whichcompletes (the other two) . . . by perfecting theirobject: (This is) the great discovery of our age, but ofwhich our posterity will reap the benefit.

Adam Smith, The Wealth of Nations, IV (ix), 38

AimsThis chapter aims to give an overview of howinnovation takes place in financial infrastructure, anduses a contingency theory approach to show how it canbe conceptualised using established models of innova-tion in capital goods.

IntroductionWithin the academic innovation literature, serviceshave traditionally been considered non-innovative.1

This perception is reflected in the disproportionate biastowards research into mass-production manufacturingtechnologies. This may partly be attributed to a relativelack of visibility compared to consumer goods, andpartly to the fact that service innovation is oftenintangible or dependent on innovation in bespokeinfrastructures, and is therefore poorly reflected bytraditional innovation indicators, such as patents.However, it is also because the nature of innovation inservices is different from innovation in the massproduction consumer goods traditionally studied in theliterature.

This academic neglect is unfortunate given theimportance of service innovation to GDP growth,employment, the economy, social change and thegeography of global cities such as London or New York(Barras, 1986, 1990; Freeman & Perez, 1988; Soete &Miozzo, 1989). In the OECD, services account forapproximately two-thirds of employment and eco-nomic value added (Bryson & Daniels, 1988; Gallouj& Weinstein, 1997; cf. Eurostat, 1998). The fact thatthe extent of employment of scientists and engineers inservices overtook manufacturing in the USA in 1990suggests that services are not as low-tech as theirprofile in the literature might suggest.

If one looks at financial services in OECD countries,they typically represent some 5–10% of GDP and a

1 Important exceptions can be found in Bell (1973) and Fuchs(1968).

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similar proportion of employment. For example, 5.4million people are employed in the U.S. bankingsector, which is more than twice the combined numberof employees in automobiles, computers, pharmaceuti-cals, steel and clothing (Frei et al., 1998, p. 1). Bankingis also extremely high-tech: global banks often spendover $1 billion a year on IT (the cost of technology issecond only to wages for most financial servicescompanies) and, as Barras (1986, 1990) has argued, itis a vanguard sector that has been one of the first toadopt and diffuse new information technology.

This chapter will use a contingency theory approachto achieve three aims: firstly, to identify and explore thekey features of innovation in financial services; sec-ondly, to relate these specific features to variousempirical studies on innovation; and thirdly, to try andexplain how the contingent features of innovation infinancial services infrastructure—in particular, com-plexity, software intensity, its multi-user nature andheavy regulation—can explain the various aspects oftheir patterns of innovation. It aims to provide anoverview of how innovation takes place within finan-cial infrastructure projects in order to draw out theimportant features.

The chapter explores the history of innovation infinancial services and provides definitions of some ofthe terms used in this chapter; compares innovation ininfrastructure and complex capital goods with innova-tion in mass production consumer goods; and exploresthe specific problems associated with innovation inembedded software. The final section draws conclu-sions.

Financial Services—Definitions and HistoryEarly research on services tended to stress theirintangible nature, heterogeneity, and the importance oftime constraints on service delivery (Lovelock, 1983).Time is important because the output of services isoften a ‘performance rather than an object’ (Lovelock,1983; Lovelock & Yip, 1996; McDermott et al., 2001,p. 333). Storage is impossible when production andconsumption are simultaneous, making the reliable andtimely co-ordination and control of resources highlyimportant. More recent academic literature has tendedto move away from clear-cut distinctions betweenintangible services and tangible manufacturing andnow understands them as a continuum (Brady &Davies, 2000; Miles, 1996; Uchupalanan, 2000). Theintangible aspect is dominant in certain areas such asteaching, and the tangible aspect in areas such asdetergent production. There is also a large middleground where they overlap, such as fast food. In thischapter, the difference between services and manu-facturing along this continuum is understood in termsof the tangibility of functions, so that service firms arepaid to perform a function, while manufacturingcompanies produce objects that are bought to provide afunction (Nightingale & Poll, 2000b).

The financial system performs the overall functionof moving money between savers and borrowers. Thisallows people to transfer their ability to use money totransform the world through time and space. This linksthe present to the future, allowing borrowers and saversto switch between current income and future spending.2

Prior to the development of money, a barter system wasused for trade but was limited to the ‘here and now’.Gradually, precious metals began to be used ascommodity money to measure and store value throughtime and space as a medium of exchange. Coins, forexample, were in use by the eighth century BC Later on,the risk and cost of carrying around large amounts ofprecious commodities saw the development of contractmoney that could be exchanged for deposited gold andsilver. Contract money gradually developed into fiat orfiduciary money that maintains its value even though itis not backed by gold. This development allowed amore extensive allocation of resources through timeand space, but its geographical locus was limited by itsbasis in personal rather than institutional trust. Finan-cial institutions perform various functions within thefinancial system, including using technological infra-structure to profitably and reliably deliver services, andso extend the institutional trust upon which thefinancial system depends.

Financial institutions are typically used to mediatethe relationship between savers and borrowers becausethey have specialised technical capabilities (see Mer-ton, 1975a, 1995; Merton & Brodie, 1995; Nightingale& Poll, 2000a). Firstly, they have extensive specialisedknowledge of the risks involved. Secondly, there aregenerally large differences in the amounts of moneythat borrowers and lenders have or need. Thirdly, theliquidity requirements of lenders and borrowers aredifferent. Liquidity refers to the ability to turn assetsinto cash quickly and cheaply. Lenders generally wantquick access to their cash, while borrowers wantlonger-term, more stable funding. Fourthly, financialinstitutions allow the pooling of savings and risk whichincreases the liquidity of long-term debt. Lastly,financial institutions can take advantage of economiesof scale by spreading the fixed costs of investments ininfrastructure over a large number of contracts.

The development of specialised financial institutionsand a sophisticated division of labour came aboutthrough an expansion of the market for financialservices following the emergence of the fiscal-militarystate in the 16th and 17th centuries. As early-Modernwars were fought by attrition, the ability to allocatefunds through time and space effectively determinedmilitary power and enabled smaller countries like

2 Money and financial contracts in general are only means toother ends—they allow one to inter-convert goods and labourthrough time and space: for example, allowing the young toborrow to buy a house and the old to save productively.

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Holland and England to take on France and Spain.3

This ‘financial revolution’ started when the provincialStates of Holland accepted collective responsibility forwar loans by securing them on future tax returns(Dickson, 1967, p. 63). The Bank of Amsterdam wasset up in 1609, with the English waiting until the costof a war with France in 1689 forced them to form theBank of England in 1694.4

Improved commercial finance quickly followed tofund the activities of the newly emerging joint stockcompanies. The first joint-stock company, the MuscovyCompany, was set up in London in 1553. As tradeexpanded, merchant banks emerged and acted asaccepting houses that charged interest and commissionon the bills of exchange that were used to finance trade.A virtuous cycle emerged in which expanded tradeprovided capital for financial institutions, who in turnprovided capital for expanded trade and new firms. Asjoint stock companies became more important, thebuying and selling of shares became more formalised,and exchanges were set up. The New York StockExchange was started under a buttonwood tree in 1792.The institution that would develop into the LondonStock Exchange was started in 1760, renamed in 1773and officially regulated in 1809.

Distinct institutions emerged within this heavilyregulated financial system. While there is huge diver-sity within the sector, the three main forms of financialinstitutions are banks, exchanges and investment insti-tutions. The institutional differences are determined byfunctional differences in how firms move moneybetween savers and borrowers. Each method used, inturn, defines the technological trajectories (Dosi, 1982)that the firms follow, and influences the kind oftechnologies and infrastructure they use (Buzzacchi etal., 1995; Penning & Harianton, 1992).

Banks are traditionally divided into two kinds,depending on how they move surplus funds frominvestors to borrowers (Berger et al., 1995). Commer-cial or retail banks rely on deposits drawn fromindividual savers that they re-lend at a profit; the saversare typically paid a nominal amount of interest.Investment banks, however, make their money throughfees charged from arranging complex financial deals(Eccles & Crance, 1988). Unlike commercial banksthat load the assets they hold, investment banks helpallocate surplus savings by underwriting securities thatare sold to other investors. Securities have the advan-tage of being liquid, and consequently allowinginvestors to make rapid changes to their portfolios.

Exchanges are institutionalised markets for thetrading of financial contracts. The two main types of

contracts sold on exchanges are company shares(which are sold in equity markets), and governmentand company debt (which are sold in bond markets).5

Markets have an advantage over institutional invest-ment mechanisms in that they publicise informationabout the price of resources, allowing financial actorsto improve their resource allocation. Exchanges arenormally closed institutions with their own rules andregulations, which allow well-established financialprocesses to be developed and used in a more‘trustworthy’ environment, improving the liquidity oftrading. Since the value of assets generally increaseswith their liquidity (though there are exceptions), theability to easily trade a contract has important eco-nomic implications.

Investment institutions such as pension funds,mutual funds (called unit trusts in the U.K.) and life-insurance companies are a fairly recent development.Up until the late 19th century, private individuals werethe main investors, but after that date, investment trustsbecame increasingly important, followed by unit trustsin the 1930s (Golding, 2000). In the 1960s, pensionfunds took off, and the life-insurance industry becamea major force in financial investment. Investmentinstitutions now hold about two-fifths of U.S. house-hold financial assets, and the largest five fund managersplace assets larger than the combined GDPs of Franceand the U.K. They bundle together the savings andinsurance contributions of individuals to invest in arange of assets on a long-term basis. Strictly speaking,investment institutions rely on fund managers for theirinvestment management, but in practice most invest-ment institutions are directly involved in investing inquoted companies. During the 1980s and 1990s,investment institutions grew rapidly, until they nowcontrol approximately US$26 trillion of funds, US$13trillion of which are in the U.S.—approximately threetimes the GDP. Each type of investment institution hasdifferent investment preferences. Pension funds havepredictable long-term liabilities, while insurance fundsrequire more liquid assets in case they should have topay out for a disaster.

Because financial services are so important to thewider global economy, they have traditionally beenheavily regulated (Berger et al., 1995). Understandingregulation and regulatory loopholes is essential forunderstanding the development of the financial serv-ices industry (Calomiris & White, 1994; Hall, 1990;Merton, 1995b). In the USA regulations limited theability of financial institutions to compete in a range ofproduct and geographic markets. In Europe and Japanthe operations of financial institutions are similarlyregulated, which in turn influences what they do, andthe structural possibilities of the technologies they use3 Philip II had been defeated in 1575 not because of military

superiority but because the cost of his army had bankruptedhim, and he could no longer pay his troops.4 London did not emerge as a financial centre until theNapoleonic wars eliminated Amsterdam.

5 Equities, unlike bonds, are ‘real assets’ that can protect theowner from inflation.

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(Channon, 1998). Because financial infrastructuretechnologies influence firm behaviour, their develop-ment will almost inevitably involve national andpossibly international regulators. These regulators mayrequire changes to legal frameworks, which compli-cates and lengthens the design and developmentprocesses. While this is true of other sectors, it does notnormal occur to the same extent.

The shift towards more market-based regulation inthe late 20th century is commonly referred to as ‘de-regulation’, but is perhaps better thought of as aprocess of re-regulation. Specific restrictions have beenremoved, but government and international regulatorsstill play a vital role in the functioning of financialmarkets.6 The ongoing changes in national and regionalregulations have led to an increased internationalisa-tion of markets and capital flows. This has, in turn,produced a corresponding increase in the geographicalscope of infrastructure and allowed firms to exploitnew economies of scale, generating market growth andincreased concentration.

The Importance of Liquidity

The increasing concentration of the financial servicesindustry over the 1980s and 1990s has seen theemergence of investment institutions with extremelylarge capital funds. The size of these funds hasintensified concern about liquidity and the correspond-ing ability to cheaply adjust portfolios when marketsmove. For example, a fund of US$500 million will notbe able to make significant changes to its performancewithout trading units of about US$10 million (Golding,2000, p. 67). Buying or selling such an amount ofshares in an ‘illiquid’ firm that trades only US$250,000of shares a day is going to be both time-consuming andexpensive, and is likely to alert other players in themarket, pushing the price up or down.

As a consequence, institutional investors have apreference for firms with large market capitalisationand very liquid shares. The size of modern financialinstitutions means that they have a major influence onmarket behaviour (and that the economist’s notion oflarge numbers of small independent investors isbecoming unrealistic).7 Since the 1980s, there has alsobeen a trend towards the securitisation of a range ofassets. This means that financial assets such asmortgages and credit card liabilities have been pooledand resold as contracts in markets where they can betraded. These bonds, backed by securitised assets, tend

to be substantially more liquid:8 not only are therefewer bonds than shares to choose from, but they arealso more frequently traded. The New York StockExchange, for example, trades about US$350 billion ofbonds a day, compared to only US$28 billion a day inequities.

Given that the liquidity of assets is determined inpart by the number of potential buyers, infrastructuretechnologies that can bring a larger, more diverse set ofbuyers into contact with sellers will increase liquidityand the value of assets. Financial institutions rely oninfrastructure technologies to do this. These technolo-gies need not, however, be owned by the same peoplewho operate the markets, and the divergent techno-logical trajectories of stock exchanges andtelecommunications networks has produced a shifttowards the outsourcing of telecommunications.

Financial Institutions as Socio-Technical SystemsFinancial institutions provide, monitor, and maintainthe processes whereby funds are pooled, matched toborrower’s requirements, and then allocated. Financialinstitutions take financial contracts (funds, bonds, etc.)as their inputs, and then process them, before reengi-neering them into new forms of contract that are thensold to customers. In doing so, they use sophisticatedprocesses comprising people, knowledge and technol-ogy, to match the financial requirements of borrowersto those of savers. As such, they can be conceptualisedas socio-technical systems (Hughes, 1983, 1987),where technology is used to improve the processesinvolved in matching financial contracts to customersby replacing person-based (and often market-based)mechanisms with an organised technology-basedmechanism. This allows financial institutions to pro-vide better services, develop new products, and exploitimproved economies of scale, scope and speed (Barras,1986, 1990; Ingham & Thompson, 1993; McMahon,1996; Nightingale & Poll, 2000b).

The relationship between innovation in infrastruc-ture technologies and performance improvement isfairly well understood in large manufacturing firmsfollowing the work of Alfred Chandler (1990). Heshowed the way in which firms invest in high fixed-costinfrastructures that improve the capacity and speed ofproduction processes. The effective use of technologycould then generate the fast, high-volume flows thatwould turn low-cost inputs into high-value outputs. Inthis way, the high costs of the infrastructure are thenspread over a large volume of output to keep unit costslow.

If the volume of production is too low, then highfixed costs cannot be adequately spread and unit costs

6 The links between internationalisation and regional regula-tion have led Howells (1996) to question the usefulness of theconcept of globalisation.7 It is now not uncommon for large British companies to have80–90% institutional ownership (Golding, 2000, p. 31).

8 Bonds backed by regular mortgage payments can be soldinstantaneously and very cheaply, while repossessingand selling several thousand homes is extremely costly anddifficult.

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will rise (Chandler, 1990, p. 24). As a result, manu-facturing firms organise and co-ordinate the resourcesrequired to fully utilise the capacity of productionprocesses and increase the average speed of ‘flows’.9

This is done in two ways. Firstly, firms developedsophisticated managerial techniques for controllingprocesses and, secondly, they exploited external andinternal infrastructure technologies such as the tele-graph and railway systems to ensure that productionwas uninterrupted.

Within the financial services sector things areslightly different. Profitability is linked to the efficientcontextualisation and processing of information ratherthan the utilisation of capital machinery (Nightingale &Poll, 2000a). This means that profits are far lessconstrained by infrastructure technology than they arein manufacturing. For example, the profitability con-tribution of a worker on a production line or in a steelmill is largely determined by the technology itself, anda worker would be hard pressed to increase profitssubstantially. By contrast, a trader in a bank who wassmarter, faster or had superior analysis than others inthe market could very easily make significantly moreprofits with very few restrictions from technology. Theamount of profit made on a trade of US$100 millionwill be more than on a trade of US$1 million, eventhough the same telecommunications system might beused. This is called leverage.

The flip side of high leverage is that while profits arealmost unlimited, the same is true of losses, creating anincentive to understand and manage the extent andlikelihood of these losses occurring, namely risk. Asinfrastructure technology has allowed larger and morecomplex contracts to be produced and traded, under-standing risk has increased in importance- andrisk-management technologies are key aspects ofmodern financial service infrastructure.

These differences underpin Barras’ (1986, 1990)concept of the reverse product life cycle, wherebyinnovation in financial services is processrather thanproduct-driven (1986, 1990). Following Vernon (1966),Utterback & Abernathy (1975) argued that innovationin manufacturing follows a cycle that initially concen-trates on product innovation until an established designis formulated, after which time competition is based onprocess innovation. Barras (1986, 1990), by contrast,argues that financial services follow a pattern wherebyinnovations in processes (and infrastructure technolo-gies) allow new products to be introduced (1986). BothUtterback & Abernathy (1975) and Barras’ (1986,1990) ideas have been heavily criticised (Pavitt &Rothwell, 1987; Uchupalanan, 2000). In financialservices, where the process is often the product, thesharp analytical distinction between product and proc-

ess innovation is questionable (cf. Easingwood &Storey, 1996).

Another way to look at innovation in financialservices is to look at how, on the one hand, technologyis used to increase the scale and liquidity of financialtransactions by increasing ‘reach’ and bringing morebuyers and sellers into contact with one another and, onthe other hand, at how technology can be used to bettermatch savers and lenders. In this way the manufactur-ing categories of process and product innovation aresubsumed into the overlapping categories of serviceprovision and control. This has the advantage ofallowing us to see how innovation in financial serviceinfrastructure co-evolves with innovation in infra-structure technologies outside the financial system. Forexample, the infrastructure of the railway network co-evolved with the telegraph system and the developmentof new financial products and services, which allowedthe transportation of goods to be profitably co-ordinated.

It also allows one to conceptualise the direction ofinnovative activity. Infrastructure technologies thatallow increases in the scale and liquidity of financialservices tend to do so by expanding the range of saversand borrowers with whom firms interact because thescale and liquidity of financial transactions are closelyrelated (Peffers & Tuunainen, 2001). For example,prior to the introduction of the telegraph, the New YorkStock Exchange only managed to sell 31 shares over asingle day in March 1830 (DuBoff, 1983, p. 261).However, infrastructure technologies that improve theinternal allocation and control of service provision tendto focus on improving the accuracy, scope, speed andreliability of control processes (Nightingale & Poll,2000b).

Technologies of communication and transportationare therefore particularly important for financial serv-ices, as delays in communication lead to deviations inmarket prices and opportunities for arbitrage. Conse-quently, the evolution of the financial services sectorhas been influenced by developments in informationand transportation infrastructure technologies. Forexample, when communication between London andAmsterdam was dependent on sailing boats, it tookthree days for information to travel between themarkets. This inefficiency in the communication ofmarket information created opportunities for arbitrage.Similarly, when communication within Britain wasundeveloped and irregular local exchanges flourished,but with the advent of regular mail coaches, leavingfrom Lombard Street in the City of London (starting in1784), there was a pull towards the larger market inLondon (Michie, 1997, p. 306).

The development of the telegraph was a significantadvance in infrastructure technology. Starting in theUnited States in 1844, by 1860 there were 5 millionmessages being transmitted annually along 56,000miles of wire and 32,000 miles of telegraph poles

9 One way to maintain capacity utilisation (and thereforelower unit costs) is to exploit unused production capacity innew product lines, thereby generating economies of scope.


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(DuBoff, 1983, p. 255). In 1851 a link was introducedbetween London and Paris which transcended theprevious 12-hour communication times and allowedreal-time price communication. In 1866 the first linkbetween London and New York was set up, by 1872 thetelegraph had linked London and Melbourne, and by1898 there were 15 undersea cables (only nine wereworking) under the Atlantic (Michie, 1997, p. 310). By1890, information could travel the 400 miles fromGlasgow to London in 2.5 minutes (ibid.).

The telegraph had important implications for com-modity exchanges as, together with improvedinfrastructure of railroads and storage, it allowedcontracts to be linked to the point of production. As aconsequence ‘to arrive’ contracts started to replaceadvanced payment based on ‘certified’ samples (Du-Boff, 1983). This produced greater price stability, aslarge amounts of commodities were not being dumpedin the illiquid markets of commercial centres. With theadvent of the telegraph a far wider range of buyers andsuppliers could be searched enabling exchanges andmarket makers to better match supply and demand.Infrastructure technologies such as the telegraph there-fore increased liquidity and allowed the centralisationof exchanges (DuBoff, 1983).

The telegraph technology was very limited until the1860s, with transmission rates of about 15 words perminute. With the invention of the stock telegraph in1867, this went up to 500 words per minute, whichagain improved market performance (DuBoff, 1983,p. 263). Middlemen could be cut out of transactions,buyers and sellers did not need to travel, time lags werereduced, and there was a substantial reduction in therisks involved. As DuBoff notes:

The expected savings from a given market searchwill be higher the greater the dispersion of prices, thegreater the number of production stages, and thegreater the expenditure on the resources or service.For example, the only way to know all the priceswhich various buyers and sellers are quoting at agiven moment would be to bring about a completecentralisation of the market, only then will costs ofcanvassing, or search, be at a minimum. Conversely,with infinite decentralisation these costs will reach amaximum. To lower them it pays to centralise—toreduce spatial dispersion and the number of inde-pendent decision makers (1983, p. 266).

The temporal and geographic reach of buyers andsellers was further improved by the introduction of thetelephone. The geographic scope was increased againwhen the first transatlantic telephone cable was laid in1956. The previous radio-based telephone infrastruc-ture could only deal with 20 people, but by 1994submarine cables could handle some 600,000 calls at atime (Michie, 1997, p. 318).

While much of the early innovation in infrastructureinvolved financial services firms ‘piggybacking’ on

established technologies like the telegraph, by the late1980s firms were investing heavily in their owncommunications networks that could link localbranches together. In doing so they relied heavily onexternal suppliers such as telecommunications com-panies and specialised financial information supplierslike Reuters, Dow Jones/Telerate, Knight Ridder andBloomberg. By linking more institutions and peopletogether with technical infrastructure, financial institu-tions have been able to save time in allocating financialcontracts and increase the scope and number of buyersand sellers. This has made markets more liquid andallowed customers to reduce their inventories and thefinancial resources needed to maintain them. In doingso, the process of buying and selling has becomedisintermediated, with middlemen who traditionallymatched buyers and sellers being replaced. Thisprocess of centralisation allows larger institutions toexercise improved control and secrecy, which in turnallows them to exploit new economies of scale (cf.Berger et al., 1995; DuBoff, 1983).

Internal InfrastructureOnce telecommunications systems had increased thereach of markets, and railroads had made improvednational transportation of goods possible, marketliquidity and the scale of transactions improved,creating new innovation challenges. Firms makinglarge numbers of transactions needed to work out howbest to allocate resources between different customers.Until recently, this had been done using unsophisti-cated technologies, largely subjective assessments ofrisk, and a ‘my word is my bond’ trust-based attitudetowards risk control.

Since the 1980s the development of sophisticatedtheoretical tools in financial engineering has allowedproducts to be better priced and controlled (Marshall &Bansal, 1992). The initial developments came in 1952when Harry Markovitz developed the fundamentals ofportfolio theory (1952). William Sharpe helpeddevelop the Capital Asset Pricing Model (CAPM) inthe 1960s, and in the 1970s, Merton, Scholes & Blackdeveloped their option-pricing model (Black &Scholes, 1973). These theoretical developments haveco-evolved with developments in internal IT infra-structure technologies that can integrate data andperform complex pricing and risk calculations (Bansalet al., 1993; Nightingale & Poll, 2000a).

The ability to value options has transformed financebecause it has allowed risk to be approximatedmathematically (Berstein, 1996). As a result, internalrisk-management processes that previously relied onindividual bankers’ subjective assumptions, can now bemodelled. This allows risk management to be moreaccurately related to the real risk exposures thatfinancial institutions face. The development of thesenew tools and new internal software-intensive, ITinfrastructure technologies increased the ease and

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accuracy of analysis and product pricing, and hasallowed a range of new products and services to bedeveloped (Nightingale & Poll, 2000a).

The shift towards a more theoretical basis forfinancial engineering was dependent on a transforma-tion in the relationship between front and back officefunctions. Previously, the back office function infinancial institutions had been automated to reducecosts. Risk exposures were typically computed on largemainframe technologies that would provide analysis ofpositions at the end of the day. With the development ofsophisticated computer workstations and analyticalsoftware packages on traders’ desks, many back-officefunctions were brought to the front office where theycould be carried out closer to the customer. Riskanalysis is typically now performed at various organi-sational levels within financial institutions, and traders,for example, will have a degree of sophisticatedanalysis available at their desks. This analysis can beperformed in close to real time, allowing improvedcontrol over positions and exposures, but also imposinga large cost on organisations (Brady & Target, 1996).

This shift in the architecture of control has beendependent on the development of increasingly power-ful software-intensive technologies and systems thatare able to perform complex calculations quickly onlarge data sets. This has required a number of the largerfinancial institutions to develop technological capabil-ities in information technology, and these capabilitiesare beginning to be detected within the patent statistics(Pavitt, 1996). Financial service firms are developingtheir own capabilities in IT and in the mathematicalalgorithms needed to derive solutions to their risk andpricing calculations.

Nightingale & Poll (2000a) described how aninvestment bank developed the internal infrastructuretechnology needed to produce an increasingly sophisti-cated range of financial products. They showed that theability to control the pricing and risk of financialcontracts is dependent on the scope of the data that areaccessed, the accuracy of the models, the speed ofcalculation and the reliability of the system.

The scope of control is important because calculatedrisk exposures will come closer to real risk exposures ifthe infrastructure allows the scope of risk analysis toextend and include more trades that the bank is partyto. For example, if the bank’s offices in London, NewYork and Tokyo are all exposed to the same position,each office may be within its local risk limits but thebank as a whole may be over-exposed. This exposurecan be reduced if a wider scope of trades is included inthe analysis.

Similarly, the ability to calculate exposures andprices is dependent on the accuracy of the models usedand the power of the computer systems. If the modelsare inaccurate, or it takes a long time to calculate aposition, the quality of the approximation will bereduced as inaccuracies build up or markets move.These two factors, and the drive towards more scope,lead to increases in the size and power of these ITsystems. As Table 1 shows, increases in size andcomplexity lead to qualitative changes in the technol-ogy. Problems that might occur very rarely onindividual workstations, for example, may occur everyday in IT systems comprising several thousand glob-ally linked servers, making maintenance a verycomplex and increasingly centralised process. Conse-quently, modern financial institutions are among themost innovative and demanding customers for thetelecommunications and IT sectors (Barras, 1986,1990).

Financial InfrastructureFinancial institutions depend on innovation in infra-structure technologies in two main ways. Firstly, theydepend on external infrastructure technologies tounderpin their trading and bring buyers and sellerstogether. Typically these infrastructure technologies,like the old railroads and telegraphs or modern IT andtelecommunications networks, link wide geographicareas and allow financial firms to co-ordinate anincreasingly diverse selection of customers. Thisincreases the liquidity of markets, reduces middlemenand allows centralised firms to exploit new economies

Table 1. Qualitative changes in risk management infrastructure.

Feature Craft Mass Standardisation Complex

Number of servers 1–100 100–1000 1000 +Maintenance Craft-based Standardised Very complexArchitecture Decentralised Centralised CentralisedKey factor Automation Change function Risk & reliabilityProblems Few Predictable One in a millionImportance Limited Business cost Business criticalRisk analysis Limited End of day Quicker and wider

Adapted from Nightingale & Poll (2000a).


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of scale and scope. Often these technologies areproduced and maintained by external suppliers, butmany firms possess sophisticated technological capa-bilities and may operate their own Virtual PrivateNetworks with telecommunications suppliers.

Secondly, once financial institutions have reached acertain scale they can exploit internal infrastructuretechnologies to calculate and control how their internalresources should be allocated. This use of internalinfrastructure technology is dependent on firms build-ing their internal technological capabilities to developand use IT. Even when firms outsource their IT andtelecommunications, they must still have sufficienttechnical capabilities to be intelligent customers andoperate and use the infrastructure reliably.

This first half of this chapter has explored whyinfrastructure is important to the financial servicesindustry, showing the importance of regulations andregulatory compliance to their development, and howthe technologies comprise a range of complex, soft-ware-intensive capital goods that form part of, and arelinked to, wider Large Technical Systems, such asglobal telecommunications networks. Despite theireconomic importance, there is little academic literatureon the specific problems of financial infrastructureinnovation, compared to other industries such aspharmaceuticals, for example. Consequently, the nextsection looks at the wider innovation literature, andexamines what it can illuminate about the features ofinfrastructure outlined here. It will explore howinfrastructure is similar to, and differs from, the massproduction consumer goods traditionally studied in theinnovation literature, and how the particularities offinancial services influence the relevance of this widerresearch.

Innovation in Infrastructure

The Nature of InnovationInnovation is generally characterised by high levels ofuncertainty and sector specificity (Dosi, 1988; Free-man, 1982; Pavitt, 1984, 1989). Technological noveltyand complexity contribute towards uncertainty, andmean that attempts to develop and use innovationsoften run into difficulties. These uncertainties can,however, be managed (Tidd et al., 1997) and a series ofempirical studies have attempted to establish thefactors that contribute to success. Project ‘SAPPHO’was one of the first attempts to systematically identifywhat differentiates pairs of successful and unsuccessfulinnovations (Rothwell et al., 1974; SPRU, 1972).Although the original study was based on processinnovations in the chemical industry and productinnovations in the scientific instruments sector, threemajor findings came through clearly and have beensupported by subsequent studies (Bacon et al., 1994;Clark et al., 1989; Cooper, 1983; Cooper & deBrentiani, 1991; Cooper & Kleinschmidt, 1990, 1993;

Prencipe, 2002; Shenhar et al., 2002; Tidd et al., 1997).These are: the importance of understanding user needs,the importance of good internal knowledge co-ordina-tion, and the importance of having strong internaltechnical capabilities in order to access and incorporateexternal sources of knowledge.

While these findings remain important for under-standing innovation in financial infrastructure, caremust be taken to recognise the sector specificity oftechnical change (Freeman & Soete, 1998). Pavitt(1984) has produced a taxonomy of sectoral patterns ofinnovation that divides innovations into three typeswith different characteristics. These are: supplier-dominated, production-intensive and science-based.More recently the taxonomy was updated to includeinformation-intensive software and services (Pavitt,1990, cf. 1996). While the original paper is highlycited, many people miss that it added an additional“fourth category . . . to cover purchases by govern-ments and utilities of expensive capital goods, relatedto defence, energy, communications and transport”(1984, p. 276).

The notion that innovation in complex capital goodsis different from innovation in consumer goods wasdeveloped in the military-technology literature (Walkeret al., 1988). Walker et al. (1988) formulated ahierarchy of military technologies that extends fromvery-low-cost materials and components (such as nutsand bolts), to high-cost components (such as jet fighterengines), and on to entire military systems costingbillions of dollars (such as missile defence systems).They noted that:

as the hierarchical chain is climbed products becomemore complex, few in number, large in scale, andsystemic in character. In parallel, design and produc-tion techniques tend to move from those associatedwith mass-production through series- and batch-production to unit production. Towards the top of thehierarchy, production involves the integration ofdisparate technologies, usually entailing large-scaleproject management and extensive national andinternational co-operation between enterprises.Thus, the pyramid is also one of increasing organisa-tional and managerial complexity (Walker et al.,1998, pp. 19–20).

During the 1980s and 1990s a growing body ofresearch analysed these highly engineered, bespokecapital goods. The research found that they tend to beproduced by temporary networks of systems integra-tion firms and their suppliers and regulators (Burton,1992; Hobday, 1998; Hughes, 1987, 1983; Miller et al.,1995; Prencipe et al., 2002; Shenhar, 1998; Walker etal., 1988). Research on the management of theirdevelopment has stressed the importance of goodproject management, good risk management, andeffective control over the various suppliers involved in

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project development (Dvir et al., 1998; Hobday, 1998;Lindkvist et al., 1998; Might & Fischer, 1985; Miller etal., 1995; Morris, 1990, 1994; Pinto et al., 1993, 1998;Shenhar, 1998; Shenhar et al., 2002; Shenhar & Dvir,1996; Tatikonda & Rosenthal, 2000; Williams, 1995;Williams et al., 1995 for a review of recent research).Insightful work has also revealed the particular prob-lems associated with large-scale software development(Boehm, 1991; Brady 1997; Brooks, 1995; Gibbs,1994; Parnas, 1985; Walz et al., 1993). In an importantstudy of 110 Israeli defence projects Dvir et al. (1998)found that the balance of success factors was far fromuniversal, with software projects being very differentfrom hardware projects, and risk management andbudget control less important for small-scale projectsbut vital for large ones.

In two seminal papers Hobday explored innovationin complex products and systems (CoPS) and notedthat they are characterised by their business-to-business, capital good nature, their batch productionprocesses, high-cost, inherent uncertainty, and thedegree of embedded software. They tend to haveproduction process that are based on temporary,negotiated and bureaucratically driven project-basedfirms (PBFs) and organisations (involving webs ofusers, producers and regulators) (cf. Gann & Salter,1998; Lemley, 1992; Marquis & Straight, 1965; Might

& Fischer, 1985). This is particularly true of financialinfrastructure, like the CREST or TAURUS systems,which are used and developed by many firms. Furtherresearch has highlighted the importance of early userinvolvement and the heavily regulated, bureaucraticallyadministered nature of the market and developmentprocesses (Burton, 1993; Morgan et al., 1995; Morris,1990; Sapolski, 1972; Walker et al., 1988). Thesefeatures can be contrasted with mass-production goodsthat are low-cost, well understood, contain littleembedded software and are sold in largely unregulatedmarkets. This contrast can be seen in Table 2 (derivedfrom Hobday, 1998, p. 699).

The Process of Infrastructure DevelopmentThe combination of high technological complexity anduncertainty, complicated user needs, long developmenttimes, high costs and high risk makes infrastructuredevelopment extremely difficult. There tends to besubstantially more stress on risk management, projectco-ordination, and uncertainty management than intraditional innovation processes. Despite these attemptsat dealing with uncertainty, infrastructure projectssuffer from a range of innovation problems andfrequent project failures (Flowers, 1996; Hobday,1998; Morris, 1990; Nightingale, 2000; Sauer &Waller, 1993; Tatikonda & Rosenthal, 2000).

Table 2. Contrast between innovation in complex capital goods & commodity products.

Feature Complex Capital Goods Commodity Products

Product Characteristics Very high cost Low costMulti-functional upstream capital goods Single function downstream consumer

goodsComplex components and interfaces Simple components and interfacesMany bespoke components Small number of standardised componentsHierarchical and systemic Simple architecturesHigh degree of embedded software Little software

Production Characteristics One-off projects or small batch production High volumeHighly uncertain Well understoodSystems Integration Efficient productionScale-intensive mass production notrelevant

Incremental process cost improvements

Innovation Process User-producer-driven Supplier-drivenHighly flexible craft-based FormalisedInnovation and diffusion collapsed Innovation and diffusion separateInnovation path agreed ex ante amongsuppliers, customers and regulators

Innovation path mediated by marketselection

Industrial Co-ordination Elaborate temporary network Structured around large firms and theirsupply chains

Project-based, multi-firm alliances Mass production by single firm

Market Characteristics Duopolisitic structure or internal provision Many competing buyers and sellersFew large transactions Large number of transactionsBusiness to business Business to consumerAdministered markets Regular market mechanismHeavily regulated and often politicised Limited regulation


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One way of dealing with the uncertainty and highrisk is to reverse the traditional method of ex postselection of consumer products in markets. Consumergoods innovation typically starts with R&D and movesinto development, then production, then marketing andfinally product launch and the selection of products bymarket mechanisms (see, for example, Utterback &Abernathy, 1975, cf. Barras, 1990).

By contrast, in complex infrastructure technologiesthe high risks and costs involved mean that customers,suppliers, regulators and government bodies negotiatecontracts, product designs and production methodsbefore development is begun (Hobday, 1998; Peck &Scherer, 1962; Walker et al., 1988; Woodward, 1958).This is intended to reduce risk and ensure that the endproduct matches the various stakeholders’ require-ments. As a consequence, infrastructure innovationprocesses will typically start with marketing and sales,and only after an outline design and production processis specified will the contracts be signed and develop-ment started.

The importance of understanding user needsthroughout the development process is well recognisedwithin the innovation literature. The SAPPHO projectfound it to be the major determinant of success(Rothwell et al., 1974; SPRU, 1972). This has beensupported by subsequent research (Cooper, 1986;Grieve & Ball, 1992; Keil & Carmel, 1995; Lundvall,1988; Mansfield, 1977; Rothwell, 1976; Teubal et al.,1977). In particular, von Hippel (1976) has shown theimportance of users as sources of innovation, and thecase studies of the SAPPHO project illustrated theimportance of users making adjustments to technolo-gies after they had been acquired (Rothwell, 1977).Bacon et al. (1994) found that using developmentengineers rather than marketing staff to liase withcustomers, and using prototypes to aid customerfeedback, produced superior results.

This initial stage of infrastructure developmentinvolves understanding user requirements, dealing withtechnical uncertainties, finding solutions that areacceptable to the various customers and users and thenobtaining commitment from a whole network ofstakeholders to an uncertain and potentially very riskyventure. Simply getting the various users and custom-ers, as well as national and international regulators, todefine the infrastructure’s function in any detail can beextremely difficult. Infrastructure technologies’ initialcost estimates and overarching function are generallyvague enough to interest a range of parties, but onceone moves to the more specific architectural layout ofthe technology, the engineering trade-offs becomeincreasingly politicised.

Choices about technologies become politicisedbecause the implementation of new infrastructuresoften causes disruptions to established practices withininstitutions. The changes potentially impact a range ofactors who may be resistant to the proposed project if

they feel that their interests will suffer. The ‘Big Bang’in the City of London, which moved the City towardselectronic trading, for example, required a large pushfrom the U.K. government and met resistance fromestablished groups within the City. The introduction ofany major technology will have positive and negativeeffects for different groups, and those who arenegatively affected may have very rational reasons tobe against the project. The very late shift towardselectronic trading in the New York Stock Exchange, forexample, is indicative of the power of entrenchedinterests. Even with a largely positive group of users, itis not necessarily the case that they will have the samerequirements or demands, and substantial negotiationis needed to define acceptable solutions (cf. Moynihan,2002). These negotiations will necessarily involvetrade-offs and the final proposals may not necessarilymatch anyone’s specific requirements, a feature thatapplies as much within organisations as it doesbetween them (Barki & Hartwick, 2001).

The problems involved in specifying the functions ofa major infrastructure technology are complicatedbecause many firms and institutions lack the technol-ogy capabilities to be ‘intelligent customers’.Infrastructure technologies, after all, are not producedby banks everyday. As a consequence, financialinstitutions are susceptible to being misled by consult-ants and contractors into producing overly complextechnologies that do not match their needs (Collins,1997). Despite the substantial in-house capabilities thatfinancial institutions have for developing and usinginformation technology, many still lack the expertiseneeded to fully comprehend the complexities andpotential difficulties they face in developing andimplementing new infrastructure. Software firms aregenerally reluctant to be open about these potentialproblems, as they have an incentive to downplay themin order to secure contracts (Flowers, 1996). Evenusing external consultants to assess the bids may notovercome this problem, as their independence from thesoftware industry is often questionable (Collins, 1997;Flowers, 1996).

The costs, complexities and risks involved indeveloping infrastructure mean that development pro-jects require commitment from a range of actors. Thistypically comes in the form of written legally enforce-able contracts, organisational commitment, andpolitical endorsement. Unfortunately, these can easilylock counter-parties into a particular direction oftechnical change that may involve using inferiortechnologies and architectures (Collingridge, 1983;Walker, 2000). Similar problems emerge within organi-sations and make changes to heavily committeddecisions difficult to undertake (Flowers, 1996). Theprocess of securing commitment to a technology canmake ‘pulling the plug’ on failing projects verydifficult. This is a common problem in IT-intensiveinfrastructure. Keil et al. (2000a) found that between

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30% and 40% of all the information systems in theirsample exhibited some degree of “escalation ofcommitment to failing courses of action”, where theprojects in question spiralled out of control (cf. Keil,1995; Keil et al., 2000b; Keil & Montealegre, 2000;Smith et al., 2001). As with infrastructure projects indeveloping countries, larger projects require greatercommitment, which makes changes in the light ofpotential failure more difficult.

Project Uncertainty and Process FlexibilityOnce the initial specifications are defined and thecustomers are committed, the process moves to thenext stage where engineers and technologists propose,test and modify solutions. As Vincenti (1990, p. 9)notes, the design process:

for devices that constitute complex systems is multilevel and hierarchical . . . The levels run more or lessas follows from the top on down:

(1) Project Definition—translation of some usuallyill defined military or commercial requirementinto a concrete technical problem for level 2;

(2) Overall Design—layout of arrangements andproperties . . . to meet the project definition;

(3) Major Component Design—division of project. . . ;

(4) Subdivision of areas of component design fromlevel 3 according to engineering disciplinerequired . . . ;

(5) Further Division of categories in level 4 intohighly specific problems.

Thus, for complex infrastructure technologies thedesign process will involve specifying components andarchitecture in increasing detail. This process iscomplicated for infrastructure technologies becausemany sub-components are systemically related. AsNelson points out:

A particular problem in R&D on multi-componentsystems arises if the appropriate design of onecomponent is sensitive to the other components.Such interdependencies mitigate against trying toredesign a number of components at once, unlessthere is strong knowledge that enables viable designfor each of these to be well predicted ex ante or thatthere exists reliable tests of cheap models of the newsystem (1982, p. 463).

This systemic complexity means that the effects ofincorrect design are magnified as design modificationsspread to other systemically related subsystems.

The inherent uncertainties involved mean that initialsolutions will rarely work correctly (Petroski, 1986).Instead, an iterative process of trial and error design isused to bring proposed solutions closer to the desiredfunction. Each design iteration will cause the innova-

tion process to feed back to earlier stages adding to itscost and schedule (Nightingale, 2000; Williams et al.,1995). With simple technologies this process is rarelyproblematic, but with complex infrastructure technolo-gies there are a larger number of possible feedbackloops and potential failures within and betweencomponents.

If the process of updating design specifications takestime, the design changes are extensive, or the compo-nents are related to a large number of ‘sensitive’components, the amount of redesign work can be verylarge. There is consequently a danger of ‘redesignchain reactions’ that spread through the differentsystems with disastrous effects (Nightingale, 2000). Asthe next section will show, this is a particular problemwith software-intensive systems which tend not toscale in a linear way, so that small, local designmodifications have the potential to grow into funda-mental design changes at the project level.

The complexity of infrastructure projects means thateven without these redesign loops, unforeseeableemergent problems can develop during the productionprocess. As a consequence, production and develop-ment overlap to a far greater extent than in traditionalinnovation processes, where development problems aretypically ironed out before production begins. Theoverlap between development and production is alsorequired because the long timescales involved indeveloping infrastructure technologies (often years)mean that some component technologies can undergoradical technical changes during the lifetime of theproject. This is especially true of rapidly changingtechnologies such as IT. Consequently, the project’sdesigns and processes need to be flexible and able toincorporate new developments as the project proceeds(Hobday, 2000).

The fluidity of design specifications and the high riskand cost of infrastructure make good internal, cross-functional knowledge co-ordination important forproject success. Research on other sectors has high-lighted the performance differences between firms thatintegrate functional disciplines and those that have asequential innovation process (Bowen et al., 1995;Clark & Fujimoto, 1991; Leonard-Barton, 1995;Iansiti, 1995; Rothwell, 1992, 1993; Womack et al.,1991). The importance of organisational structure forknowledge integration has been highlighted by Gal-braith (1973), Wheelwright & Clark (1993) and Clark& Fujimoto (1991), building on the original insights ofBurns & Stalker (1961). The importance of differentorganisational forms in the development of large,complex technologies has been well recorded in theliterature (Burns & Stalker, 1961; Larson & Gobeli,1987, 1989; Miles & Snow, 1986). Larson and Gobeli,for example, show how the success of projects isdependent on appropriate organisational structures andrelate that to the complexity, technological novelty,managerial capabilities and functional definition of


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projects (1989). Rothwell (1993, 1992) has shown howinformation technology can be used to improve theintegration of knowledge (cf. Bacon et al., 1994).

The Importance of Embedded SoftwareThe problems associated with financial infrastructureinnovation are exacerbated by the increasing impor-tance of embedded software. This software is used tocontrol how financial information is routed and proc-essed and can radically improve infrastructureperformance (Nightingale & Poll, 2000a). However,the incorporation of software adds to the complexityand uncertainty of development (Nightingale, 2000,p. 5) and turns what were previously straightforwardengineering tasks into high-risk development projects(Brooks, 1986; Hobday, 1998).

Software development is problematic because of itsvulnerability to errors and fragility (Brooks, 1995; cf.Boehm, 1981, 1991; Mills, 1971; Parnas, 1985;Ropponen & Lyytinen, 2000; Royce, 1970; Willcocks& Grithiths, 1994). Software is more vulnerable todesign errors than other technologies because theabstract construction of interlocking concepts, datasets, relationships, algorithms and function invocationsthat make up a piece of software must all workperfectly if the software is to function properly.Unfortunately, the potential problems inherent in astring of code are not easy to find, making debuggingand testing embedded software extremely difficult andtime-consuming, and as a result, systems are oftenlaunched without being properly tested (Flowers,1996).

The fragility of embedded software refers to thedifficulties involved in modifying software as com-pared to many other technologies. As software isvulnerable to ‘bugs’, must work almost perfectly, andtypically scales in non-linear ways, minor changes incode can necessitate extensive redesigns. These, inturn, can produce feedback loops within the softwaredevelopment process that can snowball into resource-intensive redesign processes and lead to increasinglyfragile and low-quality products.

Software also adds to the development problemsassociated with the more tangible parts of the infra-structure because it may increase the degree ofsystemic interactions between physical sub-systems.While this can improve system performance, it can alsoreduce the ability to break complex innovation prob-lems into sub-problems and modularise development.

The problems associated with producing largesoftware systems are well recognised in the literature(Collins, 1997; Flowers, 1996; Fortune & Peters,1995). This literature is mainly drawn from largeprojects that developed software from scratch, butfinancial service infrastructure often involves buildingon older legacy systems. Such systems may haveinappropriate architectures and data structures thatwere designed for an older generation of infrastructure.

Similarly, the code may be written in a language that isno longer in common use, and the system expertsmay no longer be with the organisation, or, if they arestill within the organisation, they may be committed tothe older system and highly resistant to change.

The intangible nature of software further compli-cates the testing process. With a physical piece ofinfrastructure such as a road, it is possible to reliablyevaluate how much further work is needed, but withsoftware this is often extremely difficult. Softwareengineers have a saying that software is ‘90% finished90% of the time’. This difficulty in evaluating theextent of further work creates extra uncertainties thatcan delay the cancellation of a failing project wellbeyond the point at which it would have been stoppedhad the full extent of the required work been known(Block, 1983; Flowers, 1996; Staw & Ross, 1987).

The problems involved in developing software-intensive infrastructure can be gauged by Gibbs’ (1994,p. 72) point that “for every six new systems that are putinto operation, two others are cancelled. The averagesoftware development project overshoots its scheduleby half; larger projects generally do worse and somethree quarters of all large scale systems are ‘operatingfailures’ that either do not function as intended or arenot used at all”. Similarly, the General AccountingOffice has reviewed large U.S. government IT projectsand noted:

During the last 6 years, agencies have obligated over$145 billion building up and maintaining theirinformation technology infrastructure. The benefitsof this vast expenditure, however, have frequentlybeen disappointing. GAO reports and congressionalhearings have chronicled numerous system develop-ment efforts that suffered from multi-million dollarcost overruns, schedule slippages measured in years,and dismal mission-related results (GAO, 1997,p. 6).

Since financial services are significant users of ITinfrastructure, they are particularly vulnerable to theselarge-scale IT failures. There have been a number offailed projects within the sector, ranging from the high-profile TAURUS system in the London StockExchange to a whole host of lower-profile failureswithin other institutions. These smaller failures areoften covered up as financial institutions attempt tomaintain their reputations for reliability, but interviewssuggest that they are extremely common. Even moredifficult to analyse are the numerous operationalfailures that produce significant losses for the institu-tions involved. Typically a software glitch or designerror may cause a financial institution to miss-sell aseries of trades. Whether these operational failures aredue to the technology, training and operations, orauditing and management, may be impossible to tell.Whatever their cause, there are numerous cases of

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financial institutions losing hundreds of millions ofdollars.

Implementation and FailureIn addition to the difficulties described above, theimplementation of financial infrastructure projects canalso cause a successful design project to ultimately bean operational failure. To a large extent, successfulimplementation is dependent on effective projectmanagement (Pinto & Slevin, 1997, cf. Currie, 1994;Pinto & Covin, 1989; Slevin & Pinto, 1987). Thisincludes, first and foremost, realistic planning in termsof both resources and time (ibid.) and recognising theimportance of ‘soft’ human resource issues (Corbato,1992; Levasseur, 1993). Within the U.K. financial-services sector, contractors work on a rule of thumbthat implementation will be twice as expensive andtake twice as long as the best initial estimates.

As infrastructure technologies are often business-critical, and financial services are time-dependent, theintroduction of new systems can be very risky(Nightingale & Poll, 2000a). The business-criticalnature of these technologies means that back-upsystems must be in place and maintained to a very highstandard. It is not uncommon for system failures toresult in significant financial loss and damage toreputation. One interviewee likened the implementa-tion process to “changing the foundations of a housewith the house still standing” and added that “you also,at any given time, must be able to return thefoundations back to their original state without anyonenoticing you are there”. The business-critical nature ofthese technologies often means that multiple back-upsystems are put in place (many of which are used forsimulation-based training).

One particular area of risk involves data conversionand migration from old to new systems. In someinstances this may be so difficult that it is judgedquicker and easier to re-key all the data. This problemis gradually receding as more and more products arebuilt on similar database platforms and are releasedwith Other Data-Base Connectivity (ODBC) drivers.This highlights the point that effective design involvesconsidering implementation issues at an early stage, inparticular customers’ end-to-end business systems andrequirements. Unfortunately, the changes that newinfrastructure make to these processes are extremelydifficult to foresee before implementation begins.

The risks and uncertainties involved in systemimplementation mean that it is common to run parallelsystems, often for months, in order to iron out last-minute design problems. These systems are also usedfor staff training before implementation. Typically,within the City of London at least, and where it istechnically feasible, the new systems will be run in aminimum of three environments; a live system, a QA(or quality assurance) system, and a test system. Thetest system often contains scrambled data (allowing

wider access than confidential customer informationwould permit) and is used for training and experi-mental late-stage design. The QA system is used formore sophisticated and rigorous testing of designchanges, before components are implemented into thelive system. There are limitations to the use of parallelruns, particularly in larger, more complicated, real-time, environments and where systems are beinginstalled that are fundamentally different from theearlier technology.

The problems associated with the design andimplementation of financial service infrastructure,highlighted in this chapter, mean that project failuresare extremely common. No reliable figures are availa-ble, but anecdotal evidence suggests that at least a thirdof these projects are terminated or are operationallycompromised. A classic, and well documented, exam-ple of financial infrastructure failure was the City ofLondon’s TAURUS system (cf. Currie, 1997; Flowers,1996).

The TAURUS SystemThe TAURUS (Transfer and Automated Registration ofUncertified Stock) system was an infrastructure projectwithin the City of London that was intended to createa paperless share-trading environment. Legal require-ments in the U.K. meant that registers of all sharetrades had to be held by all publicly listed companies.Consequently, even small trades involved a veryinefficient process whereby at least three pieces ofpaper were physically moved around the City. Unsur-prisingly, an international report had criticised thesettlements system, and it was clear that the systemwould be unable to cope with the mass share ownershipthat would follow the privatisation of U.K. publicutilities. By August 1987, for example, a backlog ofnearly 650,000 unsettled deals had built up (Flowers,1996, p. 101).

Moving to a computerised system would, it washoped, reduce time and cut costs by removing the needto physically transfer paper during trading, and themiddlemen who controlled the process. The LondonStock Exchange (LSE) had previously installed a lessambitious Talisman system in 1979 as part of acomputerised share settling system, and its success ledto the proposal in 1981 to automate the entire marketand end paper trading. Consequently, work started onthe system that was to become TAURUS in 1983, andthe LSE as an institution committed itself very publiclyto develop a world-class system that would ensure itsdominance over other European exchanges.

The original architecture of the project involvedreplacing the registrars (who recorded information ontrading) with a single large database administered bythe LSE. Unfortunately, the project had many stake-holders who had vested interests in maintaining theirpositions. Instead of re-engineering from scratch, andthen automating, the project recreated the highly


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inefficient organisation of the exchange. The initialdesign was pushed forward before the various partieshad reached agreement about what it should do, beforelegislation and regulations had been changed andbefore the processes had been simplified. The registrarsquickly saw that the technology would deprive them oftheir lucrative (but inefficient) livelihoods and becamean entrenched group of actors hostile to the newtechnology (Drummond, 1996a, 1996b).

The LSE carried on with the project from 1983 until1988, but the share registrars’ opposition produced anindependent technical review. This showed that theproposed project would be extremely costly (£60million) and very technically complex, requiring twoIBM 3090 mainframes and 560 disk drives to cover thetransactions in the trillion or so shares in issue by theLSE (Flowers, 1996, p. 102). Taurus 1 was conse-quently cancelled, and the Bank of England (the U.K.financial regulator at the time) became involved and setup the Security Industry Steering Committee on Taurus(SISCOT Committee).

In the spring of 1989, the SISCOT Committeesuggested the far cheaper and less risky option ofextending the Talisman system from the original 32Market Makers to around 1,000 other financial institu-tions (ibid.) Shares would be held in accounts on behalfof clients by TACs (Taurus Account Controllers) whowould maintain the records. However, TACs could passthe maintenance of records on to registrars, which hadthe unfortunate effect of replicating the previous paper-based system in parallel with the electronic TACs. Thisdesign, however, made it extremely difficult forcompanies to know who owned their shares, andconsequently, if they were being prepared for a hostiletakeover. Together with stockbrokers, who were con-cerned about costs, the large firms forced through aseries of design changes.

In March 1990 a detailed outline of the project waspublished, and in October 1990 the technical specifica-tions were published. These showed that rather thanbuild a system from scratch, the project would involvebuying and modifying a U.S.-based system. Thedevelopment work was however based on split loca-tions, with 25, out of a team of 40, based at VistaConcepts New York office (Drummond, 1996a, 1996b;Flowers, 1996). By December 1991, as costs escalatedand schedules slipped, the new legal framework for theoperation of TAURUS was produced. By this time thepress were becoming hostile as rumours emerged aboutsoftware-development problems. By February 1993, atechnology review predicted that it would take anotherthree years to build the system and that its costs woulddouble. By March the project was cancelled at a cost ofabout £75 million. The total costs to the City wereprobably in the order of £400 million.

The Taurus project illustrates the range of problemsinvolved in producing financial infrastructure. Inparticular, it shows the major innovation problems in

financial infrastructure concerning the ‘soft’ issuesinvolved in co-ordinating a wide range of users whohave divergent needs and concerns about the technol-ogy. The main cause of the failure was the inability ofthe various agents involved to restructure the inefficientinternal processes before the development of thetechnology was begun. This meant that the project re-created vested interests who were hostile to changes.The slow-moving regulation process, the decision toundertake substantial redesign of a packaged system,and confusion about ultimate control over the projectfurther complicated matters.

The complexities inherent in the design of such asubstantial system were made worse by constant designchanges. The software-intensive nature of the projectmeant that it was very difficult to know the extent offurther redesign work. The lack of organisational co-ordination within the development process, and inparticular the use of two main development sites ondifferent sides of the Atlantic further complicated theprocess. The story does, however, have a happy ending.After the Taurus fiasco, the Bank of England projectmanaged the development of the CREST system whichappeared on schedule and to budget in the mid-1990s.

ConclusionThis chapter has given a brief overview of an extremelycomplex field. It has shown both the diversity ofdifferent forms of financial service infrastructure, andthe heterogeneity of the various firms and organisationsthat use it. Financial services are extremely high-techin many areas, and the traditional view of services asun-innovative clearly needs to be revised. While thereare encouraging signs that this is taking place, ourunderstanding of innovation in services, and financialservices in particular, is a long way behind ourunderstanding of manufacturing innovation.

Financial services differ from manufacturingbecause they involve firms performing functions forcustomers rather than providing goods that performfunctions. These functions are typically consumed asthey are produced, making their provision time-dependent in a way that manufactured products whichcan be stored are not. Firms rely on a range ofinfrastructure technologies for this temporal control oftheir internal processes. The internal processes them-selves are also typically dependent on infrastructure.

Financial services are also different in that theydepend heavily on the liquidity of markets. Thisliquidity can be improved by using external infra-structure technologies to bring together larger numbersof diverse buyers and sellers. As they are based on thecontextualisation and processing of information, ratherthan physical materials, financial services have thepotential to leverage technology to produce moreprofits than is possible in manufacturing. Unfortu-nately, the ability to generate large profits goes hand inhand with the ability to suffer substantial losses. This,

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in turn, creates a much larger incentive for managingrisk than is common in manufacturing.

In this chapter, we have divided infrastructuretechnologies into internal and external. Infrastructurethat is external to financial institutions is oftenprovided by third parties and is used to increase thereach of markets in time and space. This makesmarkets more liquid and improves the financial valueof assets, and makes trading more efficient. Typically,these technologies will involve telecommunicationssystems that have historically ranged from the earliesttelegraph systems to today’s high-powered settlementsystems and global networks.

Internal infrastructure technologies, however, areused within firms to allocate resources to customers.They involve technologies such as customer-focusedinformation systems, ATM machines and their net-works, and internal risk-management systems. In bothcases, there is a tendency for the infrastructures toincrease in size and complexity. External infrastructureincreases in size and complexity because this allowsthe liquidity of markets to improve. Internal infra-structure is driven towards increasing coverage,computing power, speed and reliability, because thisimproves the ability to profitably control transactions.

The tendency towards increases in the size andcomplexity of these infrastructure technologies hasconsequences for innovation. As projects increase insize and complexity they become more uncertain andmore risky. This increased uncertainty comes fromunpredictable emergent phenomena caused by compo-nents interacting in new ways, from technical changesto sub-systems as the time taken for the projectsincreases, changes in regulations, and changes inbusiness practice. All of these factors make defining thesystems’ requirements and freezing them extremelydifficult. The complexity has become even moreproblematic in the last two decades with the introduc-tion of embedded software, and the need to work withlegacy systems.

The complexity and uncertainty of innovation meanthat when problems emerge, the design process has tofeedback to earlier stages in the innovation process.This adds to the cost and schedule of the project.Within software the problem is exacerbated as repeatedredesign can lead to an increasingly unreliable andfragile product.

However, the really important issue in innovation infinancial infrastructure is not technical at all. The bigproblems concern ‘soft’ issues about dealing withmultiple users, dealing with regulators and coping withthe politics of the organisational and institutionalchanges that the introduction of new infrastructurebrings (Murray, 1989). These factors probably gofurther towards explaining why the financial servicesindustry is riddled with failed infrastructure projectsthat have not delivered what they were intended toprovide.

Despite the constant stream of failed projects and, inparticular, IT-based failures, the financial servicesindustry does manage to produce infrastructure that isreliable and performs its task well. Failure may becommon, but successes abound. Given the complex-ities of the innovative tasks involved, this success inboth development and operation is a major achieve-ment. Given the importance of these successes to themodern financial system, and by extension their impacton the global economy, understanding the nature oftheir innovation is important and worthy of furtherresearch.

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Innovation in Integrated Electronics andRelated Technologies: Experiences with

Industrial-Sponsored Large-ScaleMultidisciplinary Programs and Single

Investigator Programs in a ResearchUniversity

Ronald J. Gutmann

Center for Integrated Electronics, Rensselaer Polytechnic Institute, USA

Abstract: University research innovations in integrated electronics are presented with a focus onthe impact of industry support. University knowledge-based innovations are divided intodiscontinuous (or radical) and continuous (or incremental), with the relative contributionsaffected by program funding guidelines, program review methodology, industrial mentoring, andin-house industrial research and development in such a rapidly evolving industry. The impact ofthe Semiconductor Research Corporation (SRC) initiated in 1981, SEMATECH initiated in 1988and the Microelectronics Advanced Research Corporation (MARCO) initiated in 1998 ishighlighted.

Keywords: Integrated electronics; Discontinuous/radical innovations; Continuous/incrementalinnovations.

Introduction

The semiconductor/integrated circuit (IC) industry hasmade significant continuous and discontinuous innova-tions in the past four decades, thereby fueling theinformation-technology revolution that has trans-formed our society. During the past two decades thisindustry has financially supported and guided researchuniversities in the required disciplines, both to obtain asource of research and development personnel and toobtain leading-edge research; the latter can be dividedinto continuous (or incremental) innovations, ofteninvolving contributions to the scientific knowledgebase, and discontinuous (or radical) innovations,namely high-risk, high-payoff ideas which are notembedded in critical paths of the industry roadmap.This paper presents a single investigator perspective ofthe research university role in both types of innovation,

derived from three decades of experience of involve-ment.

The main tenets of the chapter include the following:the semiconductor/IC industry has been very astute indealing with research universities; the research uni-versities have benefited tremendously from thefinancial support and guidance; continuous or incre-mental innovations are more easily accomplished andmore easily measured, often involving insight into theunderlying knowledge base of industry practice; dis-continuous innovation in this field is dominantlyachieved by industrial research laboratories, withresearch universities as fast-followers; large-scalemulti-investigator programs are very effective forcontinuous innovations and for providing multidiscipli-nary educational and research experiences, but havenot established a strong record of discontinuousinnovation; programs work best with a strong personal

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commitment for research success in a professional andenjoyable environment.

Innovation by the semiconductor/IC industry hasfueled the information-technology (IT) revolution thathas transformed our society. The technology base wasestablished mostly by industrial organizations, bothvertically integrated high-tech companies like AT&T(now Lucent) Bell Laboratories and IBM as well asSilicon Valley companies that grew with the semi-conductor/IC industry explosive growth like Intel. Thecost reductions and performance enhancements of thisindustry as succinctly summarized by Moore’s Law arewell known; less well known is the role of thesemiconductor/IC industry in supporting, and workingwith, research universities—in many ways as unique acharacteristic of this industry as IC performanceadvances.

This chapter is a personal reflection of an individualparticipant in the university research and educationenterprise. The author has served as Program Directorat the U.S. National Science Foundation (NSF), wherehe was on the first Technical Activities Board (TAB) ofthe Semiconductor Research Corporation (SRC)1 in agovernment liaison role. He has also served two termson the University Advisory Committee of the SRC andon the SEMATECH1 University Advisory Board. Hehas served as co-Director of a SEMATECH Center ofExcellence (SCOE) on Multilevel Interconnects atRensselaer and participated in other SRC, NSF Engi-neering Research Center (ERC), Defense AdvancedResearch Program Agency (DARPA), and industry-sponsored multi-disciplinary programs—and manysmaller research programs sponsored by U.S. govern-ment agencies and national/international companies.For five years, the author served as Director of theRensselaer Center for Integrated Electronics.

The chapter emphasizes the research university rolein innovation for the semiconductor/IC industry andhighlights the role of the SRC (and related industryorganizations); the role of U.S. government supportfrom NSF, DARPA and other mission agencies is onlybriefly mentioned in highlighting innovative issues, asthis role is similar in all disciplines. Innovation isdivided into two categories: continuous (or incre-mental) innovation and discontinuous (or radical)innovation. One main tenet of the paper is that theSRC-based funding has been extremely successful incontinuous innovation (as well as in providing fundingfor graduate student education and training) but hasbeen less successful in achieving discontinuous inno-vations.

The semiconductor/IC industry has recently movedto more discontinuous innovation-focused fundingthrough the Microelectronics Advanced Research Cor-poration (MARCO). MARCO is a wholly owned

subsidiary of SRC, enabling utilization of SRC man-agement functions (such as contracting agreements) asappropriate. MARCO was established because of boththe decline in long-range research funding in majorindustrial laboratories and the increasing impedimentsto scaling as projected by Moore’s Law and delineatedin industry-generated roadmaps (the National Technol-ogy Roadmap for Semiconductors (NTRS) and themore recent International TRS and ITRS).

Normally radical and incremental innovation refersto business practices or products in a business environ-ment. In the context of this chapter referring touniversity research, discontinuous (or radical) andcontinuous (or incremental) innovation refers to theeffect of the research results. University knowledge-based research often provides the scientificunderpinnings of current technological practice,thereby enabling better engineering design. However,university research often establishes new technologicalapproaches that lead to significantly improved per-formance, lower-cost and/or higher-reliabilitytechnology. When successful, these higher-risk endeav-ors are referred to as discontinuous (or radical)innovations.

The remainder of this chapter is organized asfollows. First, a brief history of the industry funding ofuniversities is presented, focusing on the major role ofthe SRC, but also including SEMATECH andMARCO. Second, the major tenets of the paper aredeveloped, using the technology area of most involve-ment by the author (IC on-chip interconnects). Third,these tenets are discussed using the recently initiatedMARCO Focus Centers as a benchmark on thesetenets. Finally, the author’s perspectives are summa-rized.

Semiconductor/IC Industry Funding of ResearchUniversitiesThe semiconductor/IC industry initiated a collaborativemethod of funding research universities in 1982, whenthe Semiconductor Industry Association (SIA) estab-lished the Semiconductor Research Corporation(SRC). Originally three center-type programs wereestablished (single-university multiple-investigatorprograms) with many smaller single-investigator pro-grams. The emphasis was entirely on silicon-basedresearch, as the U.S. government-sponsored universityresearch programs were dominantly in compoundsemiconductors. The SIA clearly established the pro-gram both to establish a source of pre-competitivesilicon-based research and to increase the source ofgraduates with education and training in silicon ICtechnology and design.

In the first decade the SRC funding clearly impactedthe U.S. research programs in semiconductors/ICtechnology by funding both center and single-inves-tigator research programs at major researchuniversities, and by establishing educational programs

1 The SRC and SEMATECH are described more fully in thenext section of this chapter.

Innovation in Integrated Electronics and Related TechnologiesChapter 9

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(both funding selected curriculum developments andgraduate fellowships for U.S. citizens and permanentresidents). The SRC is a research management organi-zation, with various industry boards to provide inputinto funding decisions and program evaluations. TheSRC-sponsored core programs have always beenfocused to industry concerns and more micro reviewedthan government funded university research, with theanticipated pluses and minuses of such a researchmanagement process. However, the SRC funding andprogram mentoring clearly was positive for the uni-versity research community and for the graduatestudents involved, although the micro-review processhas often been a source of friction. Originally con-sidered by many university researchers as a minorsupplement to government-funded research, the SRCevolved into an important complementary componentof university research within a decade. The industrialleveraging of government-funded research establisheda broad-based three-way partnership (industry, govern-ment and research university).

In the late 1980s the SIA established SEMATECH asa research and development cooperative to helprevitalize the U.S. semiconductor industry. As part ofthe post-site selection process in which Austin, Texaswas selected as the venue for SEMATECH, variousSEMATECH Center of Excellence (SCOE) researchprograms were established in 1989. The SCOEsfocused on different research areas of semiconductormanufacturing, received approximately $1,000,000($1M) per year for five years, and were managed by theSRC with its already established infrastructure formanaging and reviewing university research programs.Generally the SCOEs were focused on shorter-rangeresearch than the SRC programs, although the time lineis difficult to establish firmly in such a rapidly evolvingarea of research. Some conflict arose between the morefocused view of SEMATECH with a well-definedresearch and development agenda and the SRC with alonger research mission and a longer time line forresearch results (although shorter range than manygovernment-sponsored research programs). After theinitial five years, the research funding for the SCOEswas incorporated into the SRC program to a largeextent, although SEMATECH continues to fundsmaller, nearer-term more-focused university researchprograms directly.

The second decade of the SRC was marked by fullincorporation into the fabric of U.S. research uni-versities, relative stability of a mode of operation,absorption of SCOE program emphasis and researchfunds (as described above), establishing a closeworking relationship with U.S. government fundingagencies (particularly NSF and DARPA) includingjoint support of center programs, establishing a newSIA-funded SRC-managed multi-university researchcenter funding organization (MARCO) and the inter-nationalization of both the SRC and SEMATECH. The

SRC is fully established as a major university fundingsource for semiconductor/IC materials, processing,devices, design and manufacturing research, with anagenda varying from short-term to long-term focus(although still not as long range as government-sponsored research except for the newer MARCOprograms). Moreover, the past five to eight years hasseen significant collaboration with U.S. governmentfunding agencies in areas of mutual interest, includingformal agreements and collaborative funding.

The two more recent modifications have resultedfrom the internationalization of the SRC and theestablishment of the MARCO Focus Centers. Whilethe impact of the former is small at this time, theimpact could be significant in the next five years asinternational companies (particularly European andPacific Rim) participate in collaborative universityresearch and the SRC funds more research at inter-national universities. The company overhead requiredto fully take advantage of the SRC program (includingparticipation in program reviews, funding authoriza-tion/allocation meetings, and long-range policymeetings) may inhibit a rapid evolution to inter-nationalization of the SRC; full internationalizationwill be difficult to achieve.

The second recent modification has clearly beensignificant, namely the establishment of MARCOFocus Centers for multi-university collaboration inlong-range research necessary to maintain the rate ofadvancements of price and performance predicted byMoore’s Law in the presence of upcoming limitationsto CMOS IC scaling as delineated in the NTRS andITRS reports. The SIA, stimulated by Dr. Craig Barrettof Intel, established MARCO to lead a new universityresearch thrust with a long-range focus in suchtechnology needs. The first two multi-university cen-ters were established in 1998 (Interconnect as well asDesign and Test), with two additional centers estab-lished in 2000 (Materials, Structures and Devices aswell as Circuits, Systems and Software) and two moreanticipated in 2002–2003. Steady-state funding of eachcenter is projected to be $10 million/year.

These MARCO centers are impacting the researchuniversity landscape in a manner similar to the NSFEngineering Research Center (ERC) program begun inthe mid-80s and the earlier DARPA (now NSF)Material Research Laboratory (MRL) program begunin the 60s. The long range impact of MARCO centerscompared to the long-standing MRL and ERC pro-grams is difficult to project, but the impact couldbecome as significant if the industry continues acommitment to such large-scale, long-term universityresearch throughout its cyclic business environment.Since the MARCO program is jointly supported bySIA Board of Director companies (50%), equipment/materials/software suppliers (25%) and DARPA (25%),funding stability and broad support have been estab-lished in a relatively short time frame.

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Ronald J. Gutmann Part VII

Development of Major Tenets

In this section the author combines his experience andbackground outlined in the Introduction with theSemiconductor/IC Industry research funding/impactoutlined in the previous section to develop the majortenets of this paper. The section is split into three parts:first, a technology summary of IC on-chip inter-connects so that the experience and perspectivedescribed can be focused more specifically; second,personal experiences from major interconnectsresearch programs such as the SCOE on MultilevelInterconnects, the SRC Center on Advanced Inter-connect Science and Technology (CAIST) and theMARCO Interconnect Focus Center (IFC) for Giga-scale Integration, complemented by related singleinvestigator programs; third, an innovation perspectiveabstracted from these experiences.

IC On-Chip Interconnects: A Technology Summary

In the late 1980s CMOS technology scaling indicatedthat conventional IC on-chip interconnects would limitmicroprocessor speed within the next decade. On-chipinterconnects had gradually evolved in the number ofmetal interconnect levels, but the technology had notchanged significantly—aluminum lines (or trenches),oxide interlevel dielectric (ILD), patterning of metallines followed by ILD deposition with gap fill andplanarization constraints, and a change from aluminumto tungsten vias for vertical interconnection betweenaluminum lines. As the minimum feature size con-tinually decreased, CMOS devices become faster, butthe interconnect delay does not scale similarly. As aresult the interconnect delay continually increasesrelative to the device delay, projecting that microproc-essors would become interconnect-limited.

In the search for reduced interconnect delay, metalswith a higher electrical conductivity and dielectricswith a lower dielectric constant (low-k ILD materials)were explored in the 1990s. The Research Division ofIBM led the development, both in copper and alter-native high conductivity conductors and in polymersand other low-k dielectrics. While IBM had doneappreciable research in the mid-1980s and beyond,other companies and the research universities did notbecome appreciably involved until the 1990s when theinterconnect bottleneck became more widely realizedand the limitations of aluminum and oxide as the keyon-chip interconnect materials became clear.

Since the choice of conductors is relatively limited(copper, silver and gold) and the choice of low-k ILDsis relatively large, the industry settled initially oncopper as the interconnect conductor of choice.However, since copper cannot be patterned by reactiveion etching (RIE) at room temperature (easily donewith aluminum), an alternative patterning strategy wasestablished for copper. In the so-called Damascenetechnique, the ILD is patterned for trenches (or lines)

and vias, followed by copper and appropriate linerdeposition and then chemical-mechanical planarization(CMP) to eliminate copper and liner between thetrenches and vias. The CMP process was originallyintroduced to planarize the ILD and/or vias in inter-connect structures to allow an increasing number ofon-chip interconnect levels, but the use in a Damascenepatterning strategy was a key development for on-chipcopper interconnects.

The research and development in this field washighlighted by the IBM announcement in the Fall of1997 when copper was announced as going intomanufacturing, with products introduced within a year.Three years later IBM announced the first low-k ILD togo into manufacturing, a spin-on polymer from DowChemical called SiLK (for silicon low-k technology).Numerous IC manufacturers, both large verticallyintegrated companies and manufacturing foundries,have introduced copper interconnects in manufactur-ing, with some low-k ILDs (mostly organosilicate glassrather than SiLK).

In summary, the 1990s was the interconnect technol-ogy decade, with four major advances being introducedinto IC manufacturing:

(1) CMP enabling six-to-eight metal levels of on-chipmetallization with high yield during IC manu-facturing;

(2) copper metallization for lines and vias to replacealuminum lines and tungsten vias for improvedelectrical conductivity and electromigration capa-bility;

(3) dual Damascene patterning to replace metal reac-tive ion etching (RIE) and dielectric gap fill toimprove line definition and to lower manufacturingcosts;

(4) low-k ILDs to replace oxide for lower line andcoupling capacitance and, when combined withcopper trenches, lower interconnect delay.

SCOE, CAIST, MARCO and Related Research

The recognition of the growing importance of on-chipinterconnect technology in the late 1980s coincidedwith the establishment of SEMATECH and the fundingof the SCOEs. The New York SCOE was established atRensselaer in 1988–1989 in Multilevel Interconnects,with research activities to extend the knowledge base inmore conventional technology as well as in new areasof copper metallization and CMP. The award of thisprogram initiated a multi-disciplinary research pro-gram in IC interconnects at Rensselaer and has led toensuing center-based programs such as the SRCCAIST launched in 1996 (centered at Rensselaer) andstrong participation in the MARCO Interconnect FocusCenter (IFC) led by Georgia Tech and launched in1999. Faculty at Rensselaer who have had keyleadership roles in this decade-plus thrust in IC


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interconnects include Tim Cale, Dave Duquette, BillGill, Ron Gutmann (author), Toh-Ming Lu, JimMeindl, Shyam Murarka and Arjun Saxena, fullprofessors from four different academic departments.

In parallel with this semiconductor/IC industryconsortium support, this support was highly leveragedwith other research support, both cost-sharing fundsfrom Rensselaer and New York State and additionalresearch support. The latter includes both U.S. govern-ment support and single company support for specificresearch. While most of these programs were relativelysmall, a large-scale program that was funded by IBM inparallel with the SCOE emphasized polymer materialsfor low-k ILDs, an area not funded in the SCOE at thetime. This interactive program with IBM allowedRensselaer to establish a useful base in low-k materials,processing and characterization, and completed anadvanced interconnect technology portfolio.

The Rensselaer programs in copper CMP and coppermetallization technology (liners, alloys and Damascenepatterning strategies) became particularly well known,with Rensselaer established as a major researchuniversity in IC interconnects as a result of the SCOEand CAIST programs (and related leveraged researchsupport). However, the role of IBM Research ininitiating and leading the development of copperinterconnect research is clear, with Rensselaer andother research universities providing an underlyingscience base in many areas and in providing a databasefor other company investments and directions. Thedifferences between the industrial contributions and theresearch university contributions are best compared bytracking not only refereed journal articles and con-ference papers, but also the patent literature.

The author believes that the SRC program review, arelatively micro-look at deliverables and annual results,is effective in educating graduate students for industrialopportunities and in providing a desirable scientificknowledge base for industrial practice (i.e. continuousinnovation). However, the process does not encouragelong-term research focused on discontinuous innova-tions, as the programs are reevaluated and redirectedwith modified budgets on an annual basis. Mostimportantly, the industry participants are asked toevaluate individual tasks based upon the impact of theresearch to their company rather than to the industry atlarge. While this approach is desirable to keepindividual companies pleased with their SRC invest-ment, the impact on long-term research of a high-riskhigh-payoff nature with discontinuous innovationresults can be (and has been in the author’s opinion)negative.

The SIA has recognized the need for different modeof research management to better encourage ‘out-of-the-box’ thinking and discontinuous innovation inestablishing MARCO as a new subsidiary of the SRC.National and international roadmaps by the industry(NTRS and ITRS) indicate many future needs where

no known solutions exist, resulting in a consensus thatincreased funding of consortium-managed universityresearch was essential. The resulting major differencesbetween MARCO and SRC core research programshave been the individual program size, the emphasis onlong-range research and the increased emphasis ondiscontinuous innovation research results, as well asthe mode of research management. The SIA requiresmore high-risk high-opportunity research in all theMARCO programs, and anticipates many discontin-uous innovations to emerge.

As an example, the Rensselaer IFC program (part ofthe New York State program led by the University atAlbany) emphasizes wafer-scale three-dimensional(3D) ICs, optical interconnects for chip-to-chip and on-chip broadband interconnect, terahertz technology forinterconnects and characterization, carbon nanotubesfor interconnects, nano-metrology techniques and mul-tiscale materials and process modeling. The 3Dprogram uses the on-chip interconnect technology,fundamental understanding and research experiencefrom the 1990s to establish a new approach tomonolithic wafer-scale 3D ICs, using dielectric adhe-sives to bond two fully processed IC wafers and copperDamascene patterning to form inter-wafer intercon-nects. Such a program would not be possible withoutthe research expertise established by the SCOE,CAIST and related interconnect programs, companionresearch expertise in IC design and packaging technol-ogy and the new funding paradigm established by theMARCO Focus Centers.

Innovation PerspectiveBased upon this experience and perspective, the authorbelieves that the SRC-funded core research programsare effective at continuous innovation, with the uni-versity research community providing the scientificunderpinnings for recently developed industry innova-tions. In addition, the research universities can beeffective fast followers when stimulated and mentoredby leading industrial research laboratories, therebycontributing to truly discontinuous innovations affect-ing the semiconductor/IC industry. The more recentMARCO program has been initiated to provide incen-tives and freedom to pursue truly discontinuousinnovations by the research university community. Theresults of this initiative will take five years or more toevaluate fully.

Another perspective is the generally inhibiting roleof large university centers on truly discontinuousinnovation. While in some disciplines centralizedshared facilities may require larger multi-investigatorprograms, the relatively small single (or an interactivefew) investigator(s) may be best for truly discontinuousinnovations, where new approaches may be investi-gated over some extended duration in relative privacywithout specific deliverables and milestones. Largemulti-disciplinary programs have many advantages and

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many attractive results, but the author does not believethat discontinuous innovations are best achieved insuch an environment, particularly with traditionalmeans of SRC program management and review.Effective management of technological innovation isdifficult enough within an industrial organization, asdescribed by both J. Bessant and R. Katz in thishandbook.

In fact, a main purpose of this chapter is to put fortha personal perspective (rather than fully annotated withreferences) to stimulate further discussion, not only inthe semiconductor/IC research community, which hasbeen extremely innovative in dealing with universitiesand where these issues have been examined, but also inother industrial sectors. The SRC core and traditionalcenter programs have clearly been successful; com-plementary programs like the SCOEs have had animportant role. The MARCO program may be anothercomplementary program with a relatively short life-time, but the MARCO program is a great opportunityfor the university research community to extend its rolein the discontinuous innovation arena. Hopefully,MARCO will become a long-term important ingredientof the SRC research portfolio, with many discontin-uous innovation accomplishments. Challenges ininnovation management as described by J. Bessant inthis handbook are magnified with such industrialconsortia. However success can be achieved withcommitment and vision.

This unique industry support of integrated electron-ics has been in parallel with government support.Research and innovation contributions of such pro-grams have not been included here, but the integratedelectronics field has a similar history as presented by Y.Miyata elsewhere in this handbook. However, theimpact of the U.S. Semiconductor industry support ofuniversities described here is more positive than themore general evaluation by Miyata (2003). Perhaps thisresearch field is unique in ways the author has beenunable to abstract.

Discussion of Specific TenetsMain tenets of the chapter include the following: thesemiconductor/IC industry has been very astute indealing with research universities; the research uni-versities have benefited tremendously from thefinancial support and guidance; continuous or incre-mental innovations are more easily accomplished andmore easily measured, often involving insight into theunderlying knowledge base of industry practice; dis-continuous innovation in this field is dominantlyachieved by industrial research laboratories, withresearch universities as fast-followers; large-scalemulti-investigator programs are very effective forcontinuous innovations and for providing multidiscipli-nary educational and research experiences but have notestablished a strong record of discontinuous innova-tion; programs work best with a strong personal

commitment throughout. In this section these majortenets are presented with a brief discussion of each.

Semiconductor/IC Industry Astute in Dealing withResearch UniversitiesThe SIA in launching the SRC in 1982, the SCOEprogram with SEMATECH in 1988–1989 and theMARCO Focus Research Centers in 1998 has demon-strated an ability to understand the operation ofresearch universities and to establish a mode ofoperation which accommodates professorial independ-ence and creativity for the industry advantage. Like anyeffective enterprise, the SRC has established a basicoperating mode in becoming a permanent fixture inresearch university support.

The research university community has benefitedsignificantly from SRC programmatic interactions, inthe funding support that is provided (direct costs and‘full’ government-equivalent indirect costs) and in thementoring of research program directions and graduatestudents. While the SRC mode of operation is differentthan government funding agencies or single companysupport/interaction, the complementary nature of thetime frame of the research focus and the review processoffers a complementary breadth to more classicalsponsor interactions.

Continuous Innovations More Easily Accomplishedand MentoredThe SRC core programs, both center-based andindividual investigator-focused, are more amenable tocontinuous innovations with major contributions to thescientific underpinnings of present or near-futureindustry practice or relatively small advancements tothe field. The proposal and program review practicesare the major factors, as the research activities arereviewed in detail annually from the perspective of theindividual company sponsors. This process mandatescontinuous contributions, which leads to short-timehorizon research.

Discontinuous Innovations Led by Industrial ResearchLaboratoriesKey discontinuous innovations in the semiconductor/IC industry have resulted from major industrialresearch laboratories. While this statement may bedebatable in isolated areas, the author believes that thetenet is widely true and has presented the IC on-chipinterconnect paradigm shift in the 1990s as anexample. Universities can be fast-followers and makemajor contributions and may occasionally introducediscontinuous innovations from SRC and governmentfunded programs (e.g. Rensselaer in interconnects inthe 1990s), but the overall record is not outstanding.The new MARCO Focus Centers are an excellentopportunity for research universities to become astronger contributor in discontinuous innovation, at atime when major industrial research laboratories are


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often shrinking their time horizon and when dis-continuous innovation is needed to maintain progressaccording to Moore’s Law as CMOS shrinkingbecomes more difficult and more expensive. TheMARCO research objectives, funding level andresearch management approach provide such anopportunity.

Large Center Program Perspective on Innovation

Large center programs have a few tremendous advan-tages, both those within a research university and thosewith multiple research university participation. Theyencourage/require meaningful multi-disciplinary inter-actions, sharing of expertise and facilities, and effectiveinteractions among faculty, research staff, graduatestudents and industrial researchers and managers. Theexperience for all stakeholders is worthwhile, partic-ularly for graduate students. However, as described, theauthor feels strongly that such programs tend to focuson continuous innovations rather than discontinuousinnovation where a greater risk of failure is involved.The MARCO program removes most of the impedi-ments; five years will be needed before the impact canbe evaluated.

Strong Personal Commitment Necessary for Success

All such programs work best where there is a truecommitment to the objectives of the program, princi-pally the participating faculty and the industrialmentors. Clear and honest communication, includingrespecting the intellectual property (IP) of the partici-pants, is a necessary ingredient for achieving the mosteffective research results in a professional and enjoy-able environment.

SummaryThe author’s perspectives and major tenets of thischapter are presented in the previous section. While theperspectives presented are a personal viewpoint, theauthor believes that many in the SRC researchcommunity hold similar views, both the universityresearchers and industrial mentors. Whatever the viewtoward continuous and discontinuous innovation, theSIA has clearly changed the scope and focus ofresearch universities in the field of semiconductors/ICs,both in the past two decades and for the foreseeablefuture. The initiative with the MARCO Focus Centersindicates that the SIA continues to evolve differentfunding mechanisms in the future, but probably notuntil the MARCO Focus Centers become fully estab-lished (5-year funding ramp anticipated) and can befully evaluated (2008 time frame). Other industrysectors could well benefit from a careful review of thetwo-decade experience of the semiconductor/IC indus-try in forging new relationships between industry,government and research universities. The researchuniversity role in the contributing to the semiconduc-

tor/IC industry has clearly been enhanced by these SIAinvestments as managed by the SRC.

AcknowledgmentsThe author gratefully acknowledges his faculty col-leagues for their technical contributions to theRensselaer program and for shaping the author’sperspectives presented here, namely T. S. Cale, T. P.Chow, D. J. Duquette, P. S. Dutta, W. N. Gill, T. M. Lu,J. F. McDonald, J. D.Meindl, S. P. Murarka, K. Rose,E. J. Rymaszewski and A. N. Saxena. He has alsobenefited over this period from supervising manytalented doctoral students, including S. Banerjee, C.Borst, K. Chatty, T. Chuang, M. Heimlich, C. Hitch-cock, Y. Hu, N. Jain, J. Jere, S. Khan, V. Khemka, B. C.Lee, T. Letavic, P. Lossee, K. Matocha, D. McGrath, J.Neirynck, D. Price, R. Saxena, P. Singh, S. Saroop, B.Wang, C. Wong, Y. Xiao, and A. Zeng. The author’sperspective on discontinuous innovation has beeninfluenced by interactions with faculty in the Rensse-laer Lally School of Management, namely R. Leifer, C.McDermott, J. Morone, G. O’Connor, L. Peters, M.Rice and R. Veryzer.

Discussions over these issues with many industrialresearch leaders have helped shape the author’sperspectives, particularly J. Carruthers (Intel), G.DiPiazza (AMP), D. Fraser (Intel), D. Havemann (TI),W. Holton (TI/SRC), R. Isaac (IBM), J. Ryan (IBM),R. Schinella (LSI Logic), W. Siegle (AMD), L.Sumney (SRC) and B. Weitzman (Motorola). Discus-sions with colleagues from other research universitieshave also been very beneficial, particularly J. Gibbons(Stanford), G. Haddad (University of Michigan), D.Hodges (University of California, Berkeley), A.Kaloyeros (University at Albany), N. Masnari (NorthCarolina State University), T. McGill (Cal Tech), R.Reif (MIT), A. Tasch (University of Texas, Austin), andE. Wolf (Cornell).

The funding of the SIA through the SRC, SEM-ATECH and MARCO, many semiconductor/ICcompanies and government agencies over this period isgratefully acknowledged and is much appreciated.

ReferencesSemiconductor Industry Association (SIA) Roadmaps

—National Technology Roadmap for Semiconductors,(NTRS), 1992, 1994, and 1997

—International Technology Roadmap for Semiconductors(ITRS), 1999 and 2001

all available from the:

Semiconductor Industry Association4300 Stevens Creek BoulevardSan Jose, CA 95129

Semiconductor Research Corporation (SRC)P.O. Box 12053Research Triangle Park, NC 27709

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SEMATECH, Inc.2706 Montopolis DriveAustin, TX 78741

Semiconductor/Integrated Circuits (ICs) Technology

—S. K. Ghandhi, ‘VLSI Fabrication Principles’, WileyInterscience, 2nd edition, 1994.

—Y. Nishi and R. Doering, ‘Handbook of SemiconductorManufacturing Technology’, Marcel Dekker, 2000 (Chap-ter 37 by G. D. Hutcheson entitled ‘Economics ofSemiconductor Manufacturing’ contains an excellentdescription of Moore’s Law).

IC Interconnect Technology

—IBM Journal of Research and Development special issueentitled ‘On-Chip Interconnection Technology’, Vol. 39,July 1995, pp. 369–520 (excellent description of pre-copper interconnects).

—S. P. Murarka, I. V. Verner and R. J. Gutmann, ‘Copper-Fundamental Mechanisms for MicroelectronicApplications’, Wiley Interscience, 2000.

—J. M. Steigerwald, S. P. Murarka and R. J. Gutmann,‘Chemical Mechanical Planarization of MicroelectronicMaterials’, Wiley Interscience, 1997.

Innovation Literature

—R. Leifer, C. M. McDermott, G. Colarelli, O’Connor, L. S.Peters, M. Rice and R. W. Veryzer, ‘Radical Innovation—How Mature Companies Can Outsmart Upstarts’, HarvardBusiness School Press, 2000 (this book uses ‘radical’ and‘incremental’ innovation in a similar manner as ‘dis-continuous’ and ‘continuous’ innovation in this chapter,with a focus on commercial products rather than theunderlying knowledge base emphasized here).

—Y. Miyata, ‘An Analysis and Innovation Activities of U.S.Universities’, in L. V. Shavinina, editor, ‘InternationalHandbook on Innovation’, 2003.

—J. Bessant, ‘Challenges in Innovation Management’, in L.V. Shavinina, editor, ‘International Handbook on Innova-tion’, 2003.

—R. Katz, ‘Managing Technological Innovation in BusinessOrganizations’, in L. V. Shavinina, editor, ‘InternationalHandbook on Innovation’, 2003.

Note: Since writing this chapter, the MARCO centers have matured with the promise of discontinuous innovation being realizedin various areas.

RJG, April 2003


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The Barriers Approach to InnovationAthanasios Hadjimanolis

Department of Business Administration, Intercollege, Cyprus

Abstract: The nature of barriers is first clarified and their effect on innovation is broadly outlined.The various taxonomies of barriers are presented and critically evaluated. Their impact andmechanisms of action are then developed. The pattern of barriers in different contexts isconsidered and various aspects of a theoretical explanation of barriers are discussed. Sincebarriers are especially important in small firm innovation and in difficult environments, e.g. insmall countries, these special cases are studied in some depth. Finally, the empirical studies onbarriers are reviewed. The chapter ends with suggestions to overcome barriers and a conclusionssection.

Keywords: Innovation; Barriers.

IntroductionThe importance of innovation for the competitivenessof firms and as an engine of growth at a regional orcountry level is widely recognized (Hitt et al., 1993;Tidd et al., 1997). At the same time it is believed thatthere is an ‘innovation problem’, in the sense that themajority of organizations are not doing enough tointroduce or adopt innovations (Storey, 2000). One ofthe approaches to examining the reasons for inadequateinnovation is the study of constraints or factorsinhibiting innovation—that is the ‘barriers to innova-tion’ approach (Piatier, 1984). There exists a largeamount of literature on innovation barriers (Bitzer,1990; Piatier, 1984; Witte, 1973). Despite the extensiveempirical research on barriers it seems that there is,however, no conceptual framework that would inte-grate the factors acting as barriers and would permit anexplanation of their combined effect.

What follows is a relatively selective review thatattempts to present a reasonably comprehensiveaccount of the theory and the existent research. Thestudy of barriers provides an insight into the dynamicsof innovation, while it is also a first step in the processof overcoming them. Bannon & Grundin (1990) arguethat the existence of barriers in innovation is the rulerather than the exception and that:

“In most cases organizational and business proce-dures work against both successful development anduse of innovative products” (Bannon & Grundin,1990, p. 1).

The aim of this chapter is therefore to relate the reviewof innovation barriers to a practical understanding ofthe innovation process and to action for its facilitationwith the elimination of barriers.

Innovation as a complex phenomenon needs amultilevel model of analysis (Drazin & Schoonhoven,1996). Barriers can then be studied at various levelsstarting from the individual and moving up to the firm,the sector or community, and the country level. Whileall levels are considered in the following sections, themain emphasis is on barriers to innovation at the levelof the firm. The other levels are also considered mainlyin relation to the firm. For example, innovative action atthe individual level is considered from the point ofview of managers or employees, rather than consumersor members of social groups. Innovation is initiallyviewed in a very broad sense; later the focus is ontechnological innovation in the context of the privatefirm. The following broad definition is used:

“Innovation is the search for and the discovery,development, improvement, adoption and commer-cialization of new processes, new products and neworganizational structures and procedures” (Jorde &Teece, 1990, p. 76).

Technological innovation mainly focuses on newprocesses and new products.

In the following sections the nature of barriers is firstclarified, and various ways of classifying barriers areconsidered in detail. The broad classification intointernal and external barriers is used for a descriptive

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exposition of the main barriers. The next sectiondiscusses the role of barriers within the innovationprocess, their points of impact, as well as their effectson innovation. Since barriers may act at various pointsof the innovation process, this process is brieflydiscussed and the various models for innovation arementioned. The static view of considering barriers asantecedents of innovation and predictors of outcome isexpanded in this section into a dynamic analysis oftheir evolution, and interaction, during the variousphases of the innovation process.

Next, the theoretical explanations for the existenceof barriers are considered. Barriers—especially inter-nal ones—may emerge as symptoms, and their deepercauses and underlying factors have to be accounted for.Since barriers are especially critical in the case ofinnovative small firms, as well as in difficult environ-ments, as is the case with small and developingcountries or countries in transition, these special casesare studied in some depth. The section also includes ashort overview of some of the existing empirical dataon barriers in both industrial products and servicesinnovation in small firms. Then the methodologicallimitations of such empirical studies are discussed.Some measures and ways to overcome barriers by thefirm itself and by regional/national authorities areproposed. The chapter ends with conclusions andsuggestions for further research.

Nature and Classification of Barriers

Nature of Barriers

A barrier to innovation is any factor that influencesnegatively the innovation process (Piatier, 1984). Thefactors with a positive influence are called facilitators.Barriers to, and facilitators of, innovation are, however,related. On the one hand, facilitators may turn tobarriers, or vice versa, as the firm evolves throughoutits life cycle stages or as external conditions change(Koberg et al., 1996). However, many barriers areactually due to lack of facilitators. It is only then, foranalytical convenience, that barriers are studied sepa-rately from facilitators of innovation and for acomplete picture the study of both is necessary.Barriers are also known as obstacles, constraints, andinhibitors. Although there may be subtle differences inthe meaning of these terms, they are used as synonymshere.

It is important at this point to consider someassumptions in the barriers approach.

• An implied assumption is that innovation is inher-ently a good thing and any resistance to it byemployees or managers, which could be interpretedas a barrier, is unwelcome (Frost & Egri, 1991). Thisis not always true, and resistance or skepticism may

be actually well founded and a positive action for thegood of the firm (King, 1990).

• It is also frequently assumed that removal of barrierswill somehow restore the natural flow of innovation.This is far from true, because innovation is arelatively unnatural phenomenon in the sense that itneeds motivation, extraordinary effort, tolerance torisk and coordination of the activities of many actors(Hadjimanolis, 1999; Tidd et al., 1997). It seems thatthe removal of barriers is a necessary—but not asufficient condition—for innovation to take place.

• A third assumption is that existence of barriers is byitself a bad thing, and all efforts should be made toremove them. While this is generally the case,barriers may occasionally turn into positive factorsstimulating innovation or providing valuable for thefuture learning experience for the firm (Tang & Yeo,2003). For example learning to live with barriers ortheir gradual elimination at a local level may be anecessary first step in the internationalization processof innovative firms. Internationalization is sometimesvital for the long-term survival and success of firmshaving small national markets (Fontes, 1997).

• A dubious assumption is that focusing on innovationbarriers is more important than focusing on reinforc-ing positive factors for innovation. Perhaps both areequally necessary and complementary.

Classification of BarriersDue to the multitude of barriers, a classificationscheme would be useful in their study. Barriers can beclassified in a number of ways, and there are severaltypologies. They are usually based on the origin orsource of barriers. A useful classification is in distin-guishing between internal to the firm and external tothe firm barriers (or endogenous and exogenousrespectively (Piatier, 1984)). Similarly other types ofbarriers, e.g. export barriers, are classified into internaland external (Leonidou, 1995). External barriers havetheir origin in the external environment of the firm andcannot be influenced by it, while the firm can influenceinternal barriers. Barriers can further be classified intodirect/indirect according to their impact on the innova-tion process and into general /relative. General barriersare barriers affecting all firms, while relativebarriers selectively affect some of them (e.g. in specificsectors). Barriers could also be classified as tangible orobjective and cognitive or perceptual. The latter are not‘real’ barriers, but are subjective and perceived by thefirm. This distinction is further considered in the nextsections, although it should be noted that the existenceand significance of all barriers is related to theperceptions of the firm’s managers and employees.

As mentioned above, obstacles can also be con-sidered at various levels starting from the micro-leveland ending up at the macro-level. These are theindividual, group, firm level, inter-organizational level,and regional/national level (King, 1990). The first three

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can be considered as internal barriers, and the last threeas external. Barriers can refer to the presence orabsence of some factors. The most common classifica-tion of external and internal barriers is used here for adiscussion of barriers. Table 1 illustrates the classifica-tion with some sub-categories of each major type.External barriers can be subdivided into market related,government related and other.

External Barriers(i) The market-related barriers refer to various typesof market failure and other market induced innovation-hampering factors. One type of market failure refers toinsufficient appropriability (i.e. ability of the innovat-ing firm to capture rents or profits created throughinnovation (Teece, 1986)). Other types include marketrisk, inadequate size of R&D that is undertaken byprivate firms, and externalities (Cohen & Noll, 1991;Sanz-Menendez, 1995). The public good character ofinnovation may lead to know-how leakage and otherspillovers, which impair innovation incentives and mayact as barriers (Jorde & Teece, 1990). Supply anddemand deficiencies may also present barriers. Forexample lack of skilled employees in the market orlack of innovative users. The nature and intensity ofcompetition within the market affect the profitabilityand strategy of firms and are indirect causes ofbarriers.

Another market-related barrier is what has beencalled the ‘short-termism’ problem (Storey, 2000). It isan effect of pressure, e.g. from the stock exchangemarket on public quoted firms, to show profits in theshort term. Investments with a long-term paybackperiod, as many innovation projects tend to be, are thenneglected in firms with a short-term horizon. Suchprojects are however necessary for the success andeventual survival of the firm, although they may havean adverse short-term impact on profits.

Under the market-related barriers the most fre-quently mentioned type is that of financial barriers(Piatier, 1984). These barriers may result from thereluctance of lenders, e.g. commercial banks, toshare—the perceived as high-risk of innovation pro-jects. Information asymmetry between lenders andborrowers is especially high in the case of innovation,and outside capital providers have difficulty in thefinancial assessment of innovative projects (Pol et al.,1999). This fact aggravates the risk and uncertaintyfactor. Innovators are also frequently unable to providethe collateral for loans as a security for the bank.

Financial barriers are especially important for smallfirms and for start-ups (Storey, 1994). The lack ofventure capital for innovative high technology start-upsis a frequent complaint as further discussed in a latersection.

(ii) Government and its policies and regulations are afrequent source of barriers to innovation (Piatier, 1984;Pol et al., 1999). Many policies directly or indirectlyrelated to innovation are designed to correct marketfailure. Problems may arise, however, due to unin-tended consequences of such policies and side effectsof regulations. Standards imposed by government or bysupra-national organizations, such as the EuropeanUnion, may also act as obstacles to innovation.Bureaucratic procedures in getting licenses or grantsand in other contacts with governmental organizationsare also a frequent cause of barriers. Problems in policycommunication may induce discrimination againstsome firms, e.g. micro-firms and small firms, prevent-ing them from getting the support they are entitled to.

Laws and regulations may give rise to barriers due toeither their side effects or inadequacies in implementa-tion. Firms have to comply with regulations at thelocal, regional, national, and even supra-national level(for example European Union directives). Regulationsmay discourage innovative activities and hinder firmsfrom entering new markets, by increasing uncertaintyand risk. They may also prevent some firms fromundertaking promising projects, because they increasetheir time frame, cost and risk (Preissl, 1998). In otherinstances, they may impose unnecessary limitations onthe operations of the firm. It is important to note thatthe same regulations may be beneficial for innovationin some industrial sectors and detrimental in others.

Examples of legal constraints include labor andconsumer protection legislation, environmental regula-tion, and anti-trust legislation (Jorde & Teece, 1990).The legal frame for the protection of intellectualproperty has an even more direct impact. A weakintellectual property regime, for example, allows theeasy copying of innovations and acts as a disincentiveand inhibitor for firms to undertake costly innovationthat could easily, and at a fraction of cost, be exploitedby their competitors (Chesbrough, 1999). The taxsystem is a potential source of indirect barriers byreducing incentives to innovate. Trade barriers, forexample, the so-called non-tariff barriers, may preventforeign market entry and reduce the commercialsuccess of an innovative new product.

Regulations, standards, and rules mentioned aboveare examples of institutions. Institutions related toinnovation include the science and technology infra-structure and the physical infrastructure. Manyinstitutions are therefore under the direct or indirectcontrol of central or regional government. The term‘institutions’ is however used here in a very broadsense to include all political, social and cultural

Table 1. External and internal barriers to innovation.

External Internal

1. Market related 1. People related2. Government related 2. Structure related3. Other 3. Strategy related

The Barriers Approach to InnovationChapter 1

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institutions—formal and informal—and also all relatedrules and procedures. These institutions characterize asociety and cannot easily change without the coopera-tion of wider social forces, e.g. firms and theirassociations, labor and government (Sanz-Menendez,1995).

Lack of suitable institutions, inadequate perform-ance of existing ones, and what has been calledinstitutional inertia or rigidity, i.e. resistance of oldinstitutions to change, may lead to innovation barriers(Freeman, 1994). Institutional structures may haveadverse effects on transaction costs, making innovationmore costly or hardly affordable. Institutional factorsalso affect the extent of cooperation, trust and mutualconsideration of firms and the formation of alliancesand other forms of cooperation.

(iii) The ‘other’ category includes technical, societaland inter-organizational barriers. Technical barriersmay originate from predominant standards, e.g. intelecommunications, or arise due to changes in tech-nology (Freeman, 1994). Risk of technologyobsolescence, destruction of a firm’s competences withchange of technology, and dangers from picking thewrong technology, are major considerations in somefields of high technology (Starbuck, 1996). Othertechnical obstacles are due to the scale of capitalrequirements for entering a particular new technologyfield and scale of experience effects (technologicalentry barriers).

Societal factors may form important innovationbarriers (Shane, 1995). Norms and values of a societyand attitudes towards science, socio-economic changeand entrepreneurship determine the innovation climate(Piatier, 1984). The latter, if it is negative, has anadverse effect on innovation efforts and on thewillingness of Government to assist innovation.

External barriers may also arise at the inter-organizational level when firms have to cooperate at aregional, national or international level (Tidd et al.,1997). For example, barriers to innovation occur duringcooperation along the supply chain, when customersdiscourage product changes or access to distributionchannels is problematic for a new firm. Although thelatter example refers to vertical cooperation, there aresimilar problems in horizontal cooperation betweenfirms of the same sector when there is no tradition ofsuch cooperation or there is lack of trust.

Inter-firm networks are frequently seen as facili-tators of innovation by being sources of ideas,information and resources (Swan et al., 2003). Theycan, however, act as obstacles to innovative change dueto technical, knowledge, social and administrativedependencies. There are barriers to exit from a networkarising from investments made by the company itselfand by other network members (Hakansson, 1990).While inter-organizational barriers are treated here asexternal, there is also an internal dimension in the

sense that firms should have special competences inorder to develop, maintain and take advantage frominter-organizational relationships.

Internal BarriersInternal barriers relate to the characteristics oforganizational members, the characteristics of theorganization, and the management of innovation as achange process. They can be conveniently classifiedinto people related, and structure- and strategy-related.

(i) People related. They can be studied at the individ-ual and the group level and, if necessary, separately formanagers and employees. They are due to perceptions,including biases and lack of motivation, deficits inskills, but also to vested interests and personal goalsdiffering from organizational ones. For example,innovation may affect the status and privileges ofexperts by making their expertise obsolete. Suchexperts then resist innovation and change. To overcomethis natural resistance, so-called ‘innovation champi-ons’ are needed, and their absence may prove a majorbarrier to innovation (Gemuenden, 1988; Hauschildt,2003). An innovation champion is an individualrecognizing the potential in a new technology or amarket opportunity, adopting the relevant project ashis/her own, committing to it, generating support fromothers and advocating vigorously on behalf of theproject (Markham & Aiman-Smith, 2001). The role ofchampions is further discussed in a later section.

Management may be preoccupied with the currentoperations and have a conservative attitude, which maylead to perceiving innovation as being risky anddifficult. Lack of commitment of top management toinnovation, as indicated by not rewarding risk takingand lack of toleration of failure, is mentioned as amajor innovation barrier (Hendry, 1989). The decision-making process of managers, constrained by theirbounded rationality, and its organization regardingsearch procedures, information sources, and evaluationrules, is also a source of barriers (Schoemaker &Marais, 1996).

Witte (1973) classifies people-related barriers intotwo categories, i.e. those due to lack of will, and thosedue to lack of competence. Barriers of the first categoryrefer to the attraction of the status quo and fear of theunknown, but also fear of failure and being blamed forit (Bitzer, 1990). Factors causing will-related barriersinclude the effects of specific personality traits andfeelings of managers and employees—acting as indi-viduals or as members of teams. For example,perceived favoritism, jealousies and resentments havedetrimental effects on innovation (Webb, 1992).Causes of people-related barriers are considered inmore detail later.

Competence barriers are due to lack of creativity andspecific new knowledge required by the innovation(Tang & Yeo, 2003).

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Inhibiting factors or blocks to individual creativity,such as lack of training, autonomy, and extrinsicmotivation are closely related to innovation barriersand have been extensively studied, but their detailedexamination is beyond the scope of this chapter(Amabile, 1997). The lack of skills, as a competencebarrier, has several dimensions. For example, Yap &Souder (1994) refer to the lack of both breadth anddepth of personnel (i.e. number and variety of special-ists) as an innovation barrier. Similarly, Staudt (1994)refers to the lack of suitably qualified managerialpersonnel as a barrier to innovation, but also toincumbent managers having competences in fieldsbecoming obsolete, rather than competences in emerg-ing fields.

(ii) Structural. Structure affects the behavior of organ-izational members during the innovation process anddetermines the problem-solving capacity of the firm.Structural obstacles include inadequate communicationflows, inappropriate incentive systems, and obstructionproblems by some departments (Hauschildt, 2001).The latter is also referred to as lack of inter-functionalintegration (Hitt et al., 1993). Collaboration betweenmarketing and R&D for example is vital, especially forproduct innovation. Problems in this collaboration, dueto different values, motivations, and goals, have anadverse effect on innovation (Hendry, 1989).

Centralization of power in an organization affectsnegatively innovation in older firms (while beingpositively correlated with innovation in new ventures(Koberg et al., 1996)). Mechanistic structure (i.e. arigid hierarchical structure without many participationpossibilities for employees) in a turbulent environmenthas been mentioned as a barrier to innovation in earlystudies on innovation (Burns & Stalker, 1961). Schoe-maker & Marais (1996) refer to firm inertia andformalized procedures as obstacles to process innova-tions. Webb (1992) mentions the contradiction betweenformal organization and actual management practicesas a problem leading to defensiveness and distrust onthe part of employees, with detrimental effects oninnovation. Lack of time is a frequently mentionedinternal barrier (Hadjimanolis, 1999). While time canbe seen as a resource, it is also clearly related tostructural issues like organization of the work, delega-tion of tasks, and specialization.

Structural inertia may be accompanied by culturalinertia and internal politics games (Maute & Locander,1994; Starbuck, 1996). Culture refers to the sharednorms, values, and beliefs of the firm (Armenakis &Bedeian, 1999). Cultural barriers are then due to theexisting beliefs and values of the firm that are notsupportive of change. A culture of blame and fear ofresponsibility, for example, obstructs experimentation,change, and innovation. Cultural barriers are related tomotivation and reward and punishment systems, andare intertwined with the people-related barriers men-

tioned above. The work environment has a directimpact on the intrinsic motivation for creativity andinnovation, and an adverse environment may stifleinnovation efforts (Amabile, 1997).

Problems related to systems may also be includedhere. Inadequate search and information acquisitionsystems from external sources (Madanmohan, 2000)and problematic internal dissemination mechanismsmay hamper innovation (Sheen, 1992). Other examplesare out-of-date accountancy systems (Rush & Bessant,1992) and lack of planning systems.

(iii) Strategy related. Many internal barriers arerelated to strategy. Lee (2000), for example, mentionsthe failure of strategy in British firms to connect theintroduction of flexible manufacturing systems withthe long-term aims of the firm, e.g. its competitiveposition. Technical people may also be unaware ofstrategy and objectives, and cannot therefore persuadesenior managers of the benefits and necessity of newtechnology, while senior managers—being techno-logically ignorant—cannot see these benefitsthemselves. Other barriers may be goal-related in thesense that senior managers may fail to appreciate thenecessity for innovation or are too risk-averse toattempt to innovate. Markides (1998) mentions com-placency, satisfaction with the status quo, andreluctance to abandon a certain present (adequatelyprofitable) for an uncertain future, as potential innova-tion barriers. Fear of cannibalizing sales of existingproducts may be a more specific excuse for avoidinginnovation.

Strategy is today related to the development of corecapabilities and resources that are difficult for com-petitors to imitate (Peteraf, 1993). Some keycapabilities related to innovation are technologicalones, such as the capacity to produce ideas and developthem to products. Other capabilities are, marketing andservice skills and legal skills to protect the firm’sintellectual property. Also ability to network, formalliances and span inter-firm boundaries (Rosenkopf etal., 2001). The lack of the above capabilities, or theirinadequate level, may form major internal barriers toinnovation. Core capabilities may however turn to‘core rigidities’ with environmental, e.g. technologicalchange and develop into traps and barriers when theystop offering a competitive advantage (Leonard-Bar-ton, 1995). Rigidities are also related to the ‘sunk cost’fallacy and commitment to existing technologies(Schoemaker & Marais, 1996).

Resource-related barriers include lack of internalfunds (e.g. from cash flow), and lack of machinery,testing or other technical equipment (Bitzer, 1990).Important barriers may arise from the lack of an ownR&D department, a low percentage of organizationalresources dedicated to development work, and techni-cal problems due to inadequate experience orknowledge. Resource-related are also what Teece


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(1986) has called appropriability constraints, i.e. lackof complementary assets or capabilities to take fulladvantage of an innovation that a firm has developed oradopted. Slack resources are considered important forinnovation, but there is disagreement over whethertheir lack, forms a barrier to it. Nohria & Gulati (1996)claim that there is a U-shaped relation between thedegree of slack and innovation. Initially innovationincreases, with an increasing degree of slack, up to aninflection point; then negative effects set in andinnovation decreases.

Impact of Barriers on the Innovation Process

Innovation Process ModelsWe have briefly considered above the nature ofbarriers. This section describes their effect or impact onthe innovation process and also the frequency of theiroccurrence and their intensity. The relevant actor,whose action is inhibited by a barrier, could be anindividual, a group, a firm, etc. The emphasis here is onprivate firms as actors. Barriers may act on one or morestages of innovation, and their impact may be differentat their various points of action. Of particular interest isthe role of barriers during the initial stages of theinnovation process, since inability of the firm toovercome them leads to a passive attitude and avoid-ance of innovation. In order to facilitate the discussionof the impact of barriers, we have first to describebriefly the innovation process.

The traditional linear model of innovation conceivedinnovation as a linear sequence of events from researchto development, production and commercialization. Ithas since been recognized that the innovation processis a much more complex phenomenon with manyplayers and feedback and feed-forward loops betweenthe various stages. The simultaneous model (Kline &Rosenberg, 1986) corrected some of the deficiencies ofthe linear model by recognizing the existence of tightlinkages and feedback mechanisms between the stagesof the innovation process.

The current interaction models of innovation (forexample the system integration and networking model(SIN) of Rothwell (1992)) emphasize the internalinteraction among the various departments of the firmand the external interaction with suppliers, customers,technology providers, and governmental institutions—even competitors.

The interaction model is an outcome of a systemicapproach to innovation, which recognizes that firms donot innovate in isolation, but as a part of a muchbroader system. The innovation system can be con-ceived at a local or regional level, at a national level oreven at an international level. The regional dimensionof innovation will be considered in the next section,while the national innovation system (NIS) concept isdiscussed in more detail in the section entitled ‘Barriersin Special Cases’.

The interaction model gives an indication of thecomplexity of the innovation process, the many actorsinvolved, and the multiplicity of interaction. It implies,therefore, the fluidity of the process. Another dimen-sion of this fluidity is the fact that goals are not fixed atthe beginning of the innovation process. They changealong the decision process as new alternatives appearand interact with the problem-solving activities(Gemuenden, 1988). Barriers have, therefore, adynamic nature due to the characteristics of theinnovation process itself. They should not then beconsidered as pre-existing and given (i.e. as ante-cedents of innovation), but rather as fluid and evolving.This fact adds further difficulties to their study andevaluation.

While the identification of barriers, the estimation ofthe frequency of their appearance, and the ranking oftheir importance, is not without problems, the evalua-tion of their impact is much more difficult due to anumber of reasons presented below. By ‘impact’ wemean the exact final effect on innovation, i.e. its partialor complete inhibition. In other words the barrier maystop innovation completely, delay innovation orincrease its cost. Apart from these negative effects,positive effects may also arise, for example anincreased sensitivity and awareness of barriers and avaluable learning experience for future innovationefforts. Evaluation of the impact of barriers includesthe determination of the point or stage of theinnovation process at which they act and the mecha-nism of action.

The main difficulties of this evaluation are asfollows:

• barriers may have a dynamic nature, i.e. a systematicvariation according to the stage of innovation. Theirevolutionary character increases the difficulty ofassessing their exact impact;

• barriers may not act in isolation, but may interactmutually, reinforcing their action and leading to avicious circle. Mohnen & Rosa (2000) refer to thecomplementarity of barriers and suggest a systemicapproach for their elimination or reduction;

• barriers may not act directly on the firm, but mayhave their effect during one or more stages of theinnovation process through intermediates, such asbanks, customers or competitors (Piatier, 1984). Thisimplies that barriers tend to act on the interfaces ofthe firm with other actors within the innovationsystem.

Patterns of Barriers in Different Contexts

We have so far discussed barriers to innovation ingeneral. Most studies, however, challenge the univer-sality of barriers and tend to suggest that their nature,frequency and impact probably vary in accordancewith the context of innovation (Pol et al., 1999). Thereare therefore different patterns of constraints for

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different contexts. Some of these contexts include thetype of innovation, the type of innovator, the sizeof the firm, the sector, the location, and probablyeven the business cycle. They are briefly consideredbelow.

(i) Type of innovation. Incremental or radical innova-tion types probably involve different types of barriers.In other words, the degree of novelty of the innovationis related to the level of difficulty to innovate. Whatmatters, as an innovation barrier, is the perceiveddegree of novelty by the innovation actor (Tidd et al.,1997). The type (product, process, or social innovation)and characteristics of innovation and its complexitydetermine to some extent the difficulties that the firmfinds producing or adapting innovation to its needs.With increasing complexity of innovation, for example,problems of communication and process managementbecome more severe (Hauschildt, 2001).

(ii) Type of innovator. We can distinguish herebetween non-innovators, new venture innovators (start-up firms), first time innovators (but already establishedfirms) and frequent innovators, i.e. firms that areexperienced innovators and innovate on a continuousbasis. The type of innovator is therefore both related tothe firm’s life cycle, from start-up phase throughmaturity to decline, and the firm’s experience ininnovation. Barriers tend to be higher during thetransition stages from one phase to another. Mohnen &Roeller (2001) suggest that innovation intensity inestablished innovators and the probability of becomingan innovator are two different innovation processes thatare subject to different sets of constraints. There arealso differences between producers of innovation (thatis those developing their own innovations internally)and adopters of innovation developed elsewhere. In thecase of adoption, barriers to diffusion and technologytransfer have to be considered (Godkin, 1988), as wellas, the problem of internal resistance to a foreigninnovation, also known as NIH (Not Invented Here)syndrome (Katz & Allen, 1982).

(iii) Size of the firm. Barriers may vary by the size ofthe firm, i.e. for small and large firms (Mohnen &Rosa, 2000; Piatier, 1984). The size of the firmprobably determines not only the nature, but also theimportance of barriers, with small firms perceivingtheir impact as more severe. It is widely believed thatthe main innovation barriers in large firms are largelythe internal ones, while such firms have the resourcesand the know-how to overcome any existing externalbarriers (Vossen, 1998). Internal barriers arise fromtheir complexity and are due to lack of motivation,problems of communication and coordination andpossibly lack of incentives. According to Quinn (1985)the main bureaucratic barriers to innovation in largefirms include top management isolation from produc-

tion and markets, intolerance of entrepreneurialfanatics, short time horizons, the accounting practices,excessive rationalism and bureaucracy, and inap-propriate incentives. In the case of small firms externalbarriers are very important, while resource-related,internal ones may also be critical. Barriers in smallfirms, due to their importance, are further considered inthe section entitled ‘Barriers in Special Cases’.

(iv) Sector. Barriers probably vary by sector (Preissl,1998). Some barriers may be industry-specific in thesense that they are typical for the firms of one sector,but not for firms of other sectors. Similarly, theperceived importance of export barriers is reported tovary across industries due to industry-specific factors(Leonidou, 1995). The inter-sectoral differences inbarriers arise from business demographic differences,i.e. firm size distributions by sector, but also from thecontext for innovation and the level of innovation ineach sector. Innovation is expected to be much higherand continuous in high technology sectors with a highpercentage of R&D such as information technologyand biotechnology, against low technology sectors asglass or woodworking. Barriers may also be related tothe industry life cycle, since opportunities for innova-tion and resources may differ between these stages.While traditionally there are many studies on barriersin various sectors of the manufacturing industry,studies on innovation barriers in services have onlyrecently received attention (Mohnen & Rosa, 2000;Preissl, 1998).

(v) Business cycle. Barriers may also vary during thedifferent phases of the business cycle of the economy,i.e. recession and growth, due to the differentiatedavailability of resources and the investment climate.Their variation may also be attributed to the differentextent of government interference in the economy ineach of these phases.

(vi) Location. We refer here to the effects of thespecific location of the firm, any regional resourcedeficits and problems of the national innovation systemof the country. Country specific institutions, regula-tions, and other conditions create country specificbarriers. National cultural factors may also affectdifferentially the perception of barriers (Shane, 1995).The size of the country and its level of industrialdevelopment are major factors in innovation and theexistence of barriers. Country effects are furtherelaborated later. The regional dimension of innovationhas received considerable attention in recent years(Love & Roper, 2001). A number of tangible factors,such as the local industrial structure, local institutionsand regional policies and regulations may affectinnovation. There are also several intangible factors,such as culture, social capital, and the extent of localnetworking that may be relevant.


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Theoretical Explanations of Barriers

Barriers have been studied by various disciplines suchas economics, sociology, psychology and management.We discuss briefly each of these approaches.

Economists tend to concentrate on external barriers,i.e. those resulting from market failures, governmentalpolicy implementation, institutional inadequacies orrigidities and supply/demand deficiencies. They con-centrate on externalities, imperfect and asymmetricinformation available to the various actors involved ininnovation and imitation, as well as their effect asincentives and disincentives for innovation. Econo-mists also study the effect of various factors on costs,including opportunity costs, and how they impact onthe perceptions of risk and uncertainty. The transactioncosts perspective (Williamson, 1985) is a usefulapproach in such studies. When they study internalbarriers, economists focus on incentives and resources.The aim of the economic approach is frequently toconnect the problems with national policy aspects andsuggest policy changes.

The resource-based view (Conner & Prahalad, 1996;Peteraf, 1993) is an interesting perspective in theeconomics and strategic management literatures, whichcould illuminate some aspects of the appearance ofbarriers. This view concentrates on the uniqueresources and capabilities owned by the firm andcontributing to the development of its competitiveadvantage. The acquisition of resources and capabil-ities is a long-term, evolutionary, and cumulativeprocess. Innovation depends on the availability oftechnological resources and knowledge and demands anumber of capabilities. The lack of these resources andcapabilities can be manifested as internal barriers toinnovation. Obtaining these resources from the envi-ronment is costly and difficult. Cohen & Levinthal(1990) have suggested that the firm must have an‘absorptive capacity’ in order to be able to obtain, toadapt, and use externally available technological infor-mation and knowledge.

Even economists recognize the importance of per-ceptions. Competitive intensity, for example, is not justa matter of market concentration, but also an issue ofperception by the firm itself. There is now a branch ofevolutionary economics studying aspects of percep-tions, mental models, and learning processes, theirimpact on innovation, and other economic issues(Howells, 1995). The study of perceptions is howevermainly the realm of social psychologists. The lattergive emphasis to the internal barriers and study the roleof attitudes of managers and employees at an individ-ual and a group level. Psychologists also examineperceptions of mainly internal, but also of externalbarriers. We shall briefly consider various aspects ofthe social psychology approach to the understanding ofbarriers.

Innovation implies change; it usually represents amajor change for the organization involved. The studyof the more general phenomenon of organizationalchange can then provide a useful framework for thestudy of barriers and their effects (Armenakis &Bedeian, 1999). Barriers to innovation can then beconsidered as a subset of barriers to change and haveanalogies and similarities to barriers identified in otherorganizational changes. As examples of such changeswe can cite barriers to growth of small firms (Chell,2001; Storey, 1994), barriers to export and inter-nationalization (Leonidou, 1995), barriers to learning(Kessler et al., 2000; Steiner, 1998), as well as barriersto structural reorganization (Armenakis & Bedeian,1999).

In order to understand barriers to change, we have tostudy the perceptions, assumptions, interpretations andcognitions of managers and employees. Particularemphasis is given to managers (especially in the caseof small firms), since managers determine organiza-tional priorities and make resource allocation decisions(Storey, 2000). Their perceptions are therefore vital forchange and innovation. Organizational members inter-pret environmental signals and the external reality in asense-making process (Weick, 1995). In this processthey form mental representations and models anddevelop their cognitions. Carl Weick (1979) hasintroduced the concept of the enacted environment,which is shaped by managerial interpretation andstrategic choice in contrast to the ‘objective’ reality ofan external environment. Mental models affect thereadiness for change (Swan, 1995). For example,deeply held assumptions about the market may affectthe process of identifying or creating opportunities andmay therefore act as barriers to adoption of new waysof thinking in technological innovation. Perceptionsfilter information and therefore affect the assessment ofbenefits and costs of innovation to be developedinternally or adopted from external sources (Tidd et al.,1997). Different perceptions of risk within the organi-zation may prevent managers from reaching an internalconsensus on the need to innovate.

A more specific model that could serve as a basis forthe understanding of barriers, is the cognitive infra-structure model of the intent to innovate (Krueger,1997), which uses the Ajzen-Fishbein framework basedon their theory of planned behavior. The modelmaintains that attitudes, beliefs, and social norms affectintentions, which subsequently influence the readinessfor change and innovation. The intentions themselvesare also affected by perceived competencies and otherfactors. The model is useful because it incorporates themain influences on the intention to innovate. It could becriticized as giving a partial picture of the phenomenonof barriers since many barriers operate at its secondstage. These barriers appear between the formedintentions and the realization of innovation, where themodel does not give much information on factors—

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other than good intentions—that affect the particularinnovation.

Innovation, both as development of new products orprocesses within the firm and as technology adoption,can be seen as a learning process (Dodgson, 1993). Atthe same time perceptions and mental models ofmanagers and employees are formulated and reformu-lated during the processes of change (and innovation issuch a process of change) by learning and interactionbetween members of an organization. Organizationallearning links, then, cognitions and innovation action.Thus a powerful perspective, which can be used in thecomprehension of innovation barriers, is that oflearning. While learning at an individual level has longbeen recognized as an essential process for theadaptation of human beings to the changing environ-mental conditions, more recently the organizationallearning process has received attention by socialpsychologists, organizational and management scien-tists (Steiner, 1998).

Adaptive learning processes in organizations fre-quently have a collaborative and interpersonal nature,which leads naturally to the consideration of theseinterpersonal relations in a network perspective (Tiddet al., 1997). We consider, then, the role of suchlearning networks in innovation. Learning can beclassified as internal, when the firm creates newknowledge leading to innovation internally, or asexternal learning when knowledge is obtained fromexternal sources. Internal and external networks areinvolved, respectively, in the learning process. Differ-ent sets of barriers operate in these two cases (Kessleret al., 2000). For organizational learning to take place,information and knowledge have to be transferredthroughout the organization and shared across manydifferent groups. Then information should be stored,retrieved, processed and finally utilized. Impedimentsto internal learning may originate from the culture ofthe organization if values such as risk-taking, commu-nication openness and appreciation of teamwork areabsent. Even if formally espoused, but not properlyrewarded, they may act as barriers.

Barriers to external learning, i.e. obstacles to thetransfer of knowledge from external sources such asuniversities, research centers, or other firms, may comefrom a lack of boundary-spanning individuals or frompolitical resistance to externally generated ideas. Thisresistance is known as the NIH (Not Invented Here)syndrome (Katz & Allen, 1982). It happens especiallyif the ideas appear revolutionary and threatening to thecurrent status of managers or other employees, andthere is no innovation champion or promotor (Hau-schildt, 2001). The innovation champion may be thesame person as the boundary-spanner or anotherindividual. External learning faces more barriers thaninternal learning. Learning has, however, sometimes tobe preceded by ‘unlearning’ of ineffective technologiesand practices (Starbuck, 1996).

Sociologists and organizational scientists concen-trate on political processes, power structures and statuswithin firms, as well as their role in change processesas, for example, innovation (Frost & Egri, 1991).Innovation may alter the status quo and balance ofpower within the firm, leading to intense politicalactivity of the affected individuals or groups (Gemuen-den, 1988). Political games and use of power to accessresources and to protect vested interests or improvecareer prospects may present powerful barriers toinnovation (Burns & Stalker, 1961). Collaborationbetween rival interest groups is frequently problematic(Hendry, 1989). The concept of organizational inertia isuseful in visualizing the role of barriers. Organizationstend to resist new ideas, adhering to old routines andmaintaining hierarchies and power structures, andinnovate only when a particular force pushes them toovercome this inertia (Aldrich & Auster, 1986). Suchforces, acting as catalysts, include innovation champi-ons and environmental threats.

Management theorists try to integrate the conceptsof the various disciplines mentioned above. Theirmodels for the explanation of barriers include manypersonal (e.g. attitudes and leadership), structural, andsystems variables. They recognize the interaction ofthese variables and their dynamic nature (Tidd et al.,1997). Management scientists suggest that barriers arefrequently due to the failure of the firm strategists toevaluate the competitive and long-term implications ofinvesting (or not investing) in new technology anddeveloping new products in time. The risk of notinnovating is not immediately visible. Managementwriters also emphasize the bounded rationality limita-tion in strategic decisions (i.e. that they are notnecessarily rational or top-down, but a result ofpolitical and cognitively-biased processes (Schoe-maker & Marais, 1996)).

Pol et al. (1999) view barriers as a component of theinnovation climate of a country in line with a systemicapproach to innovation. Other components or parts ofthe innovation climate include the national innovationsystem, incentives to innovation, and internationallinkages. This is an interesting conceptual frameworkfor external barriers, but it could be criticized in thatthe innovation climate itself, incentives and barriers toinnovation, can also be seen as components of thenational innovation system. An extension of this modelwould consider the internal innovation climate withinthe firm and the interaction of internal and external‘climates’. The concept of internal innovation climateemphasizes the interplay of structural, cultural andpolitical factors.

Barriers in Special Cases

Disadvantages Facing Small Firms in InnovationWhile there may be barriers to innovation in largefirms, due to the complexity of their organization as


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already mentioned, it is widely recognized that smallfirms, because of their size and limited resources,face particular challenges and barriers to innovation(Vossen, 1998). We follow the definition of theEuropean Union for small firms, i.e. those with up to50 employees. Medium-size firms, i.e. those with up to250 employees, frequently face similar problems,perhaps to a lesser degree, and are often included inthis discussion.

The liability of smallness is a well-establishedconcept in the literature (Aldrich & Auster, 1986).Small firms face resource gaps in terms of time, staff,and money (Garsombke & Garsombke, 1989) and tendto depend on the external infrastructure for techno-logical and other services. They have, however, a weakability to interface with the infrastructure (for exampleuniversities and other technological centers), even ifthis is available for their support (Major & Cordey-Hayes, 2003). This weakness is due to lack of time, andinadequate managerial resources, knowledge andexperience. Small firms lack economies of scale andscope in production, and R&D, and have a low abilityto influence evolving technical standards (Yap &Souder, 1994).

Small firms are not usually able to match the wagerates, career opportunities and job security of largerfirms and cannot easily recruit skilled labor andmanagerial talent. These recruitment barriers mayprove major innovation barriers, although they are notinsurmountable. Sometimes there are advantages inworking in small firms, such as recognition and a betterwork environment. Small firms may also face abureaucratic burden, i.e. a disproportionate cost ofdealing with government agencies (Levy, 1993). Bar-riers may also arise in intellectual property protection,i.e. in searching for existing patents, filing for patentsor defending them in case of infringement, due to thehigh cost and lack of suitable personnel.

Small firms depend on the capacity of their owner toreceive/interpret signals from the market environmentand identify opportunities for innovation. In contrast,large firms have a formal mechanism for such activ-ities, including teams of scientists and managers. Theinnovation strategies of small firms focus on flexibilityand exploitation of market niches (Keogh & Evans,1999; Vossen, 1998). Small firms have some advan-tages in that they have simple structures, directface-to-face communication, and a friendly internalclimate, which tend to eliminate many internal barriers(Rothwell, 1984).

We briefly present here the results of empiricalresearch on innovation barriers in small firms. Accord-ing to Piatier (1984) the three major barriers toinnovation in small firms are: ‘education and training’,‘finance’, and ‘product standards’. The top five externalbarriers in another empirical study (Hadjimanolis,1999) include ‘innovation too easy to copy’, ‘govern-ment bureaucracy’, ‘lack of governmental assistance’,

‘shortage of skilled labor’, and ‘bank policies oncredit’. In the same study, the importance of barriers asperceived by the owners/managers of firms was notfound statistically correlated to innovativeness. Onepossible explanation offered was that innovative smallfirms find ways to overcome barriers, while lessinnovative firms are not adequately aware of them. Thetop ranking barriers according to a survey of Gar-sombke & Garsombke (1989) include lack of capital,staff, time, and knowledge of available technology. Arecent ADAPT project study (Schemman, 2000) foundfinance and lack of skilled workers to be major barriersin small European firms. Similarly Keogh & Evans(1999) have found ‘lack of cash and finance’ as the topbarrier. A thorough review of the empirical studies isbeyond the scope of this chapter. There are, however,remarkable similarities in the nature of barriers in theempirical studies, although importance ranks mayvary.

Small firms form an extremely heterogeneous group,and some categorization regarding their innovationfeatures would be useful. According to Tidd et al.(1997) they can be distinguished into supplier-dominated, specialized supplier firms, and new tech-nology-based firms (NTBFs). Supplier-dominatedfirms only need competencies to adopt and assimilatetechnology developed by others, usually their suppli-ers. Specialized supplier firms have little R&D, butsignificant design and production skills. New technol-ogy-based firms are start-up firms in electronics,software or biotechnology. The latter deserve somefurther discussion in terms of innovation barriers.

Start-up firms attempting to innovate, face more—and to some extent different—barriers than establishedfirms. Due to complexity and riskiness, they haveproblems in obtaining finance in the first stages of theirdevelopment, especially in countries where venturecapital finance is underdeveloped (Storey, 1994). Newinnovative firms entering an industry may face sig-nificant entry barriers, which can be overcome if theirtechnology is clearly superior to that of the incumbentfirms (Love & Roper, 2001).

The Case of Small and Less Developed Countries

The size of a country has a direct impact on the localsupply and demand of technology and affects in otherways the national innovation system (NIS). A nationalinnovation system is defined as:

“The network of agents and a set of policies andinstitutions that affect the introduction of technologythat is new to the economy” (Dahlman & Frischtak,1993, p. 414).

Small national innovation systems are usually also‘weak’ systems. The smallness of the local market andin the case of small peripheral countries, isolationand distance from major foreign markets limit the

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opportunities for technological innovation (Hadjima-nolis & Dickson, 2001).

Nations differ in their institutional environments(Chesbrough, 1999). This is especially true when smallcountries are compared with large ones. The nationalinstitutional setting in small countries is frequently asource of problems. Vital institutions may be totallylacking, for example institutions providing techno-logical services and research results, or their number islimited, such as business incubators and technologyparks. Institutions include not only formal organiza-tions, but also informal rules and procedures.Institutional rigidities and resistance to change isanother problem (Souitaris, 2003). In addition, nationalinstitutions constrain the state capacity to define andimplement an innovation policy and shape its outcome(Sanz-Menendez, 1995).

A usual problem faced by small national innovationsystems is difficulties in flows within the system—forexample human flows of researchers between uni-versities and private firms—or financial flows towardsinnovating firms. Linkages between institutions andfirms, necessary for such flows, may be limited or non-existent (Argenti et al., 1990). All the above factorsraise external barriers to innovation and require extraefforts from the firms to innovate. The majority of thesefirms are small or very small and have, in addition, theliability of smallness as mentioned above. Opportuni-ties for innovation in small firms are much moreinfluenced by the national innovation system than thosefor large firms, since they depend more on others forinnovation, e.g. their suppliers (Tidd et al., 1997). Inthe case of small countries, barriers to internationaltechnology transfer are also quite significant, sincesmall countries depend on such transfer for a large partof their technological needs.

Less developed countries face similar problems tothe smaller industrialized countries regarding theirnational innovation systems, but these problems areusually much more acute in their case. Countries intransition such as those in Eastern Europe are under-going wide-ranging institutional and societal change,and face unique innovation barriers. The discussion ofsuch barriers (Staudt, 1994) is beyond the scope of thischapter.

Methodological Difficulties of Empirical Studies onInnovation BarriersSome indicative empirical studies on innovation bar-riers have already been summarized in the previoussection. The present section focuses on the methodo-logical difficulties in researching barriers.

Researching barriers is a difficult undertaking. Mostempirical studies are based on surveys using lists ofbarriers derived from the literature. They investigatethe opinions and beliefs of managers (very rarely thoseof employees) and their perceptions of barriers. Thefact that the studies are not based on a sound theoretical

framework leads to difficulties of interpretation of theresults and to heterogeneity. It also creates problems ofcomparison with previous research and of general-ization beyond the current research context.

Research based on case studies permits a deeperunderstanding of the particular context, although theabove-mentioned problems are not avoided. There areadditional difficulties in studies on barriers, whethersurveys or case studies, which are summarized in Table2 and then briefly analyzed.

Enterprises that failed to innovate and subsequentlydisappeared are not counted in the research, althoughbarriers are most operative in such cases (Piatier,1984). In addition, managers tend to attribute majorimportance to external barriers. This shifts attentionaway from internal barriers and shifts responsibilityaway from managers to some external and uncon-trollable sources. This ex post rationalization bymanagers pointing to external constraints, when in factinternal constraints were the problem, is frequentlymentioned in research (Barkham et al., 1996). Anothermajor difficulty is that the sensitivity of the innovator,i.e. the level of awareness of barriers, and the problemsand obstacles that are actually encountered are inextri-cably linked.

In the analysis of results, researchers frequently usefactor and cluster analysis in order to bundle barrierstogether, in order to investigate underlying factors orclassify firms into groups according to the barriers theyface (Hadjimanolis, 1999). These techniques illuminateinterrelations of barriers, but do not go far enoughtoward an explanation of why barriers occur and whichfactors affect their appearance. The dynamic nature ofbarriers is also a major problem. Barriers may changewhen environmental forces change; even the percep-tion of barriers by firms is affected by environmentalchange.

Overcoming BarriersWhile concentration on opportunity thinking, in con-trast to obstacle thinking, is a preferable thoughtpattern (Neck et al., 1999), the realistic awareness ofbarriers is justifiably necessary. Identification of bar-riers is then essential in order to deal with them andattempt to eliminate them, thus increasing the innova-tion performance of the firm. The usual types of firms’response to barriers are either the avoidance of dealingwith them or the ad hoc approach. A systematicapproach to overcoming barriers is more rarelyobserved. Piatier (1984) proposes a perception cycle

Table 2. Main difficulties in studies on barriers.

1. Failed and disappeared innovators not counted2. Overemphasis on external barriers by managers3. Sensitivity of innovator and obstacles interlinked4. Inadequate methodology and analytical techniques5. Dynamic nature of barriers


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for barriers. Initially the problem is not appreciated; itsexistence is even denied or underestimated. At a secondstep the problem is exaggerated (seen as bigger than itactually is). Then it is properly assessed, and in a finalstep it is effectively surmounted.

Research into barriers was referred to earlier as anexternal academic or consultancy approach, having asan aim to increase knowledge in this field and helpfirms and public policy makers in the identification andprioritization of barriers, as well as, the evaluation oftheir adverse effects on innovation. We can alsovisualize an internal process of continuously identify-ing and overcoming barriers to innovation. Staudt(1994) refers to the useful analogy of developing aradar system to navigate through a sea with icebergs.

This internal process should start with a realizationof the importance of identifying barriers and asystematic look into their sources, both externally andwithin the firm. The organization has usually controlover the internal barriers, but their elimination cannotbe successful without a detailed plan of action. Whileraising awareness and attention is a first step, theirclassification by importance and devising ways toovercome barriers are equally important steps in theprocess of eliminating barriers. It is particularlyimportant to eliminate or minimize barriers at the earlystages of innovation. Methods to reduce barriersprobably have to be specific to each stage of theinnovation process (Bitzer, 1990). Mohnen & Roeller(2001)—in a study, which used European data onobstacles to innovation—have found that:

The lack of internal human capital (skilled person-nel) is complementary with all the other obstacles inalmost all industries (Mohnen & Roeller, 2001,p. 15).

Measures against barriers at both the firm and theinnovation policy level should then concentrate onimproving human capital as a first priority. Measuresfor such improvement include training, but also asuitable motivation system with rewards (monetary,promotion, etc) and sanctions.

The role of champions or promoters in overcomingresistance to innovation as emphasized in the literature(Hauschildt, 2003) has been mentioned above. Theyare frequently necessary in addition to any otherstructural and system-related arrangements (King,1990). To summarize, we would say that barriers needa specific strategy and resources, within the frame of asystematic course of action, to be overcome success-fully. The overall aim should be to create a favourableinnovation climate within the firm, which encouragesidea generation, circulation, experimentation andrisk-taking. To this effect, suitable organizationalmechanisms and systems are needed, e.g. for develop-ing new ideas and rewarding creative work (Amabile,1997). The internal organizational process of over-coming barriers can be helped by benchmarking, i.e.

observing innovation management best practice insuccessful innovating firms. What such firms do toavoid and overcome the inevitable barriers, provides auseful yardstick for action.

Although most discussion in the existing literature ison ways to overcome barriers, a proactive approach toprevent barriers before they occur is apparently a morerational strategy. This implies proactive management ofthe innovation change process and periodic measure-ment of the innovation potential of the organization, inorder to determine gaps and take the necessary action.

While firms should do their part, regional authoritiesand national governments must also act against exter-nal barriers by trying to control and remove them. Thisis usually done in the context of a national innovationpolicy that addresses barriers according to theirimportance. It is essential to emphasize that the natureand importance of barriers should be established for theparticular context through research. Measures shouldthen be based on the results of such research, ratherthan subjective perceptions of policy makers of whatcould possibly constitute barriers. Different sets ofmeasures are probably needed to increase the innova-tion intensity in established innovators on the one hand,and to stimulate entry to innovation among non-innovators on the other (Mohnen & Roeller, 2001). Animportant consideration is that barriers frequently ariseas side effects of support policies with good intentions.The ‘ex ante’ and ‘ex post’ evaluation (i.e. before andafter implementation) of innovation support policiesshould, then, consider the occurrence of side effectsand a possible modification of the original policy toremove them.

ConclusionsThe barriers approach to innovation focuses on themain problems that may occur during the complex anddelicate process of innovation. The innovation processis fraught with difficulties, as it demands the closecooperation—over an extended period of time—ofmany people. The actions of all these people have to becoordinated, and their talent combined with internaland external resources for a successful outcome. Thedifficulties of motivation, coordination, development ofcapabilities, and acquisition of resources emerge asbarriers to innovation. The available theories provideonly a partial comprehension of the underlying mecha-nisms that generate these barriers. Based on thatknowledge, a process of identifying and eventuallyovercoming these barriers can be set up within the firm.At the same time regional authorities and nationalgovernments have a significant role to play in theelimination of external barriers.

Much research is still needed to illuminate the jointaction of barriers as a system, and their dynamicnature. The usual top-down approach in research that isfocused on top managers and their views on barriersshould be complemented with more ‘bottom-up’

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research, i.e. with investigation of the views ofemployees. There is also need for longitudinal researchto follow the development and interaction of barriersduring the innovation process and to find causal linksbetween innovation barriers and innovation perform-ance.

More comparative research (inter-country and inter-sectoral) is required to illustrate the patterns of barriersin different contexts, their similarities and differencesand lessons that can be drawn from them. SimilarlyDrazin & Schoonhoven (1996) call for more compar-ative studies of alternative forms of organizing forinnovation on a global scale. There is a particular needto focus on studying barriers in the case of non-innovators, i.e. use the latter not as a reference groupduring studies of barriers for innovators, but as themain group for study. Such an approach was alreadyfollowed for non-exporters in the case of exportbarriers (Leonidou, 1995).

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Knowledge Management Processes and WorkGroup Innovation

James L. Farr1, Hock-Peng Sin1 and Paul E. Tesluk2

1 Department of Psychology, Pennsylvania State University, USA2 Department of Management & Organization, University of Maryland, USA

Abstract: Following a selective review of theoretical models and empirical research on workgroup effectiveness and innovation, we present a dynamic model of work group innovation. Ourmodel integrates recent advances in taxonomies of work group processes and stages of theinnovation process with a focus on the temporal nature of innovation. We also provide adiscussion of the specific inputs, group processes, emergent states, and outcomes that appear tobe most relevant for each of the various stages of work group innovation.

Keywords: Work group innovation; Group processes; I-P-O models.

IntroductionOur focus is to bring together three streams of theoryand research that have separately addressed factors thatare thought to influence organizational effectiveness:group-based work, knowledge management, and inno-vation. Although each of these domains has receivedconsiderable attention from organizational scholars inthe past decade, little empirical research or conceptualdevelopment has occurred that might integrate them.We argue here that knowledge management processesrelated to how work groups seek, share, store, andretrieve information relevant to their performance (bothtaskwork and teamwork) are important factors asso-ciated with group effectiveness, including groupinnovation. We present an original theoretical frame-work based on several existing Input-Process-Outcome(I-P-O) models of group effectiveness that provides ourinitial understanding of how work groups’ knowledgemanagement processes influence their capacity forgenerating and implementing innovative solutions toproblems. Our focus is primarily on the group level ofanalysis, but include relevant factors at the individual(e.g. knowledge and skill) that influence group-levelknowledge management processes and innovation inimportant ways.

We present brief reviews of the extant literature onwork group effectiveness, work group innovation, andknowledge management, focusing on recent theoreticalframeworks in each domain. These reviews are fol-lowed by our model.

Work Group EffectivenessWork group effectiveness is most often studied througha lens that uses an (I-P-O) perspective (Guzzo & Shea,1992; McGrath, 1991). Because it is the dominantperspective for studying work groups, the I-P-Oframework can be used to examine differentapproaches to innovation in work groups from acommon vantage point. One of the advantages of the I-P-O paradigm is that it is inherently temporal (Marks,Mathieu & Zaccaro, 2001) and, because innovationinvolves consideration of criteria that can be viewedfrom both short-term (e.g. idea generation) and long-term (e.g. implementation, learning) perspectives, theI-P-O framework is useful for incorporating this time-based perspective.

Following the I-P-O lens, inputs refer to factorssuch as group composition, design, leadership, andorganizational context conditions (rewards, training,information systems) that influence the processes bywhich group members engage with each other andtheir environment as they work toward their object-ives. Outcomes are often considered to be in threeforms:

(1) the productive output of the team as defined bythose who evaluate it;

(2) the satisfaction of team members; and(3) the capabilities of the members of the team and

their willingness to continue working together overtime (Hackman, 1987).

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Processes have been defined in a number of differentways but typically are considered to be interactionsamong members of a team and/or with other groups orindividuals outside the team that serve to transforminputs (e.g. members’ skills) and resources (e.g.materials, information) into meaningful outcomes(Cohen & Bailey, 1997; Gladstein, 1984; Marks et al.,2001). They are, therefore, typically considered asmediating the relationship between input factors andgroup outcomes (McGrath, 1991), and in most modelsof team effectiveness, processes are depicted as servinga central role (e.g. Gist, Locke & Taylor, 1987;Gladstein, 1984; Guzzo & Shea, 1992; Hackman,1987). Indeed, an accumulating amount of research indifferent team settings has shown that processes suchas coordination and communication facilitate teamperformance in terms of outcomes such as productivityand manager ratings of group effectiveness (e.g.Campion, Medsker & Higgs, 1993; Hackman &Morris, 1975; Hyatt & Ruddy, 1996). Yet, while thereis clear evidence of their importance to team effective-ness, group process remains very much a ‘black box’when it comes to looking past short-term or immediateoutcomes to understanding innovation in the teamcontext. One of our objectives is to suggest newdirections for research to better understand how teamprocesses impact innovation in team settings.

Group processes from a temporal perspective. Aclear reality and challenge for many teams is how tomanage multiple tasks and objectives simultaneouslyand adjust to shifting priorities. To understand the roleof how processes enable teams to do this requiresincorporating a temporal perspective. While demon-strating the importance of team processes, existingresearch tells us little about how teams balance theinterests of competing outcomes from internal/externaland short-term/long-term perspectives. Because thisrequires understanding how teams manage multiplegoals and sets of activities over time (McGrath, 1991),recent theoretical work taking a temporal perspective toteam processes is particular promising for under-standing the functioning of healthy teams.

One perspective is offered by Kozlowski, Gully,Nason & Smith (2001), who identify the critical phasesin team development, how they build upon andtransition from one to another, and the primaryprocesses that take place at individual, dyadic, andteam levels as teams progress in their development.According to their framework, after their initialformation, teams focus on establishing an interpersonalfoundation and shared understanding of the team’spurpose and goals through socialization and orientationprocesses. Then, attention shifts to members’ develop-ment of task competencies and self-regulation throughindividual skill acquisition experiences. This, in turn,enables team members to immediately develop anunderstanding of their own and their teammates’ rolesand responsibilities. Finally, once team members have

a good understanding of each other, their own individ-ual task requirements, and their teammates’ roles andresponsibilities, the team then focuses on how they canbest manage interdependencies both in routine andnovel situations by attending to the network ofrelationships that connect team members to eachother.

What the Kozlowski et al. (2001) perspective makesclear is that adaptable teams are those that are able tosuccessfully manage the different challenges presentedin each phase of their development. Their model alsosuggests that, because the phases are progressive andyield enable team capabilities that build on each otherand are necessary for meeting the requirements for thenext set of developmental challenges, teams that fail toeffectively engage in critical sets of processes at earlierstages of development and achieve requisite cognitive,affective, and behavioral outcomes will be at a distinctdisadvantage at later developmental stages. Forinstance, even mature teams with a substantial sharedhistory will be limited in their adaptability and learningcapabilities if team members do not develop a highlevel of familiarity with their team members and astrong team orientation through team socializationexperiences.

Marks and colleagues (2001), who also use atemporal approach to describe team processes, offeranother complementary theoretical perspective.Although they do not take a developmental view ofhow teams build progressively complex capabilities perse, they do focus on team performance as a recurringset of I-P-O action and transition episodes whereoutcomes from initial episodes serve as inputs for thenext cycle. Thus, consistent with Kozlowski et al.(2001), team outcomes at earlier stages influence teamfunctioning in subsequent phases. Furthermore, Markset al. (2001) present a taxonomy that organizesdifferent forms of team processes depending on thephase of task accomplishment. Sometimes groups areactively engaged in working toward goal accomplish-ment and so action processes are dominant (e.g.coordination, communication, team and systems mon-itoring). At other times, groups are either planning forupcoming activities or reflecting on past performancesand so members are involved with transition processes(e.g. planning, performance analysis, goal specifica-tion). Finally, other interactions that involveinterpersonal processes, such as managing conflict,affect, and teammates’ motivation, occur during bothtransition and action phases of teamwork. Together,these different types of processes capture unique formsof member interaction, but merge together as asequenced series of team member activities andinteractions as a team goes about performing its work.

This distinction between different types of processesbased on when they occur during a team’s set ofperformance episodes is helpful both in terms of theoryand practice. For instance, interactions supporting a

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climate that encourages openly discussing problems,mistakes, and errors—a necessary condition for inno-vation through learning to occur in teams (Edmondson,1996)—depends on interpersonal processes such asaffect and conflict management in order to help raisealternative perspectives and dissenting viewpoints andkeep disagreements focused on the task rather thanbecoming personally directed at team members (Jehn,1995). These types of interpersonal processes arenecessary both when a team is actively working towardtask accomplishment as well as during transitionperiods such as, during routine pre-/post-shift meet-ings. Effective interpersonal processes are importantnot only for contributing to a team’s ability torecognize and interpret errors and utilize its experi-ences to derive learnings that enhance capabilities tomeet future demands, but also for maintaining thelong-term viability of the team by supporting cohesionand members’ commitment to the team by con-structively managing disagreements, conflict andcontributing to individual team members’ skill devel-opment and team longevity.

In contrast, if we are interested in studying howteams prioritize being innovative, attention should befocused on transition processes such as whether, andhow, teams specifically identify, discuss, and empha-size innovation as an explicit goal during transitionalphases in a team’s life cycle. From an interventionstandpoint, this might mean training teams on how tomore effectively utilize opportunities such as pre-shiftmeetings or ‘down-time’ periods to work on prioritiz-ing process improvement goals and reviewing theirperformance and conditions influencing the develop-ment of new ideas. To help teams more effectivelyweight potentially competing goals such as achievinghigh levels of productivity and generating novel ideas,interventions could also be designed to help teamsexplicitly identify, prioritize, and try to balance differ-ent, and perhaps competing, team objectives.

Work Group Innovation: I-P-O-Based ModelsWhile most researchers have adopted the I-P-O modelof work group effectiveness, it has been noted that theconceptual development of the outcome domain hasbeen the least developed (Brodbeck, 1996). Moststudies have relied on the group’s productive output(i.e. task performance) as the default criterion measurefor its effectiveness. Another common criterion meas-ure for group effectiveness has been team viability, orthe extent to which members want to continue theirteam or group involvement. In one sense these twooutput variables correspond with Hackman’s (1987)suggestion that a comprehensive assessment of successin ongoing groups must capture current effectiveness(i.e. task performance) and future effectiveness (i.e.team viability). However, Brodbeck (1996) pointed outthat two other important effectiveness dimensions for

work group research are individual well-being andgroup innovation.

An emphasis on work group innovation is consistentwith contemporary writings that note, organizationsmust continually innovate in order to increase or evenmaintain competitiveness (see e.g. Banbury & Mitch-ell, 1995; Hamel & Prahalad, 1994; Wolfe, 1994).Innovation can occur at the organizational level, thework group level, and the individual level, butthe group level has been less researched than eitherof the other two levels (Anderson & King, 1993),despite the increasing use of work groups as the basisfor accomplishing work tasks in many organizations.Recently, however, West et al. (1998) have proposed agroup-level model of innovation.

West, Borrill & Unsworth (1998) Model of WorkGroup EffectivenessDrawing on Brodbeck’s (1996) work, West et al.(1998) included team innovation as one of the outcomevariables in their review and synthesis of the researchrelated to IPO models of work group effectiveness.They suggested three possible input variables thatmight influence team innovation. First, heterogeneityin group composition is considered important as it hasbeen found to be related to group innovation (e.g.McGrath, 1984; Jackson, 1996). For instance, althoughnot directly examining this issue, Ancona & Caldwell(1992) found that when a new member from a certainfunctional area joined an existing team, communica-tion increased dramatically in that functional area.This, in turn, might favor innovation through theintroduction of additional and different ideas andmodels (Agrell & Gustafson, 1996). More directevidence has come from Wiersema & Bantel (1992),who studied a sample of top management teams in theFortune 500 companies and found that the topmanagers’ cognitive perspectives, as reflected in theteam’s demographic characteristics, were linked to theteam’s strategic management initiatives. Specifically,teams with higher educational specialization heteroge-neity were more likely to undergo changes in corporatestrategy. Bantel & Jackson (1989) reported that moreinnovative banks were managed by more educatedteams who were heterogeneous in terms of theirfunctional areas of expertise. Similarly, a more recentstudy (Drach-Zahavy & Somech, 2001) has found thatteam heterogeneity, as defined by differences inorganizational roles, was positively related to teaminnovation. Therefore, heterogeneity might affect teaminnovation because team members possess differentskills and expertise and, hence, have broader informa-tional resources and knowledge.

Second, West et al. (1998) noted that team tenuremight have a negative effect on team innovation. Forexample, Katz (1982) suggested that as team tenureincreases, team members are less likely to commu-nicate internally within the group or externally with

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key information sources. In addition, there is someevidence that team tenure might be related to teamhomogeneity, which, in turn, may have negative effectson team innovation (e.g. Jackson, 1996). On the otherhand, Bantel & Jackson (1989) did not find a directeffect of average team tenure on team innovation. West& Anderson (1996) also reported no correlationbetween average team tenure and overall level ofinnovation among a sample of top management teams,but did find a positive relationship between averageteam tenure and impact of innovation on staff well-being.

Third, West et al. (1998) suggested that groupcomposition with respect to personality or dispositionalcharacteristics of the group members might be relatedto group innovation. For example, West & Anderson(1996) reported that the proportion of innovativemembers within the group predicted the introduction ofradical innovation. This is consistent with the assump-tion that idea generation is a cognitive process residingin individual group members, although the translationfrom ideas to actions still requires a variety ofsituational attributes (Mumford & Gustafson, 1988). Inaddition, McDonough & Barczak (1992) found thatwhen the technology was familiar, an innovative styleon the part of the team as a whole led to faster productdevelopment, but that the leader’s style was not asignificant predictor.

Drawing on previous reviews (e.g. Agrell & Gus-tafson, 1996; Guzzo & Shea, 1992), West et al. (1998)also identified four possible group process character-istics that might influence group innovation. First, theexistence and clarity of group goals or objectives wasfound to be positively related to group effectiveness ingeneral (e.g. Guzzo & Shea, 1992; Pritchard, Jones,Roth, Stuebing & Ekeberg, 1988). Pinto & Prescott(1988) studied the life cycle of over 400 project teamsand found that a clearly stated mission was the onlyfactor that predicted success at all the four stages of theinnovation process. They suggested that clarity of goalsaided the innovation process because it enabledfocused development of new ideas.

Given that innovation is not solely stimulatedthrough a burst of creativity by a talented individual,but is influenced by an interactive process among theteam members (Agrell & Gustafson, 1996; Mumford &Gustafson, 1988; West, 1990), West et al. (1998)argued that high levels of participation of groupmembers in decision making should result in moreinnovation. According to Agrell & Gustafson (1996),although the innovation process begins with theproduction of ideas from individuals, often the poten-tial innovation may be abandoned or defeated if theseideas are not properly discussed in a dialogue thatinvolves the whole team.

Task-related team conflict, which implies divergentthinking and perspectives, appears to serve as animportant process that contributes to successful innova-

tion or generation of ideas (Mumford & Gustafson,1988). Tjosvold and colleagues have provided someinteresting empirical evidence that constructive con-troversy leads to improved quality of decision-makingand, therefore, innovation. For example, Tjosvold(1982) reported that supervisors who used a cooper-ative-controversy style explored, understood, accepted,and combined workers’ arguments with their own tomake a decision. It was concluded that task-relatedcontroversy within a cooperative context can result incuriosity, understanding, incorporation, and an inte-grated decision. In another study, Tjosvold, Wedley &Field (1986) asked 58 managers to describe successfuland unsuccessful decision-making experiences by indi-cating the extent to which those involved in making thedecision experienced constructive controversy. Resultsindicated that constructive controversy was signifi-cantly related to successful decision-making. De Dreu& West (2001) found that minority dissent (the publicopposition by a minority of a group to the beliefs,ideas, or procedures of the group majority) stimulateddivergent thinking and creativity, and that such dissent,when combined with high levels of participation indecision making among group members, led to morefrequent group innovations.

Fourth, innovation is more likely to occur when theorganizational and/or group contexts are supportive ofinnovation (Amabile, 1983). Burningham & West(1995) examined the contribution of individual innova-tiveness and team climate factors to the ratedinnovativeness of work groups in a study of 59members of 13 teams in an oil company. Support forinnovation was found to be the most consistentpredictor for predicting externally rated group innova-tiveness. In addition, West & Anderson (1996) foundthat support for innovation was positively correlatedwith their measures of overall amount of teaminnovation, the number of innovations introduced, andthe rated novelty of the innovations.

West’s (2002) Model of Work Group Innovation andCreativityDrawing on the earlier model of work group effective-ness (West et al., 1998) that we have just described,West (2002a) has recently proposed a model focusedon the work group innovation process. This modelsuggests that four sets of factors are the primarydeterminants of work group innovation. They includecharacteristics of the group task, group knowledgediversity and skills, external demands on the group, andintegrating group processes. The effects of group taskcharacteristics and group knowledge diversity andskills on innovation are both hypothesized to be fullymediated by integrating group processes, whereasexternal demands are hypothesized to have both adirect effect on innovation and an indirect effect, alsomediated by group processes. In addition, West arguesthat the innovation process includes two stages,


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creativity and innovation implementation. Creativity isconcerned with idea generation and development,whereas innovation implementation is the applicationof those ideas to produce innovative products, services,and procedures (West, 2002). West further proposesthat external demands on groups can have verydifferent effects on creativity and innovation imple-mentation, although he argues that the effects of taskcharacteristics, knowledge diversity and skills, andintegrating group processes on creativity and imple-mentation are similar.

An examination of the West (2002) model’s specificcontent of the four determinants of creativity andinnovation implementation and of the forms of theeffects they are hypothesized to have both clarifies howWest believes that the innovation process unfolds andraises some questions about the model. First we presentmore details of his model and then we note someconcerns.

Group Task Characteristics include factors takenfrom sociotechnical systems (e.g. Cooper & Foster,1971) and Job Characteristics Theory (Hackman &Oldham, 1980), such as autonomy, task significance,task identity or completeness, varied demands, oppor-tunities for social interaction, opportunities forlearning, and opportunities for development. Themodel predicts that higher levels of these factors willbe correlated with higher levels of both creativity andinnovation implementation.

West contends that requisite diversity of knowledgeand skills among group members is needed forcreativity and implementation innovation, that is, an‘optimal’ level of knowledge diversity exists for agiven task that will encourage creativity and innovationthrough enhanced task performance capabilities, vari-eties of perspectives and approaches to problems, andconstructive conflict. Too little diversity leads toconformity and common approaches to problems.However, too much diversity (or insufficient overlap inknowledge and skills) among group members mayresult in disparate mental models and poor levels ofcoordination and communication that, in turn, neg-atively affect the innovation process. Thus, diversity islikely to have a curvilinear relationship with groupprocesses that mediate its link with creativity andinnovation implementation.

Certain work group processes, termed integratinggroup processes by West (2002a), are affected by grouptask characteristics and knowledge and skill diversityand mediate the impact of the task and knowledgefactors on the innovation process. Integrating groupprocesses allow group members to work collabor-atively to capitalize on their diverse knowledge andskills (West, 2002a). Integrating group processesinclude clarifying and ensuring commitment to groupobjectives, participation in decision making, managingconflict effectively, minority influence, supporting

innovation, developing intra-group safety, reflexivity,and developing group members’ integration skills.

West (2002a) argues that the external context of thework group affects directly the group’s innovationprocess and also the group’s integrating processes. Theexternal context, whether internal or external to theorganization of which the group is a part, makesdemands that can have differential and complex effects.Demands can motivate, but they can sometimes beperceived as threats to the group. In general, Westpredicts that external demands inhibit the creativitystage of the innovation process, but facilitate theimplementation of innovation, although excessivedemand levels may make effective implementation beseen as impossible and lessen group members’ motiva-tion to implement the proposed innovation. Externaldemands may also force the development of moreeffective group integrating processes by serving as adriver of change in the ways that group members worktogether.

In summary, the model of the innovation processproposed by West (2002a) makes important contribu-tions by clearly delineating two major stages of theprocess, a creativity stage and an innovation imple-mentation stage; by specifying the roles of group taskscharacteristics, the diversity of group members’ knowl-edge and skills, and integrating role processes in theinnovation process; and by noting the differentialeffects that external demands and threats have on thecreativity and implementation stages. However, webelieve that West’s model does not fully address veryrecent advances in thinking about I-P-O models ofgroup effectiveness (e.g. Marks et al., 2001). Inaddition, we believe that how work groups managetheir individual and team knowledge requires moreextended elaboration than it has received in existingmodels of group innovation. Consistent with thisbelief, we now consider recent research and theoryconcerning knowledge management. Following that,we present our conceptual framework for work groupinnovation.

Knowledge ManagementIncreasingly, organizations that have effective meansof creating, storing, and transferring knowledge havebeen seen by organizational scholars as having com-petitive advantage over those competitors who do not(Nahapiet & Ghoshal, 1998; Thomas, Sussman &Henderson, 2001). We will use knowledge management(see e.g. Hedlund, 1994) to refer to the way in which anorganization and its units acquire, store, retrieve, share,and transfer information both across organizationalunits and among members of a single unit.

Because we are primarily interested in work group-level phenomena, most of our attention and theoreticaldevelopment related to knowledge management isdevoted to this level. Hedlund (1994) has noted that thework group is the level at which much knowledge

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transfer and learning takes place in organizations,especially with regard to innovation and productdevelopment. Hinsz, Tindale & Vollrath (1997) in areview of research and theory on groups as informationprocessors note that a distinction can be made in termsof the contributions (including knowledge) that indi-vidual members bring to the group interaction and theprocesses involved in the way these individual con-tributions are combined (aggregated, pooled, ortransformed) during group interaction to produce groupoutcomes. Thus, the knowledge of individual groupmembers is an individual-level input and the combina-tion of the members’ knowledge is a group process inan I-P-O approach to group effectiveness.

Following Mohammed & Dumville (2001), weorganize our discussion of knowledge managementwithin the context of team mental models. Team mentalmodels are shared understandings and knowledgestructures that team members have regarding the keyelements of the group’s environment, including thegroup task, equipment, working relationships, andsituations (Mohammed & Dumville, 2001). We suggestthat knowledge management within a work group thatis attempting to create and implement an innovation isultimately concerned with the development of: (a) ashared mental model of the desired end-product (theinnovative product, service, or system) and the meansby which it can be achieved; and (b) an accurateinventory of the task-related and teamwork-relatedknowledge and skills possessed by group members.The shared mental model defines the requisite knowl-edge and skills perceived to be needed for an effectiveinnovation. A comparison of the requisite needs and theinventory of existing knowledge and skills definesthe necessary knowledge and skill acquisition that thegroup must attain by learning or other means.

Several specific aspects of group-level knowledgemanagement have interesting implications for workgroup innovation, including transactive memory, cog-nitive consensus, knowledge distribution andinformation sharing, imputation of own and others’knowledge, and group learning behavior. We brieflydiscuss these below.

The concept of transactive memory systems wasintroduced by Wegner (1987) and has been applied togroups (e.g. Moreland & Myaskovsky, 2000). It refersto systems of memory aids that groups may use to helpensure that important information is recalled. Suchmemory is a social phenomenon and individuals withingroups may use each other as a form of externalmemory aid to augment personal memory. For trans-active memory to be effective, group members musthave a shared awareness of what knowledge is knownby what members of the group. Furthermore, newknowledge that comes to the group should be storedwith the member who is the group’s expert for thatknowledge domain, resulting in increasing special-ization among individual members’ memories

(Mohammed & Dumville, 2001). We suggest that thetype of information may moderate whether suchspecialization is desired: specialization of task-relatedinformation memory may reduce cognitive load and beuseful, whereas teamwork-relevant knowledge mayrequire that it be stored in the memory of all groupmembers. Most existing research in transactivememory has emphasized task-related knowledge(Mohammed & Dumville, 2001), so there may belimits to generalizing to team mental models thatinclude other forms of information.

The members of work groups that are formed torepresent a wide range of perspectives and con-stituencies (e.g. cross-functional teams and task forces)and charged with developing an innovative solution toa complex organizational problem frequently have verydifferent perspectives and interpretations of the issuesinvolved (Mohammed & Dumville, 2001). Such groupsneed to reach cognitive consensus on the interpretationof the issues before they can develop effective andmutually acceptable decisions about courses of actionto take (Mohammed & Ringseis, 2001). While cogni-tive diversity has been suggested as predictive of ideageneration and creativity, cognitive consensus may berequired in order to develop an acceptable groupmission or set of goals.

Research on knowledge distribution and informationsharing in groups (e.g. Stasser, Taylor & Hanna, 1989)suggests that group members tend to discuss what theybelieve to be shared information known to all groupmembers. This finding implies, that groups in whichknowledge is distributed may not effectively use theirdiverse knowledge unless group processes explicitlyelicit unique knowledge from all group members(Mohammed & Dumville, 2001). Stasser (1991) hassuggested that group members be told that they eachmay hold unique information and that others may alsohave unique information.

While one might assume that most individuals inwork groups would understand that information is sodistributed among members, Nickerson (1999) in areview of the research on imputing what other peopleknow concluded that individuals often impute theirown knowledge to others, i.e. assume that others knowwhat they know, especially when they have little directexperience with the other people. While the tendencyto overimpute one’s knowledge to others decreases asone learns more about others, there is a danger in newlyformed work groups for errors to occur that may hinderthe development of shared mental models. To theextent that groups do not develop transactive memorysystems and fail to discuss their unique knowledge,however, such learning about others may not occureffectively and erroneous knowledge imputation maycontinue.

Edmondson (1999) suggested that work groupswould be more effective to the extent that they engagein team learning behaviors, such as seeking feedback,


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sharing information, asking for help, talking abouterrors, and experimenting. However, some of theselearning behaviors are potentially costly because anindividual exhibiting such behaviors may appear to beincompetent and, thus, threaten his or her image(Brown, 1990). Despite the potential benefit of learningbehaviors for the team or organization, research hasshown that people are generally reluctant to disclosetheir errors and are often unwilling to ask for help (Lee,1997). Therefore, when faced with situations that arepotentially embarrassing, people tend to behave inways that inhibit learning (Argyris, 1982). Suchbehaviors would be detrimental for successful groupinnovation.

That said, Edmondson (1996) found that people aremore likely, and willing, to ask for help, admit errors,seek feedback, and discuss problems when theyperceive the interpersonal threats in the situations assufficiently low. Drawing on these insights, Edmond-son (1999) proposed that teams differ in their levels ofteam psychological safety, which is defined as the“shared belief that the team is safe for interpersonalrisk taking” (p. 354). Psychological safety is a teamclimate characterized by interpersonal trust and mutualrespect such that people are comfortable and willing toengage in learning behaviors that make them vulner-able to threats and ridicules (Robinson, 1996). Forexample, Edmondson (1999) found that: (a) teampsychological safety was positively related to teamlearning behavior; (b) team learning behavior waspositively related with team performance; and (c) teamlearning behavior mediated that relationship betweenteam psychological safety and team performance.Therefore, teams with higher levels of team psycho-logical safety engaged in more learning behavior,which in turn led to higher levels of team performance.Equally important, Edmondson also found that teampsychological safety mediated the effects of teamleader coaching and contextual support on teamlearning behavior. In other words, coaching andcontext support promotes team psychological safety,and team psychological safety promotes team learn-ing.

Integration of Current Research and Theory onGroup Effectiveness and Innovation

As we have noted earlier, West and colleagues (West etal., 1998; West, 2002) have provided valuable insightsconcerning work group effectiveness and innovation.We seek to elaborate West’s (2002) model of workgroup innovation in three ways. First, although Westemphasized the important difference between a crea-tive or an idea generation stage and an innovationimplementation stage in the overall innovative process,we draw from Marks et al. (2001) and suggest that thecreativity stage and the implementation stage eachcontain both a transition phase and an action phase.

Second, again following Marks et al. (2001), weemphasize the distinction between team processes andteam emergent states. Specifically, team processes areinterdependent acts members take to yield collectiveoutcomes. However, emergent states refer to “con-structs that characterize properties of the team that aretypically dynamic in nature and vary as a function ofteam context, inputs, processes, and outcome” (Markset al., 2001, p. 357). We contend that some of theprocesses (e.g. intragroup safety) presented in West’s(2002) model should be regarded as team emergentstates. The distinction between group processes andemergent states is an important one. Team processesdepict the nature of team member interactions (e.g.participation, conflict management), whereas emergentstates depicts the cognitive (e.g. team efficacy; sharedmental model), affective (e.g. team cohesion and teampsychological safety), and motivational (e.g. team’sintrinsic motivation) states of the teams. In addition,emergent states can be considered as both inputs to theteam’s current phase of the innovative process and asproximal outcomes that then become inputs for thenext innovative phase.

Third, West (2002) proposed in his model that mostinfluences on the innovative process (i.e. task charac-teristics, diversity in knowledge and skills, andintegrating group processes, but not external demands)have identical relationships with both the creativity andinnovation implementation stages. Instead, we proposethat our elaborated temporal perspective of work groupinnovation suggests that different input and processvariables are relevant and important in predicting therespective outcomes in each phase of the innovationprocess. Concomitantly, we attempt to include a morecomprehensive and inclusive set of variables that arerelevant for the investigation of work group innovation.In particular, we elaborate on the knowledge manage-ment processes and systems that groups use duringinnovation.

We now present our model in more detail.

Dynamic Model of Work Group Innovation

Figure 1 depicts the temporal sequence of variousphases in the process of work group innovation,drawing on both West (2002) and Marks et al. (2001).(Although it is clear that the innovation process is notlinear (West, 2002), we have portrayed it in Fig. 1 as ifit were for sake of parsimony in our graphic depictionof the process.) First, there are two distinct stages inthe innovation process: creativity and innovationimplementation. Within both the creativity and imple-mentation stages are transition and action phases.Nested within each phase are various input and processvariables that influence the interim outcomes for thatphase. Table 1 presents our initial thoughts aboutspecific inputs, processes, and outcomes that are mostrelevant for each of the four phases of the innovation

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process. Below we briefly discuss the major compo-nents of our model.

OutcomesWe believe it is helpful to first discuss the outcomesbecause they help to demarcate the change from one

phase of the innovation process to another. Referring toTable 1, there are clearly identifiable outcomes thatpunctuate the phases. In the transition phase of thecreativity stage, the team interprets the relevant issue(s)and identifies the problem the team or organization isfacing. Once issues are interpreted and the problems

Figure 1. Dynamic model of work group innovation.

Table 1. Inputs, processes, task-related outcomes, and emergent states in work group innovation.

Creativity Stage Innovation Implementation Stage

Transition Phase Action Phase Transition Phase Action Phase

Inputs:

• Taskcharacteristics

Autonomy,Completeness,

Autonomy,Completeness,

Significance(Intrinsic motivation)

Significance(Intrinsic motivation)

• Individual Expertise Goal orientations Expertise Goal orientationOpenness to experience Agreeableness Conscientiousness

• Group Leader behavior(Psychological safety)

Requisite diversity inknowledge and skills

Requisite diversity inknowledge and skills

Diversity in personalityand attitudes

Demographic diversity Social network

• Externaldemands

CompetitionUncertainty

Goal orientation(Group efficacy)

Problem importance Goal orientation(Group efficacy)

Time constraints Time constraintThreats

Processes:

• Transition Mission analysis Goal specification(Shared mental model) Strategy formulation

(Shared mental model)

• Action Monitoring progress Monitoring progresstoward goals toward goals

Team monitoringCoordinationSystem monitoring

• Interpersonal Conflict management Conflict management Conflict management Motivation andAffect management Affect management Affect management confidence building(Group cohesion) (Group cohesion) (Group cohesion) (Group efficacy)

Task-relatedoutcomes

Interpretation of issuesProblem identificationand recognition

Generation of creativeideas/solutions

Evaluation andselectionof ideas/solutions

Application ofideas/solutions toproblem

Group outcomes:Emergent states

Psychological safety; Group efficacy; Shared mental models; Group cohesion; Group affect


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identified, the team moves into an action phase wherethe goal is to generate creative ideas and solutionspertaining to the problem. The transition phase of theinnovation implementation stage starts when the teambegins to evaluate and assess the ideas and solutionsgenerated earlier. Finally, the action phase of theimplementation stage is the team’s concerted effort toapply the idea or solution to the problem. Note thatthere is also a ‘feedback loop’ from the end ofimplementation stage to the beginning of the creativitystage (see Fig. 1). This happens when the teamidentifies related problems or fails to implement theinitial innovation successfully.

Input Variables

Task CharacteristicsBased on socio-technical systems theory (e.g. Cooper& Foster, 1971) and job characteristic theory (Hack-man & Oldham, 1975), West (2002) proposed thatcertain task characteristics help to evoke ‘task orienta-tion’ or intrinsic motivation in the team which will inturn facilitate innovation. In other words, when teamsexperience autonomy, perceive the team as responsiblefor completing a whole task, and consider the task to beimportant, a state of high intrinsic motivation is morelikely to emerge. According to Amabile and colleagues,a high level of intrinsic motivation is fundamental tocreativity and innovation (Amabile, 1983; Amabile &Conti, 1999). We reason that high levels of intrinsicmotivation are most crucial when teams face uncertain-ties or difficulties requiring novel approaches. Hence,ensuring high levels of intrinsic motivation is beneficialduring the transition phase of the creativity stage,where the team is still trying to assess and make senseof the problem at hand. Similarly, intrinsic motivationis crucial during the action phase of the implementationstage because the team is likely to encounter significantroadblocks and obstacles.

Individual Variables

Some individual level variables that are relevant forunderstanding innovation are level of expertise, person-ality, and goal orientation. First, level of expertise isconsidered important because knowledge is the basicrequirement before one can create something newwithin a particular domain (Amabile & Conti, 1999).However, Sternberg (1999) has noted that extremelyhigh levels of expertise can also be a hindrance tocreativity and innovation. (Diversity in the types ofexpertise within a team can help to reduce this problemas we discuss later.) We believe that expertise is mostrelevant during the transition phase of the creativityand implementation stages because problem identifica-tion and evaluation of ideas link directly to domainknowledge.

Second, research at the individual level has foundsome relationships between personality and innovation.

For example, Barrick & Mount (1991) reported in theirmeta-analytic finding that openness to experience isrelated to various learning and training criteria, whichsuggests that individuals who are high on openness toexperience are more likely to adopt new ways ofthinking and to embrace changes. We reason thatopenness to experience is most relevant in the actionphase of the creativity stage because that is when newor even seemingly ‘wild’ ideas must be entertained andencouraged. Next, agreeableness has been found to benegatively related to creative achievement but not withcreative thinking (King, Walker & Broyles, 1996). Inother words, individuals who are agreeable maysuggest more ideas but also often find it difficult toevaluate or find fault with others’ ideas. Hence, wepropose that agreeableness is relevant during thetransition phase of the implementation stage whenlower levels of agreeableness may result in betterevaluation and selection of ideas. Patterson (2002)reasoned that conscientiousness might be negativelyassociated with innovation because individuals high onconscientiousness tend to comply with rules andorganizational norms (e.g. Hogan & Ones, 1997). Wewould agree for the creativity action phase. However,we propose that conscientiousness might be positivelyrelated to innovation implementation (i.e. action phaseof the implementation stage) because a high level ofpersistence is needed when there are resistance andobstacles to change (e.g. Amabile, 1983).

Third, we propose that goal orientation is relevantduring both action phases of the innovation process.Dweck (1986) has demonstrated that the way in whichindividuals approach a task can influence their behav-ior in a number of respects. When approaching a taskfrom a learning goal orientation, the individual’s mainobjective is to increase his or her level of competenceon a given task. Alternatively, when approaching a taskfrom a performance goal orientation, individuals areprimarily concerned with demonstrating their compe-tency to others via task performance. Farr, Hofmann &Ringenbach (1993) reviewed the goal orientationresearch and noted ways that it could be applied towork behavior, including learning. A number of thesesuggested research directions have been recentlypursued. For example, Colquitt & Simmering (1998)found learning orientation to be positively related tomotivation to learn both initially and after performancefeedback had been given, whereas performance goalorientation was negatively related to motivation tolearn. VandeWalle & Cummings (1997) found in twostudies that learning goal orientation and performancegoal orientation were positively and negatively relatedto feedback seeking, respectively. Hence, during theaction phase of the creativity stage, we suggest thatindividuals with strong learning goal orientations willbe more willing to participate in idea generationwithout fear of appearing incompetent. In addition,individuals with strong learning goal orientations may

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be more persistent when encountering problems andobstacles during the action phase of the implementa-tion stage.

Group Variables

Some group level variables that are relevant forunderstanding innovation are diversity, social net-works, and leadership role behavior. After reviewingthe extant literature, Milliken & Martins (1996)suggested that various types of work group diversity(observable traits, such as demographic characteristics;unobservable traits, such as personality and values; andfunctional characteristics, such as knowledge, skillsand organizational experience) may be differentiallyrelated to group processes and outcomes. We proposethat all three types of diversity (i.e. demography,personality and attitudes, and knowledge and skills) areimportant predictors of work group innovation but aredifferentially important in different phases of theinnovation process. To summarize our predictions, webelieve that demographic diversity is most relevant foridea generation, diversity in knowledge and skills isrelevant for both idea generation and evaluation, andthat diversity in personality and attitudes is morerelevant for application and implementation of theideas. Since very high levels of diversity can result indysfunctional conflict within a team (Milliken &Martins, 1996), we use West’s (2002) concept ofrequisite diversity to suggest that there are optimallevels of diversity that lead to effective creativity andinnovation implementation.

Second, research has shown that the team’s socialnetwork within the organization is related to the actionphase of the implementation stage (e.g. Tsai, 2001;Tsai & Ghosal, 1998). Tsai & Ghoshal (1998) foundthat social interaction and trust among organizationalteams were related to the extent of resource exchangeamong teams and product innovation within thecompany. Drawing on a network perspective onorganizational learning, Tsai (2001) found that organ-izational groups can produce more innovations andenjoy better performance if they occupy centralnetwork positions that provide access to new knowl-edge developed by other units but only if units have theability to successfully replicate the new knowledge.Thus, empirical evidence from network research sug-gests that teams that are connected and embeddedwithin the social network of the organization can morereadily garner support and resources that result insuccessful implementation of their innovative ideas.

Third, as noted earlier, Edmondson (1999) foundthat team leaders’ coaching on learning behaviorspromotes psychological safety in teams, an emergentstate which we believe is crucial for efficient commu-nication and interactions among team members,especially in the idea generation and idea evaluationphases of the innovative process.

External DemandsAccording West (2002), competition and uncertainty inthe external environment will facilitate innovation.Hence, they are relevant in the transition phase of thecreativity stage in that they set off the whole innovationprocess. West (2002) also noted that having timeconstraints pushes the teams to actively implement theinnovation, but that external demands that are per-ceived as threats or constraints on the team are likely toresult in fewer ideas of lower novelty being generatedby a work group.

Next, while goal orientation is often conceptualizedas a dispositional characteristic, its theoretical founda-tion also recognizes that it can be activated by a varietyof situational factors (Dweck, 1986; Farr et al., 1993).For example, Martocchio (1994) found that trainees ina situation that elicited a learning goal orientationexperienced a significant decrease in computer anxietybetween pre- and post-training assessments, but notthose trainees in a situation that led to a performancegoal orientation. Also, trainees in the learning goalorientation condition experienced a significant increasein computer efficacy beliefs, while trainees in theperformance goal orientation condition experienced asignificant decrease in computer efficacy between thepre- and post-training assessments. Relatively fewstudies have looked at goal orientation in group or teamresearch aside from considering fit between individualand team goal orientations (Kristof-Brown & Stevens,2001). However, given that goal orientation is closelyrelated to learning behaviors (Colquitt & Simmering,1998; VandeWalle & Cummings, 1997), it is poten-tially a useful variable to be considered in teamresearch, especially in the area of group innovation. Forexample, the proportion of members with learning orperformance goal orientation within the group mightpredict the level of learning behaviors engaged by theteam, which lead to amount and/or quality of groupinnovation. It might also be possible to enhance theemergent state of group efficacy by manipulating andinducing a learning goal orientation in the workenvironment (Martocchio, 1994; Winters & Latham,1996).

Process VariablesAs described earlier, Marks et al. (2001) provide anexcellent taxonomy of team processes that are relevantfor different phases of task accomplishment. In thefollowing section, we briefly describe each process andhow they might be relevant for various phases of theinnovation process.

Transition ProcessesThree activities included as transition processes are,mission analysis, goal specification, and strategyformulation. For mission analysis, the team’s majorobjective is to identify the main tasks at hand, which ismost crucial for the innovation process during the


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transition phase of the creative stage, where the teamhas to identify the target problem and interpret therelevant issues. Goal specification refers to “theidentification and prioritization of goals and subgoalsfor mission accomplishment” and strategy formulationrefers to “the development of alternative courses ofaction for mission accomplishment” (Marks et al.,2001, p. 365). We propose that both goal specificationand strategy formulation are particularly relevantduring the transition phase of the implementationstage, where the team has to develop concrete plans ofactions to implement the ideas. Note that all the threetransition processes help to cultivate the emergent stateof shared cognitions or team mental model in the team.It is likely that cognitive consensus (Mohammed &Ringseis, 2001) is important for both of the transition

Action ProcessesAction processes include activities such as systemmonitoring, goal monitoring, team monitoring, andcoordination. Groups monitor progress toward theirgoals by assessing the discrepancy or gaps between thegoals and the current situation, which is needed bothwhen the team is generating ideas as well as whenimplementing the solutions. Team monitoring involvesfeedback, coaching, or assistance to other groupmembers in relation to task accomplishment. Coor-dination refers to actions targeted at managing theinterdependent actions of team members toward taskaccomplishment. System monitoring refers to activitiesthat track the team’s resources as well as the environ-mental conditions. Note that all four action processesare important when the team is engaging in theinnovation implementation, whereas we propose thatgoal monitoring is the primary action process needed inthe creativity stage.

Interpersonal ProcessesConflict management, affect management, and motiva-tion/confidence building are processes that workgroups engage in to manage their interpersonal rela-tionships. Conflict management can include bothpreventive and reactive approaches to managing inter-personal disagreements or disputes. Note that effectiveconflict management is not a focus on conflictavoidance, because task-or idea-focused conflict can beconstructive to the innovation process (West, 2002).Affect management involves monitoring and regulatingthe team members’ level of emotional arousal. Bothconflict and affect management are especially impor-tant during idea generation and evaluation given thehigher probability of disagreement and conflict. Notethat effective conflict and affect management willenhance the team’s emergent state of group cohesion.Motivation and confidence building involves activitiesthat preserve or enhance the team’s sense of efficacybeliefs. Hence it will lead to the emergent state ofgroup efficacy, which is crucial during the implementa-

tion stage since the team is likely to encounter highlevel of resistance and obstacles toward change.

SummaryWe have provided an I-P-O model of the work groupinnovation process that identifies transition and actionphases with each of two major stages of innovation: acreativity stage and an innovation implementationstage. The transition phases both involve primarilyplanning and evaluation tasks that guide later goalaccomplishment. The action phases are both involvedprimarily in acts that directly contribute to goalaccomplishment. Within the creativity stage, the transi-tion phase consists of interpretation of issues andproblem identification and the action phase consists ofidea generation. Within the innovation implementationstage, the transition phase consists of the evaluation ofthe generated ideas as possible solutions and selectionof the one(s) to implement and the action phaseconsists of the application of the idea(s) to the problem.We also provide our initial thoughts on the specificinputs, group processes, and emergent states that areimportant for each phase of our model. We trust thatour model will generate innovative research examiningand extending it in novel ways.

AcknoweledgmentPaul Tesluk would like to acknowledge support fromthe National Science Foundation (Grant #0115147) forhis work on this chapter.

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Creativity and Innovation = Competitiveness?When, How, and Why

Elias G. Carayannis and Edgar Gonzalez

School of Business and Public Management, The George Washington University, USA

Abstract: In this chapter, we propose to look at both for-profit and not-for-profit entities toexamine:

(a) when, how, and why creativity and innovation occur;(b) how and why creativity triggers innovation and vice versa; and(c) what are the connections and implications for competitiveness of the presence or absence of

creativity and innovation using empirical findings from both the public and private sectors.

We combine literature sources (including those of the authors) as well as field interviews on thepractice and implications of creativity and innovation from the perspective of competitiveness.

Keywords: Innovation; Creativity; Competitiveness; Institutional learning; Entrepreneuriallearning; Public-private sector partnerships.

IntroductionZum sehen geboren; Zum schauen bestellt:{Born to see; Meant to look}

(Faust, Goethe)

In Greek, the word for creator also denoting God, is theword poet. This underlines the dynamic underlyingcreativity, in that it encompasses both a structured,disciplined, scientific as well as an artistic element: onecould say that creativity emerges from the interplayand interfacing of science and art—in a way, it could beconceived of as being the art of science and the scienceof art (Carayannis, 1998b).

The relationship between creativity, innovation andcompetitiveness at a very basic level appears readilyapparent: creativity is a necessary (but not sufficient)factor enabling innovation, and innovation of differenttypes can improve national economic competitiveness.This relationship is extremely significant, however, inthat it links three levels of analysis: creativity (mostlyat the individual level or micro level), innovation(mostly at the organizational or meso level), andcompetitiveness (mostly at the national or macro level)(see Figs 1, 3 and 8). Understanding the specific linksand dynamics contained within this relationship mayprovide significant insight into the ability of nations tobuild and sustain conditions of competitiveness.

In this chapter, we explore what creativity andinnovation are and what their significance and role isfor people and organizations (and by extension poten-tially nations) as ingredients, catalysts, and possiblyinhibitors of competitiveness. We look at both for-profitand not-for-profit entities to examine:

(a) when, how, and why creativity and innovationoccur;

(b) how and why creativity triggers innovation andvice versa; and

(c) what are the connections and implications forcompetitiveness are of the presence or absence ofcreativity and innovation.

CreativityManagement is, all things considered, the mostcreative of all arts.It is the art of arts, because it is the organizer oftalent.

(Jean-Jacques Servan-Schreiber)

Starting at the individual level, creativity may bedefined as the capacity to ‘think out of the box’, to thinklaterally, to perceive, conceive, and construct ideas,models, and constructs that exceed or supersedeestablished items and ways of thinking and perceiving.

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Creativity is related to the capacity to imagine, since itrequires the creator to perceive future potentials thatare not obvious based on current conditions. From acognitive perspective, creativity is the ability toperceive new connections among objects and con-cepts—in effect, reordering reality by using a novelframework for organizing perceptions.

Creative types such as artists, scientists, and entre-preneurs often exhibit attributes of obsessed maniacsand clairvoyant oracles (Carayannis, 1998–2002,George Washington University Lectures on Entrepre-neurship) as well as the capacity and even propensityfor creative destruction that is how Joseph Schumpeterqualified innovation. Albert Scentzgeorgi, a NobelPrize laureate, defined creativity as ‘seeing whateveryone sees and thinking what no one has thoughtbefore’.

Innovation

Discovery consists of looking at the same thing aseveryone else and thinking something different.

Albert Szent-Gyorgyi—Nobel Prize Winner

Innovation is a word derived from the Latin, meaningto introduce something new to the existing realm andorder of things or to change the yield of resources asstated by J. B. Say quoted in Drucker (Drucker, 1985).

In addition, innovation is often linked with creatinga sustainable market around the introduction of newand superior product or process. Specifically, in theliterature on the management of technology, techno-logical innovation is characterized as the introductionof a new technology-based product into the market:

Technological innovation is defined here as asituationally new development through which peopleextend their control over the environment. Essen-tially, technology is a tool of some kind that allowsan individual to do something new. A technologicalinnovation is basically information organized in anew way. So technology transfer amounts to thecommunication of information, usually from oneorganization to another (Tornazky & Fleischer,1990).

The broader interpretation of the term ‘innovation’refers to an innovation as an “idea, practice or materialartifact” (Rogers & Shoemaker, 1971, p. 19) adoptedby a person or organization, where that artifact is“perceived to be new by the relevant unit of adoption”(Zaltman et al., 1973). Therefore, innovation tends tochange perceptions and relationships at the organiza-tional level, but its impact is not limited there.Innovation in its broader socio-technical, economic,and political context, can also substantially impact,shape, and evolve ways and means people live theirlives, businesses form, compete, succeed and fail, andnations prosper or decline (see Fig. 1).

Specifically, Fig. 1 attempts to illustrate the natureand dynamics of an emerging globalization frameworkin which creativity and innovation—as enabler oftechnological effort in manufacturing and as an engineof industrial development—can lead to improvedcompetitiveness and sustained development. On theother hand, lack of creativity and innovation constitutesa factor for failure in manufacturing performance and,as a result, is a factor for failure in economicperformance, too. For those countries in which crea-tivity and innovation is applied effectively, globali-zation can be an engine of beneficial and sustainableeconomic integration. However, globalization can be apowerful force for deprivation, inequality, margin-alization and economical disruption in thosenon-competitive countries.

Government or market success or failure is deter-mined by how they take advantage of the four majorelements that shape the setting for creativity, innova-tion and competitiveness in the globalized world:

(1) the coordination and synergy in the relationshipbetween governments, enterprises, research labora-tories and other specialized bodies, universities andsupport agencies for small and medium enterprises(SMEs);

(2) the power of information and communicationtechnology;

(3) the efficiency that managerial and organizationalsystems can bring to production and commerce;and

(4) the international agreements, rules and regula-tions.

All the four elements of this framework will impact oncreativity and innovation at the micro level (firm level)as well as on innovation and competitiveness at themacro level (industry, national, global).

CompetitivenessCompetitiveness is the capacity of people, organiza-tions, and nations to achieve superior outputs andespecially outcomes, and in particular, to add value,while using the same or lower amounts of inputs (seeFig. 2).

Moreover, entrepreneurial value-adding and entre-preneurial learning by doing, learning by analogy, andlearning by failing, does not belong to the realm of for-profit entities only, but also in the domain ofnot-for-profit entities. This is shown in Fig. 2 with theoverlapping circles connecting creativity and innova-tion activities across for-profits and not-for-profits.

The standard for judging whether these results are‘superior’ can encompass both prior capabilities of aparticular organization or nation and a comparison withother organizations or nations. The critical assumptionof competitiveness, then, is that it is accomplishedthrough a process of organizational improvement,where the institutions in an economy leverage people,

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Elias G. Carayannis and Edgar Gonzalez Part VIII

knowledge and technologies to rearrange relationshipsand enable higher states of production.

When, Why and How Creativity Arises

Imagination is more important than knowledge.To raise new questions, new possibilities, toregard old problems from a new angle, requires

creative imagination and marks real advance inscience.

(Albert Einstein)

The problem with ‘creativity’ is that it is an intangible.While we generally know when something is creative,we often don’t know why. It seems difficult toarticulate a precise definition of the topic.

Figure 1. The CIC value chain: Global and local perspectives.

Creativity and Innovation = Competitiveness?Chapter 3

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Aristotle, for example, suggested that inspirationinvolved a form of madness whereby great insightsbegan as a result of a person’s own thoughts progress-ing through a series of associations (Dacey & Lennon,1998, p. 17). This view of the creative individual asmad, or potentially so, continued through the nine-teenth century.

Freud believed creative ability was a personality traitthat tends to become fixed by experiences in the firstfive years of life (Dacey & Lennon, 1998, p. 36). Hemaintained that creative expression was a means ofexpressing inner conflicts that otherwise would resultin neuroses. Creativity was a sort of emotionalpurgative that kept men sane (Kneller, 1965, p. 21).During the first half of the twentieth century, B. F.Skinner and other behaviorists considered creativeproduction to be strictly the result of ‘random muta-tion’ and a product of appropriate reinforcers providedby society (Dacey & Lennon, 1998, p. 138).

Cognitive View of Creativity (Personal Creativity)Kneller (1965, p. 3) suggested that definitions ofcreativity seem to fall into four categories. Creativity isconsidered from the standpoint of the person whocreates, in terms of mental processes, in terms of itsproducts, or focuses on environmental and culturalinfluences. He states that “an act or an idea is creativenot only because it is novel, but also because itachieves something that is appropriate to a givensituation” (1965, p. 6). We create when we discoverand express something that is new to us. The operativephrase is ‘new to us’; even if another person hasdiscovered something, it is still creativity if we have re-discovered it for ourselves.

Amabile (1996, p. 33) appears to provide the mostcomplete definition available to date. She suggests atwo-part definition of creativity:

(1) that a product or response is creative to the extentthat appropriate observers independently agree it is

creative. Appropriate observers are those familiarwith the domain in which the product or theresponse articulated (p. 33); and

(2) that a product or response will be judged ascreative to the extent that it is both a novel andappropriate task at hand, and the task is heuristicrather than algorithmic. She defines algorithmictasks as those for which the path to the solution isclear and straightforward; heuristic tasks are thosefor which algorithms must be developed. She callsthese tasks ‘problem discovery’ (p. 35).

Amabile (1996, p. 90) also lists personality traits thatappear repeatedly in summaries of empirical work onthe characteristics of creative persons:

• High degree of self-discipline in matters concerningwork.

• Ability to delay gratification.• Perseverance in the face of frustration.• Independence of judgment.• A tolerance for ambiguity.• A high degree of autonomy.• An absence of sex role stereotyping.• An internal locus of control.• A willingness to take risks.• A high level of self-initiated, task-oriented striving

for excellence.

Of their nine principal traits, it may be helpful tofurther define three: stimulus freedom, functionalfreedom, and flexibility. Stimulus freedom (Getzels,Taylor, Torrance, cited by Dacey & Lennon, 1998,p. 100) occurs when people are likely to bend the rulesto meet their needs, if the stated rules of a situationinterfere with their creative ideas. Functional freedomis the ability to use items for other creative, or uniqueuses. Dacey and Lennon contend that the moreeducation a person has, the more rigid his or herperception of function is likely to become. Also,

Figure 2. CIC: Value-adding and learning topology.

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because education tends to encourage complexity ofthought, this may produce a convoluted thinking stylewhich works against producing simple ideas—the onesthat comprise many of the world’s greatest solutions.Flexibility is the capacity to see the whole of asituation, rather than just a group of uncoordinateddetails.

Gestalt psychologists believed that creative problem-solving is similar in important ways to perception.They argued that it is primarily a reconstruction ofgestalts, or patterns, that are structurally deficient.Creative thinking begins with a problematic situationthat is incomplete in some way. The thinker grasps thisproblem as a whole. The dynamics of the problemitself and the forces and tensions within it, set upsimilar lines of stress within his or her mind. Byfollowing these lines of stress, the thinker arrives at asolution that restores harmony of the whole (Kneller,1965, p. 27). Restructuring and productive thinkingoften do not occur because problem-solvers tend tobecome fixated on attempting to apply past experienceto the problem, and thus do not deal with the problemon its own terms (Weisberg, 1992, p. 51).

Creativity in an Organizational ContextCulture is the invisible force behind the tangiblesand observables in any organization, a socialenergy that moves people to act. Culture is to theorganization what personality is to the individual—a hidden, yet unifying theme that providesmeaning, direction, and mobilization.(Killman, R., Gaining Control of the Corporate

Culture, 1985)

In the business context, creativity now is championedby certain authors as the critical element enablingchange in organizations. Kao (1996, xvii) definescreativity as:

the entire process by which ideas are generated,developed and transformed into value. It encom-passes what people commonly mean by innovationand entrepreneurship. In our lexicon, it connotesboth the art of giving birth to new ideas and thediscipline of shaping and developing those ideas tothe stage of realized value.

Kao views creativity as the “result of interplay amongthe person, the task, and the organizational context”(cited in Gundry et al., 1994). Drazin et al. (1999)agree with this assertion. They conclude that creativityis both an individual and group level process. Complex,creative projects found within large organizationsrequire the engagement of many individuals, ratherthan just a few. It is often difficult to assign credit toany one individual in a creative effort (Sutton &Hargadon, cited in Drazin et al., 1999). Creativity, theybelieve, is an iterative process whereby individualsdevelop ideas, interact with the group, work out issues

in solitude, and then return to the group to furthermodify and enhance their ideas. Their sense makingperspective of creativity illustrates the notion thatindividuals are influenced in their creative effortsby such factors as conflict, political influence, andnegotiated order at the group level.

Environmental Effects on CreativityWhen I am, as it were, completely myself, entirelyalone, and of good cheer . . . it is on suchoccasions that my ideas flow best and mostabundantly. Whence and how they come, I knownot; nor can I force them. Those ideas that pleaseme I retain in memory.(W. A. Mozart, quoted in Brewster Ghiselin, 1952,

p. 34)

Woodman & Schoenfeldt (1990, p. 18) stress theimportance of social environment. They state: “it isclear that individual differences in creativity are afunction of the extent to which the social andcontextual factors nurture the creative process.Research on creativity has led to a recognition of thefact that the kind of environment most likely to producea well-adjusted person is not the same as the kind ofenvironment most likely to produce a creative person”.Because of the dearth of research in this area, we willbriefly examine the factors through an ever-wideningcircle of social influences—from family to culture.

Amabile (1996, p. 179) reports that there appear tobe three social factors that are important for creativebehavior:

• Social facilitation (or social inhibition), broughtabout by the presence of others: She reports that thepresence of others can impair performance on poorlylearned or complex tasks, but enhance performanceon well-learned or simple tasks (p. 181). In addition,there is much evidence that subjects perform morepoorly on idea-production tests when they worktogether than when they work alone.

• Modeling, or the imitation of observed behavior:Research suggests that a large number of creativemodels in one generation will stimulate generalcreative production in the next generation (Simonton,cited on p. 189). At the individual level, the patternof influence seems to be complex. At the highestlevels of creative eminence, modeling may berelatively unimportant. In addition, although expo-sure to creative models may stimulate earlyhigh-level productivity, it may be important at somepoint to go beyond the examples set by one’smentors.

• Motivational orientation, or an individual’s intrinsicor extrinsic approach to work: Studies suggest thatintrinsic orientation leads to a preference for chal-lenging and enjoyable tasks, whereas an extrinsicorientation leads to a preference for simple, predict-able tasks (p. 192).


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There is some evidence that cultures may promote orinhibit creativity. Arieti (1976, p. 303) explored cul-tural influences on creativity and suggests that thepotentiality for creativity is deemed much morefrequent than its occurrence. Some cultures promotecreativity more than others and he labeled thesecultures as ‘creativogenic’. He held that people becomecreative (or to use his term, ‘genius’) because of thejuxtaposition of three factors:

(1) The culture is right. He uses the example that theairplane would not have been invented if gasolinehad not been invented.

(2) The genes are right. The person’s intelligence,which is known to be genetic, must be high.Creativity, which may or may not be genetic, mustalso be high.

(3) The interactions are right. He offers the example ofFreud, Jung, and Adler. If Jung and Adler had nothad Freud to compete over, and against, it isquestionable whether either Jung or Adler wouldhold such a high position in psychology today.

Hofstede (1980, p. 43), in a study of the culture of 40independent nations, found four criteria by whichtheir cultures differed: power distance, uncertaintyavoidance, individualism-collectivism and masculinity-feminity. These dimensions appear to have a powerfulinfluence on the ‘collective mental programming of thepeople in an environment’. They are also grounded inour collective cultural history. Americans, for example,tend to exhibit high individualism, small powerdistance, and weak uncertainty avoidance. That theyshow these tendencies reflects American history whichhas placed high value on equality, independence, andwillingness to take risks.

This cultural influence is qualitatively different thanthe social influences mentioned in previous creativitymodels. For want of a better term, we call it ‘culturalembeddedness’, because it implies more than a socie-ty’s norms, values, and mores. It is what defines ourreality. In light of this additional component, we areproposing a new model of creativity which not onlyillustrates the components of creativity, but the creativeprocess as well. In this model, personality andcognitive factors interact with the individual and viceversa. The social environment interacts with the threefactors and vice versa; the individual initiates andparticipates in the creative process. Cultural embedded-ness influences not only all of the creative factors butall steps of the creative process.

When, Why and How Innovation Arises

Innovation is creative destruction.

The success of everything depends upon intuition,the capacity of seeing things in a way whichafterwards proves to be true.

(Joseph Schumpeter)

If creativity can be seen as a process and a product orevent, the use of the term innovation in terms ofcreativity seems to muddy the waters. If one consultsthe business literature, ‘innovation’ and ‘creativity’appear to be used interchangeably.

Innovation is seen as the panacea for competingsuccessfully in today’s global marketplace, but in muchof the literature the concept is a vague one. Managersare told they must promote innovation, but they are notgiven the specifics of how this is to be accomplished.The articles often cite one or two examples ofcompanies that are profiting from ‘innovation’, and thereader is left to grapple with the mechanics ofextrapolating useful information that is transferable tohis or her own situation.

In an extensive review of popular and academicbusiness literature, we found that the informationprovided ranged from the esoteric notion of promotingan innovative organizational climate to concrete stepsin creative problem-solving. Given the target audience,some of the information provided is aimed at practicalapplications. Apart from the confusion over terminol-ogy, it appears that the literature may be divided intothe following broad categories: the nature of innova-tion, the individual and creativity/innovation, how topromote innovation within organizations, the politicalnature of innovation, and enhancing creativity.

Drucker also links innovation with entrepreneurshipby noting that entrepreneurs appear to not have acertain kind of personality in common, but a commit-ment to the systematic practice of innovation. Hedescribes innovation as ‘the means by which theentrepreneur either creates new wealth-producingresources or endows existing resources with enhancedpotential for creating wealth’ (Drucker, 1998, p. 149).

In her study of the longevity of successful pharma-ceutical companies, Henderson (1994) found that thecompanies constantly challenged conventional wisdomand stimulated a dynamic exchange of ideas. ‘Theyfocused on continuously refurbishing the innovativecapabilities of the organization. They actively managedtheir companies’ knowledge and resources’ (Hender-son, 1994, p. 102). Gundry et al. (1994) further definesorganizational innovation by stating that: “Organiza-tions that encourage employee creativity share certaincharacteristics: They capitalize on employee attributes,enhance employees’ conceptual skills, and cultivate anorganizational culture that fosters experimentation andstimulates creative behavior”.

Chesbrough & Teece (1996) attack the problem ofdefinition from a different viewpoint. They state thatthere are two types of innovation. Autonomous innova-tions are those that can be pursued independently fromother innovations. They use the example of a newturbocharger to increase horsepower in an engine,which can be developed without the complete redesignof the engine or the rest of the car. Systemicinnovations, on the other hand, are those which must be

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accomplished along with related complementary inno-vations. Redesigning a workflow process in a factorywould be an example of a systemic innovation, becauseit requires changes in supplier management, personnel,and information technology.

The Relationship Between Creativity and Innovation

Learning is formulating intelligence. Deciding isimplementing intelligence.Entrepreneurship is organized abandonment.

(J. B. Say)

To promote and support innovation within organiza-tions, a corporate culture must be adopted that willaccept and defend the vagaries of individual and groupcreativity. Leaders must ensure that employees believethe expectation that innovation is part of their jobs.

They do this by providing a safe environment, wherethere is freedom to fail. A high tolerance for failureallows for trial and error, experimentation, and con-tinuous learning (Ahmed et al., 1999).

People, culture, and technology serve as the institu-tional, market, and socio-economic ‘glue’ that binds,catalyzes, and accelerates interactions and manifesta-tions between creativity and innovation as shown inFig. 3, along with public-private partnerships, inter-national R&D consortia, technical/business/legalstandards such as intellectual property rights as well ashuman nature and the ‘creative demon’. The relation-ship is highly non-linear, complex and dynamic,evolving over time and driven by both external andinternal stimuli and factors such as firm strategy,structure, and performance as well as top-downpolicies and bottom-up initiatives that act as enablers,

Figure 3. CIC linkages: A system dynamic approach.


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catalysts, and accelerators for creativity and innovationthat leads to competitiveness.

C.III. Creativity, Innovation and Competitiveness:Concepts and Empirical Findings from the Public andPrivate Sectors

Leaders in learning organizations have the abilityto conceptualize strategic insights, so that they canbecome public knowledge open to public debateand improvement

(Peter Senge, The Fifth Discipline, 1990, p. 356)

Creativity is the result of inspiration and cognition, theliberation of talent in a nurturing and even provocativecontext and it is mostly an intensely private andindividualistic process—it operates at the micro (indi-vidual) level (see Fig. 4). Innovation is a team effortand takes place at the meso (group/organizational)level, as it needs to combine the blessings of creativity

with the fruits of invention and the propitiousness ofthe market conditions—timing, selection, and sequenc-ing are important as well as ‘divine providence’,obsession and clairvoyance. Competitiveness is theedifice resting on the pillars of creativity, invention,and innovation and it materializes at the macro(industry/market/national/regional) level (see Fig. 4).

We are inspired from the Nobel-prize-winningdiscovery of the double helix as nature’s fundamentalscaffold and evolutionary competence, to elucidate andarticulate the nature and dynamics of the inter-relationship between and among creativity, innovationand competitiveness and their evolutionary pathways.We attempt to do that by means of the Creativity,Innovation and Competitiveness Double Helix(CIC2Helix) (Fig. 4) in which one strand represents theflow and record of creativity and the other that ofcompetitiveness. At any point along their evolution,these two strands are linked by the value-adding chain

Figure 4. The CIC spiral and value-added chain.

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of creativity, invention, early-stage innovation, late-stage innovation, productivity, competitiveness (CI3PCvalue-adding chain). This chain serves as the catalystand accelerator of social, economic, organizationaland individual learning and meta-learning whichallows for the CI2C Helix to continue evolving byenhancing and advancing the effectiveness of gen-erated knowledge and the efficiency of knowledgetransmission and absorption. In so doing, knowledgeeconomies of scale and scope are attained at increas-ingly higher levels, allowing for more to beaccomplished with less, faster, cheaper and better.These gains are manifested in diverse ways at themicro, meso and macro levels, namely higher standardsof living, more competitive firms, more robust econo-mies, and accelerated and more sustainabledevelopment trends.

We further attempt to shape and corroborate ourperspectives with field research questionnaires thatwere responded to by public and private sectormanagers from a number of countries around the world(see Appendix), dealing with issues related to drivers,critical success and failure factors and measures ofcreativity, innovation and competitiveness.

Our overall findings from talking to practitionersfrom the public, and the private sector, point to thefollowing main challenges that encompass as wellpotential opportunities for growth and development ifinnovation and creativity is allowed to blossom:

• Failure of imagination (where the policy makers andmanagers fail to envision the future and confront thepresent).

• Failure of courage (where the decision makers aretoo afraid to confront real challenges and as a result,they shy away from critical reality checks).

• Fear to succeed (where the decision makers and otherstakeholders are individually or institutionally hesi-tant to embrace the potential and changes ofsuccess—either consciously or subconsciously andthus undermine or undercut their own efforts).

• Fear to fail (where the policy makers and managersare so concerned with failure and fail to realize thatone can not avoid risk but that one can only manageit as best possible; as a result, they end up mis-managing risk and engendering processes and trendsthat lead to failure even when not necessary).

• Too short term focus on earnings (the ‘tyranny of themarketplace’ often precipitates decisions that arepoor from a mid- to long-term perspective and onlyserve as short-term expediencies). In the case of thepublic sector, the equivalent case is that of politiciansthat only care about or are forced to focus only onwinning the next elections.

• Strategic versus Tactical choices and actions (as aresult of all the above-mentioned ‘pathologies’ ofdecision-making, tactical choices trigger actions that

often preempt or impede strategic choices andactions).

We further organize and present our findings by keythemes:

Theme 1: Key dimensions of Innovation and Crea-tivity.

Theme 2: Drivers of Innovation—Catalysts andInhibitors.

Theme 3: A glimpse of the current situation in severalcountries—Challenges and Opportunities.

• Theme 1: Key Dimensions of Innovation andCreativityIn the public and private sectors, innovation can beunderstood as a way of rethinking and reshapinggovernment, repositioning public service/publicorganizations, managing/leading the change process,restructuring programs and service delivery, rede-signing and improving service delivery for citizens,redesigning accountability frameworks and perform-ance measures and revitalizing public serviceproviders and private firms (see Table 1).

Based on the responses we received from publicand private sector practitioners from a number ofcountries, innovation is seen as encompassing thefollowing attributes with the most important oneslisted first:

• Inventing something new.• Seeing something from a different perspective.• Introducing changes.• Improving something that already exists.• Spreading new ideas.• Performing an existing task in a new way.• Generating new ideas only.• Following the market leader.• Adopting something that has been successfully

tried elsewhere.• Attracting innovative people.

• Theme 2: Drivers of InnovationAs we can see from Figs 1, 5 and 6, there are a hostof internal and external enablers, catalysts, andaccelerators of innovation since it is a complex, non-linear, and interactive process with human,technological and cultural underpinnings. Ourempirical findings are reflected in these figures andare further discussed below.

According to the empirical findings, there arefactors that act as catalysts or inhibitors for creativity,innovation and competitiveness in the public and inthe private sector:

Catalysts

(1) Leadership, vision, strategic plan (with the rightgoals). Relative organizational autonomy andcertain degree of authority to innovate.

(2) Innovation/creativity rewards system in place.


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(3) Protection of intellectual property rights (IPR).(4) Propitious organizational environment for con-

verting tacit ideas and knowledge into explicitproposals for improvement: open and frequentcommunication and dialogue, strategic rotationbetween different functions and technologiesand access to information (Nonaka & Takeuchi,1995).

(5) The right mix of people and esprit de corpsmanifested in well-functioning teams.

(6) Sense of urgency (if you feel you’re up againstit, as happens frequently in the private sector,you become very innovative and competitive).

(7) One innovates when in need or ‘necessity is themother of invention’: review of innovationexperiences in governments during the last 25years shows that innovation occurred in almostall of the cases in an environment of financialscarcity and even crisis (Glor, 2001).

(8) Willingness of governments to innovate—forthat, motivation is required in both, central andfront line government officials and manage-ment.

(9) In the private sector, supportive managementwilling to take risks and encourage freshthinking. Public officials and private managerswith enough time to formulate and implementinnovation initiatives.

(10) Government support for R&D and incentivesfor investment in R&D such as R&D taxcredit.

(11) Availability of risk capital including angelinvestment and venture capital.

(12) Compromise between the political and eco-nomic power and existence of social control.

(13) Innovation networks and clusters such as:existence of educational institutions of higherlearning, think tanks, training programs andtechnical teams, existence of institutions thatact as conveners of networking events, collab-oration among different countries andinstitutions in international collaborative R&Dprojects.

(14) Diversity of people and free flow of ideas(generation of widely divergent views, chal-lenging of assumptions, testing of hypotheses,

Table 1. Competitiveness, productivity and innovation measures.

Competitiveness Productivity Innovation

National • Standards of Living• Gross Domestic Product

(GDP)• Expenditures• Gross National Product

(GNP)• World Economic Forum 8

Factors• Unemployment• Exchange Rate• Purchasing Power Parity• Equity Markets• Bond Markets• Interest Rates• LIBOR and Money Rates• Dow Jones Global Indexes

• GDP/worker• BW Production Index• Total Factor Productivity

(TFP)• Compensation/Hour• Tornqvist and Fisher Indexes

• Research & Development(R&D) as % GDP

• R&D• National Labs• Nobel Prizes

Industry • Sales• Market Share• Dow Jones U.S.• Dow Jones Global• Inventories• Profitability

• Output/worker• Profitability• Industry Groups• Compensation/Hour• Tornqvist Sector Output• Federal Reserve Board Index• Bureau of Labor Statistics

KLEMS

• R&D as % GDP• Patents• Scientists• R&D Expenditure• R&D Personnel• R&D % of Profit

Firm • Sales• Market Share• Equity• Profitability

• Output/worker• Profitability• Output/hour• Standard Costs

• R&D as % Sales• R&D Expenditure• Patents• Scientists• R&D Personnel• National Labs

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compensation for cultural or intellectual myo-pia).

Inhibitors

(1) Resistance comes from the elites in that elites areinclined to screen out innovations whose conse-quences threaten to disturb the status quo, forsuch disruption may lead to a loss of position forthem (Rogers, 1995).

(2) Much innovation fails due to resistance tochange: failure of courage and failure of imag-ination can prove to be formidable deterrents ofinnovation.

(3) Sense of ‘comfort’: why should I push myselfand disturb convenient routines; conservatism inits multiples forms, e.g. ‘don’t change theestablished path’ syndrome, ‘no risk policy’, ‘nonon-proven alternatives’, ‘don’t skip the line’.

(4) Lack of courage in government officials to faceopposition, fear of losing support from theelectorate and accompanying lack of long-termvision (focus only on short term gains), instabil-ity and high turnover of public officials in theirpositions.

(5) Lack of courage by Chief Executive Officers andthe Board of Directors in the private sector, toembrace change or take a long-term view,pressure from stakeholders to increase earningsper share in the short term, fear of losing supportfrom stakeholders if they do not respond to theshort-term pressure for results, frequent turnoverand little time to formulate and implement long-range growth initiatives (Perel, 2002).

(6) The way innovation is introduced is an importantdeterminant and predictor of its likelihood ofsuccess: more incremental innovations have alower impact on hierarchical power relationshipsand as a result, are confronted with smallerresistance and inertia than more radical ones.The ‘dangerous innovations’ that are resisted themost, are those of a disruptive and restructuringnature, rather than those that will only affectfine-tune the functioning of the system (Chris-tensen, 1997).

(7) Rigidity of hierarchical structures and lack ofmanagement for results; instability in the rules ofthe game (arbritrariness, favoritism), corruptionand lack of transparency; poverty and political

Figure 5. Factors affecting innovative performance.


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struggle; centralized bureaucracies, top-downpolicies and government control for the sake ofcontrol.

• Theme 3: A Glimpse of the Current Situation inSeveral Countries—Challenges and Opportuni-tiesOur findings show that there are several majorchallenges and potential opportunities associatedwith innovation and creativity-supporting initiativesand policies. Intrinsic in this is the role as well as thesuccesses and failures of the Multilateral Develop-ment Banks (MDBs). There are also challenges andopportunities facing the private sector, emanatingfrom the high and increasing rates of technologicalchange, globalization, intensity of rivalry, and dilu-tion of national franchises:

(1) There is great potential for creativity, innova-tion and competitiveness at the individual level,but there is a lack of public policies to fosterand take advantage of this capacity.

(2) In some countries, government policies, whileproviding no direct financial assistance, wereinvaluable in providing a market for productdevelopment and subsequent sales opportuni-ties.

(3) Usually in developing countries, policies aregenerated top-down without any consensus,debate or agreement with civil society. Themain emphasis is control. There are no chan-nels for participation and formulation of abottom-up policy. In this regard, the Congressis not even a representative body of the civilsociety. Also, there is no transparency, noraccountability.

(4) In some European countries there is an urgentneed for stronger links of education andresearch with the real economy, and integrationinto the European research and innovationsystem.

(5) In many countries the private sector is regardedas more competent in terms of creativity,innovation and competitiveness than the publicsector. The public sector seems to be paralyzedbecause of norms and regulations that blockdevelopment. The political indecisivenessaffects all the economic activities and makeseconomy sluggish.

(6) Universities are excellent, shining examples ofpublic sector environments that fuel creativityand innovation.

(7) Many countries have not fully tackled in a waythat policy and practice merge into a singleunified outcome. Most of the examples cited inrelation with this type of cooperation hasoccurred in pockets throughout the publicsector with the initiative being taken by individ-

uals, rather than through organizationalplanning.

(8) The major challenge is for less developedcountries because of lack of adequate capacityand infrastructure necessary to transform visioninto action and lack of a continuous and stabledynamic that foster creativity, innovation andcompetitiveness.

(9) Most developing countries still think in state-centered terms. Public sector too dominant, tooprone to doing wrong things for political oreven personal reasons. Private sectors aredisorganized; companies have poor manage-ment and strategic planning skills. Public andprivate sectors do not collaborate enough; theyoften play the blame game. Governmentspolicies often help the politically powerfulrather than expose firms to competitive pressurein order to build strength to compete on a globalbasis.

(10) Overall, in developed as well as in developingcountries, no specific, explicit and systematicmeasurements for creativity, innovation andcompetitiveness seem to be undertaken. Usu-ally, social and economic performanceindicators are considered as proxies to measureperformance in this field.

(11) Multilateral Development Banks (MDBs),although traditionally dominated by paradigmsand ideological postures that resist debate andchange, and therefore, creativity and innova-tion, have independently and also incollaboration, launched numerous initiatives tofurther promote competitiveness and higherlevels of development in their borrowing mem-ber countries.

Lately, the advances of Internet technology and highspeed connectivity have allowed member countriesto exchange vital information to participants invarious programs and projects, promoting distancelearning in a modern and cost effective manner.However, more work is needed in MDBs to measuretheir effectiveness in terms of fostering creativity,innovation and competitiveness at the national andregional levels.

Recommendations for Action based on Field Study

To learn one must be humble.(James Joyce, Ulysses)

We compile and categorize thematically below, theresponses of the public and private sector practitionerswe collected via our field research to provide thefoundation for recommendations to practitioners and aroadmap for future research.

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The Role of the Public Sector in Promoting Creativity,Innovation and Competitiveness (CIC)

The role of public sector innovation is decisive ascatalyst and accelerator of social and economic devel-opment. Public sector CIC promotion can lead tobetter, more efficient and cost-effective ways ofmanaging public sector operations and social welfarefunctions; it can as well help improving marketfunctioning in competitive environments.

The opportunity cost for the public sector notpromoting the creation of a more competitive environ-ment is enormous, since the social and economic costsassociated with obsolete or outmoded ways of conduct-ing and managing business are substantial, especiallyin less developed economies.

The role of the government in a developing countryis much more critical, as often the private sector has noways and means to raise venture capital. Thereforeunless the government steps into the vacuum there isvery little chance that the innovations will ever see thelight of day.

Usually, less developed countries lack adequatecapacity and infrastructure necessary to transformvision to action; whereas in industrial countries theseconditions are met to a greater extent. The public sectorcan act as a catalyst to CIC in developing countries forit can promote/assume works in areas where the privatesector finds inadequate profit incentives for engage-ment.

The modus operandi of the public sector can be afunction of the degree of market development, itsfunctions and the demand for public/private sectorservices. A well-developed public infrastructure shouldideally allow for CIC promotion from both public andprivate sector entities.

Government active support for innovations is essen-tial in developing countries due to the political andeconomic system inadequacies that might disturbedinnovative technology initiatives in public or privatesector.

The challenge is to have reasonable ways for thescience developed in the public sector to migrate tocommercialization when warranted, without the publicsector becoming effectively competitors with theprivate sector. The public sector should not beoperating as competitor to private sector entities,crowding out entrepreneurial initiative.

The public sector requires more aggressive policiesto foster creativity in its own public management,promote the innovation within the government andraise its own competitiveness in the process.

• The Public Sector could promote CIC in severalways

(1) Creating an environment which supports CIC. Itincludes issuing policies, norms and regulationsthat enable CIC; giving awards and incentives

like tax breaks, insurance and other favorableconditions to take advantage of internationalexperience; providing effective stimuli toresearch and scientific development throughinvesting resources adequately.

(2) Using the purchase power of the government(around 30% of Gross National Product in LatinAmerican countries) to foster competitiveness(besides efficiency and transparency).

(3) Building social safety nets for those that failwhen seeking to invent, as well as supportmechanisms for those that need additional sup-port to invent/innovate.

(4) Acting over those market failures, where theprivate sector can not act alone because of lack/asymmetry of information or problems of scale.In this regard, the promotion of non-traditionalexports or subsidies to technological innovationin small and medium enterprises could be citedas examples.

(5) Trying to commercialize research generated bythe public sector, e.g. from National AeronauticSpace Administration, federal laboratories, orDepartment of Defense.

(6) Building an efficient innovation system, the mainareas of focus are related to research andinnovation networks, innovation and techno-logical transfer programs (scientific andtechnological parks), innovative SMEs andimproved capacity of economy to absorb R&Dachievements.

(7) Making available resources for fundamentalresearch. These monies do create an environmentthat is less inclined to be driven by economicsand be very focused on ‘pure’ science.

Public-Private Sector Partnerships to Promote CICIn developed countries where markets are functioningmore efficiently private sector participation in ICC ismore pronounced. Besides, partnership arrangementsbetween public/private sector are as such as to allow ahigher degree of private sector participation in certainoperations/areas despite the relatively higher level ofcompetitiveness and/or lower profit margins (in thesecountries, private firms often have an incentive toengage in partnerships with the government for otherreasons such as to acquire a larger market share, or justfor marketing, advertisement and promotion). More-over, in developing countries with less stable socio-economic and/or political structures concerns overgovernment’s credibility often act as a deterrent forprivate sector participation

Some areas in the private sector require support fromthe public sector to improve creativity, innovation andcompetitiveness. However, it seems that what theprivate sector needs, more than public/private sectorpartnerships, is a more competitive and transparentpublic procurement process (to fight corruption) and


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incentives for university/industry as well as domestic/foreign, public/private sector partnerships.

Support new projects and initiatives in the privatesector (i.e. research in applied sciences or manufactur-ing technological development, etc.). In the last case,there is the possibility of partnerships between publicand private sector but, only in those areas in which theprivate sector is weak and in those areas reallyinnovative.

Support for new projects and initiatives in the privatesector (i.e. research in applied sciences or manu-facturing technological development, etc.) throughpublic/private partnerships is desired mainly in thoseareas in which the private sector is lacking innovativecapacity and creative competence and especially forthose areas where discontinuous innovations (poten-tially disruptive in nature) are the main pursuit.

Description of criteria to identify potential initia-tives/projects to conform partnerships between publicand private sector, criteria and process for selecting thecandidates in the private sector, and conditions inwhich public venture capital should be involved:

Criteria for identifying potential initiatives/partners

(1) Government priority areas.(2) Potential practical application and benefit for

society and/or economy.(3) Low cost or profitable project.(4) Long term project.(5) Candidates with probity and enough financial

means.(6) Willingness to change.(7) Competitive spirit.(8) Futuristic vision.(9) Complementary experience and resources.

(10) A good record of accomplishments.(11) Their contribution to innovation.(12) Expertise and market access.

Process(1) This is a very important and yet paradoxical

process, to pick a winner there is almost anelement of gambling that comes into play, yet thepublic sector has a responsibility to the communitynot to gamble with public funds.

(2) The process would need to involve the communityand the business sector in order to ensure that itwas transparent. The rules and procedures wouldalso have to be extremely clear as would theinvolvement of any public official. This would inparticular be extremely critical should the productprogress to the next stage where venture capitalwould be involved.

(3) An ‘Experts Committee’ (EC) could be establishedfor selection, oversight and evaluation. A ‘QualityAssurance’ Committee/Group could complementthe functions of the ‘EC’ to review the quality/

progress at a later stage once relevant works havebeen initiated. Financing arrangements are alwaysparticular to the specific operations in question.

The Role of the MDBs in Promoting CICThe role of MBDs in supporting public and privatesectors innovation is decisive. MDBs could:

(1) promote the formulation of national policies andaction plans for CIC with short and long termgoals, with challenging but feasible objectives;train and stimulate the staff responsible of imple-menting projects and activities in private andpublic organizations; allocate the needed resourcesand tools for performing the appropriated actions,and provide continuous technical assistance andsupervision;

(2) create the conditions to foment a climate whereinnovations in developing nations could be broughtto fruition;

(3) help developing countries get the underlyingeconomic and regulatory policies right so thatconditions to encourage new ventures and innova-tion can work within the private sector;

(4) share global best practices with developingnations. Strengthen the private sector institutionsso that business can make a greater contribution topolicy decisions;

(5) contribute to eliminate trade barriers that affect thedeveloping countries and destroy the possibility ofany innovative development;

(6) disseminate information, knowledge and success-ful experiences on CIC among member countries;

(7) promote CIC through agreements and treaties andincorporate them in the policies for development.

To be able to contribute much more effectively topromote CIC, MDBs should:

(1) incorporate CIC in its own management. It isnecessary that MDBs are co-responsible (with thecountries) for the results of its cooperation. Thisresponsibility should be measured in terms ofnational development and citizens’ wellbeing inthe target countries;

(2) be organizations more creative and more innova-tive, less rigid and less bureaucratic;

(3) support the governmental projects in which thegovernment holds strong leadership and ownershipof the plan. In other words, no financial supportshould be provided to the government that has noindependent ownership of development ideas orsustainable leadership;

(4) give more flexibility to the countries in findingtheir better path to development (no predefinedmagic formulas);

(5) being more accountable for their achievements interms of national productivity and competitive-ness.

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Conclusions and Future ResearchThe empires of the future are the empires of themind.

(Sir Winston Spencer Churchill)

In this chapter, we have attempted to address thefollowing issues concerning for-profit and not-for-profit entities:

(a) when, how, and why creativity and innovationoccur;

(b) how and why creativity triggers innovation andvice versa; and

(c) what are the connections and implications forcompetitiveness of the presence or absence ofcreativity and innovation using empirical findingsfrom both the public and private sectors.

We combine literature sources (including those of theauthors) as well as field interviews to enrich ourchapter with current academic and practitioner insightson the practice and implications of creativity andinnovation from the perspective of competitiveness.

We believe that competitiveness is a product and afunction of creativity and innovation stocks and flowsthat are determined and modified through diversetypes, ways and means of learning (top-down, bottom-up, by doing, by analogy, by succeeding, by failing,trans-national, domestic, via skills exchanges, technol-ogy-leveraging, partnerships, etc.) as well as individualcognition and inventiveness (the ‘when’, ‘how’, and‘why’ of creativity and innovation) (see Figs 6 and 7).

In Fig. 6, we show the participatory and synergisticinteraction between the public sector, the private sector,and key institutions for collaboration like universities,research institutions and Non-Governmental Organiza-tions (NGOs) in building strategic alliances towards theobjective of higher levels of competitiveness in devel-oping countries. In this context:

• governments are accountable for establishing a stableand predictable political and macroeconomic envi-ronment, issuing transparent policies and enforcinglegal and property rights, facilitating cluster develop-ment, creating a business environment with lowtransaction costs, and supporting and giving incen-tives to creativity and innovation;

• enterprises have to mount competitive strategies,develop networks and clusters for achieving effi-ciency (social capital), increase the intensity of theirtechnological effort (more resources for R&D), buildnew capabilities and skills (human and intellectualcapital) and develop a modern infrastructure. Suppli-ers of physical and service inputs and infrastructurehave to meet international standards of costs, qualityand delivery;

• universities and research institutions have to aligncurricula to business needs and craft public andprivate partnerships to develop new capacities and

skills in public and private sectors. NGOs shouldserve as enablers, catalysts and accelerators of publicand private partnerships.

The top-down institutional learning complementingbottom-up entrepreneurial learning act as enabler,catalyst, and accelerator of economic development andconvergence across developed and developing coun-tries as well as cross-pollination and transfer oftechnology and best practices across developed anddeveloping countries as well as public and privatesectors, universities and research institutions andNGOs (see Figs 3 and 6).

We find from our field research that innovation andcreativity are becoming increasingly important forpublic sector reforms as well as private sector survivaland prosperity, given the current challenges andopportunities facing public and private sectors aroundthe world. Some of the challenges and opportunitiesfacing the public sector with serious implications forthe private sector as well, encompass the following:

(a) shrinking budgets and shifting demographics withageing populations;

(b) the higher-rent-yielding tax base of knowledgeworkers attaining increased mobility;

(c) increased pressures for accountability and transpar-ency driven by privatization, globalization, and anincreasingly informed and sophisticated voterbase;

(d) increased pressures on and from the private sectorto become more competitive, demanding in returna more competitive public sector;

(e) and last but not least the role of the MultilateralDevelopment Banks and especially the Inter-national Monetary Fund which are relentless indemanding gains in efficiency and transparency inpublic sector policies, structures and practices.

Competitiveness is also a way of looking back as itreflects the past achievements of creative genius andinnovative energy as well as shaping the future byproviding the foundation and skeleton for emergingprivate and public sector endeavors. Moreover, at thepresent, the manifestations of creative and innovativeendeavor serve as auguries of the emerging horizons ofsocio-economic and institutional development andlearning which further interact with individual learningand creativity in an never-ending spiral process (seeFigs 5, 6, and 7).

We also find that creativity and innovation do notalways lead to enhanced competitiveness (at least in theshort- to medium-term) (Carayannis, 2001a, 2001b,2002).

All in all, our foray into the domain of creativity andinnovation as they relate and impact competitivenesshas identified many areas of interest that warrantfurther focused research to better understand and more


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clearly exemplify and articulate the key drivers anddimensions (content, process, context, impact) ofcreativity and innovation and the role of learning in thisprocess (Carayannis, 2002). In particular, we feel that it

would be useful to empirically map the nature anddynamics of learning and meta-learning along theCIC2Helix (Fig. 5) regarding both the public and theprivate sectors.

Figure 6. CIC learning and institutional linkages.

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A better understanding of such processes couldresult in enhancing and advancing the effectiveness ofgenerated knowledge and the efficiency of knowledgetransmission and absorption. In so doing, knowledgeeconomies of scale and scope could be attained atincreasingly higher levels, allowing for more to beaccomplished with less, faster, cheaper and better(Carayannis, 1998a, 1998b, 1999a, 1999b, 2001a,2001b, 2002).

These gains can be manifested in diverse ways at themicro, meso and macro levels (see Fig. 5), namelyhigher standards of living, more competitive firms,more robust economies, and accelerated and moresustainable development trends (see Figs 5, 6 and 7).Indeed, one could identify both a challenge and anopportunity for private and especially public sectorpolicy makers and managers with regards to the risksand possibilities inherent in the three figures we referto. Specifically, Figs 5, 6 and 7 capture and reflect upona combination of dynamic, complex and powerfulforces at play: human cognition, individual creativity,organizational productivity, and national competi-tiveness as well as institutional inertia, political short-sightedness, and market as well as government failures.Understanding how to better manipulate and leveragethose forces can result in spectacular results (to someextent, success stories in socio-economic developmentsuch as Singapore and South Korea may be cases inpoint), while attempting to work against those forcesand trying to suppress them can lead to dangerous andunsustainable regimes of poverty and autocracy. Pro-viding rewards and incentives to foster creativity andinnovation and even rewarding failure may be a strongenabler, catalyst and accelerator for creativity andcompetitiveness.

In our other chapter on discontinuous and disruptiveinnovations, we continue our study and analysis of thenature and dynamics of the relationship betweencreativity and innovation and when, how and whyquantum leaps in creative destruction occur followingthe Schumpeterian line of reasoning (Schumpeter,1934, 1942).

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Dacey, J. S. & Lennon, K. H. (1998). Understandingcreativity: The interplay of biological, psychological, andsocial factors. San Francisco, CA: Jossey-Bass.

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Appendix I: Field Research QuestionnaireQuestions: Please respond by return email within ten days of receipt and email your responses to:[email protected]

(1) Do you think that there is a role for public sector in fostering creativity, innovation and competitiveness?

(2) According with your experience, what are the main factors that act as catalysts or inhibitors for creativity,innovation and competitiveness in the public and in the private sector?

(3) What is your definition of ‘innovation?”

• Inventing something new [ ]• Generating new ideas only [ ]• Improving something that already exists [ ]• Spreading new ideas [ ]• Performing an existing task in a new way [ ]• Following the market leader [ ]• Adopting something that has been successfully tried elsewhere [ ]• Introducing changes [ ]• Attracting innovative people [ ]• Seeing something from a different perspective [ ]• Other (please, define) [ ]

(4) What kind of benefits are expected when public sector plays an active role in promotingcreativity, innovation and competitiveness?• Benefits to government/public sector [ ]• Benefits to society [ ]• Profit

(5) How could the public sector measure the benefits of fostering creativity, innovation and competitiveness?

(6) To foster creativity, innovation and competitiveness, should the public sector play a rather passive role(formulating policy, enabling legislation and regulations) or should the public sector play a more active rolein new ventures that foster creativity, innovation and competitiveness?

(7) Should the public sector foster creativity, innovation and competitiveness from outside (by partnering and/or supporting private sector in new ventures) and/or from inside (by developing its own initiatives)?

(8) How the public sector should select the winners in the private sector for partnering or receiving support inprojects and programs promoting creativity, innovation and competitiveness? What should be the criteria?How should be the process? In what stage of the process a venture capital should be involved?

(9) How does the current policy in your country impact in creativity, innovation and competitiveness?

(10) Do you see any difference in the role of government/public sector with respect of creativity, innovation andcompetitiveness in developed VS developing countries?

(11) Do you measure and benchmark creativity and/or innovation in your organization and, if yes, how?

(12) Do you benchmark creativity and/or innovation in your organization to that other organizations and are theypublic, private or both?

(13) Would you consider the public or the private sector more competent in terms of creativity and innovation,and why?

(14) What examples could you mention to describe the dynamic of creativity, innovation and competitivenessin your organization/country?

(15) What should be the role of the Multilateral Development Banks in supporting creativity, innovation andcompetitiveness in developing and developed countries?


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Appendix II: Field Research Respondents & AffiliationsCountries

Argentina, Australia, Colombia, El Salvador, Greece, Jordan, Mexico, Nicaragua, Peru, Japan, U.S.

Contributors from Field Study and Affiliations

Partner, venture capital firm (3)Partner, consulting firm (2)Coordinator, E-Government Program—Office of the President (1)Consultant, Industry & Technology Ministry (1)Secretary, Office of the President (1)Secretary, of Government (1)Director, Chamber of Commerce (1)Procurement official—Technical Secretary—Office of the President (1)Principal, Private Company (1)Consultant, International Development Agency (1)International development consultancy (1)Official, Private Bank (1)Official, Multilateral Development Bank (2)Official, University (1)Official, European Commission (2)CEO, High Tech Firm (1)

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Innovation Tensions: Chaos, Structure, andManaged Chaos

Rajiv Nag1, Kevin G. Corley2 and Dennis A. Gioia1

1 Smeal College of Business, Penn State University, USA2 College of Business Administration, University of Illinois, USA

Abstract: This chapter presents a framework for understanding the tensions that underlie anorganization’s ability to manage innovation effectively in the face of a turbulent competitiveenvironment: (1) the fundamental tension between the desire for structure and need for creativechaos, and (2) the on-going tension between technology-push and market-pull approaches toinnovation. We explore the nature and boundaries of these tensions and characterize them as fourdistinct ‘innovation contexts’. Using one high-technology organization’s struggle as an example,we discuss the notion of ‘managed chaos’, a concept that helps understand the role of innovationin the maintenance and change of an organization’s identity.

Keywords: Innovation; Chaos; Structure; Organizational identity.

IntroductionOver the course of the last generation, innovation hasprogressed from being a ‘nice to have’ to an ‘ought tohave’ to a ‘must have’ in many organizations. Quantityand quality of innovation have gone from mainly beingperformance yardsticks in R&D departments and newproduct development teams to becoming the raisond’etre for many of the organizations in which R&Ddepartments and NPD teams are housed. The rapidadvance of technology is the root cause of this shift, ofcourse, and the need for increased follow-on techno-logical innovation has been the widespread response.We now see a trend in which more and moreorganizations are treating innovation as a ‘critical tohave’—i.e. as a survival imperative.

This trend is perhaps most evident in those industriesmost violently buffeted by technological change andtechnological competition (where staying on the bleed-ing edge is itself a business imperative). Yet, thewriting on the wall suggests that this heretoforelocalized trend is quickly spreading to a much widerdomain. Business executives in such contexts can nolonger assume that they are primarily competentmanagers of the status quo or shepherds of incrementalchange. Instead, they need to assume a considerablymore complicated role that places a premium onmaintaining the dynamic balance between efficientlymanaging resources to increase shareholder value and

effectively innovating to remain competitive and insuresurvival.

A theoretical focus on this dynamic balancing acthighlights several significant internal tensions asso-ciated with innovation, especially in high-techorganizations and industries. Perhaps most evident isthe key tension between the creation of stable organiza-tional mechanisms to exploit a particular businessmodel and the subsequent destruction of those samemechanisms and models to cope with the ever-changing requirements of a highly competitiveenvironment (March, 1991). Given the acceleration ofthe latter process over the last decade, understandingthese innovation tensions becomes paramount forunderstanding the survival and growth of high-techcompanies in the modern age.

Innovation most often has been studied by examin-ing the implementation of creative ideas within anorganization (Amabile, 1988)—usually the imple-mentation of ideas that are not necessarily new to agiven domain, but are new or innovative to the focalorganization itself (Ford, 1995). In our view, however,this approach does not adequately account for the mostdynamic aspects of organizational innovation. Inparticular, it does not account for the internal tensionsarising from a turbulent competitive environment. Thesuccessful exploitation of known technologies requiresthe creation of stable organizational mechanisms that

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enhance the level of coordination. Such mechanismsrequire much lower levels of flexibility and risk-takingthan the creative exploration, development, and imple-mentation of new technologies (which also tend toengender a great deal of turmoil within an organiza-tion). When exploitation and exploration processes arenecessarily juxtaposed, as they now so frequently are,a predictable tension arises.

We characterize this juxtaposition as a tensionbetween structure and chaos (a set of provocative first-order labels used by informants in one of our researchprojects, but which arguably have application to amuch wider domain in the study of innovation).Structure/chaos tension makes the nature of organiza-tional innovation different for exploitative approachesand explorative approaches to organizational well-being and survival. Furthermore, and importantly, thisis not an either-or choice for most organizations, butrather a continuous dialectic that is closely linked tochanges in the organization’s environments.

Exploring an essential feature of this structure/chaostension reveals a different sort of innovation tension,one that emerges as the organization attempts tobalance the tendency to become a more inwardlyoriented ‘technology push’ organization versus theneed to become a more externally responsive ‘marketpull’ organization. A pure technology push approach ischaracterized by internal creative processes that arefocused on designing and developing cutting-edgetechnology as a basis for advancement and achieve-

ment, with the organization pushing the technologyinto the marketplace without first identifying a marketneed. A pure market pull perspective, on the otherhand, focuses less on the cutting-edge advancementprovided by a technology and more on the technology’sability to fulfill a need in the marketplace that willresult in sales and revenue as technology creation anddevelopment is pulled by market demand. We believethat this technology push/market pull dichotomy cap-tures a common tension in many contexts involvinginnovation, perhaps especially in R&D groups andhigh-tech organizations devoted to R&D. Furthermore,we have found that this technology push/market pulltension implicates an organization’s very identity andaffects strategic issues such as positioning within themarketplace and relationships with key stakeholders.

In this chapter, we extend traditional notions oforganizational innovation in high-tech companies andindustries by examining the implications of juxtapos-ing these two tensions. We argue that organizationalstructure/chaos and technology push/market pull ten-sions are a defining part of current organizationalpractice and, therefore, need to be more closelyexamined to further our understanding of moderninnovation processes. When these two tensions arearrayed in a 2 � 2 matrix (see Fig. 1), each of theresulting four cells represents a distinct context inwhich organizations can attempt to nourish innovativepractices (Structure-Push, Structure-Pull, Chaos-Push,and Chaos-Pull). We explore the specifics of each of

Figure 1. Juxtaposing the two innovation tensions.

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Rajiv Nag, Kevin G. Corley and Dennis A. Gioia Part VIII

these ‘innovation contexts’ and discuss managerialpractices necessary to facilitate innovation within eachcell.

Additionally, to account for the dynamic environ-ments with which organizations must cope, weexamine the issues and implications involved inmovement among the different innovation contexts. Itis no longer feasible to think of an organizationsustaining its innovative practices within a static world.Consequently, organizations must assume that theirinnovative contexts are in flux and, therefore, mustconduct their innovation efforts under changing cir-cumstances. We examine this new state of affairs byconsidering what it takes to manage these tensions in away that transitions from one cell to another are notonly possible, but also effective. To demonstrate therelevance and usefulness of this framework, we presenta case example of one organization’s attempt tomanage these tensions and its movement across cells.Our analysis highlights the issues faced by managers,as well as the implications of these tensions fororganizational survival and growth.

We conclude our discussion of these innovationtensions with the explication of a model that articulatesthe main challenges faced by organizations driven tomake innovation a vital part of their strategic efforts.Dealing with these challenges inevitably raises funda-mental questions about an organization’s identity. Thekey feature of this model is the notion of ‘managedchaos’, a concept that suggests a way to manage thesetensions by surfacing and questioning basic assump-tions about the role of innovation in the maintenance oralteration of organizational identity. We posit thatmanaged chaos is exercised not just by destabilizingexisting organizational structures and routines, but alsoby reconsidering innovation’s raison d’etre in manyhigh-tech companies. We suggest that by workingtowards a state of managed chaos, an organization canmaximize the potential found in the intersection of thestructure/chaos and technology push/market pull ten-sions of organizational innovation.

The Fundamental Tensions of OrganizationalInnovationBefore delving into the complexities involved withunderstanding the dynamic aspects of organizationalinnovation, it is first necessary to understand eachtension better and to explore the details of each of thefour ‘ideal contexts’ that arise from their juxtaposition.We begin by examining the Structure-Chaos tension inmore depth, followed by the Push-Pull tension.

Structure-Chaos TensionOrganizations are composed of individuals who bringtogether divergent skills and resources that need to beintegrated to achieve a common goal. This workingpremise is as complex as it is simplistic and obvious.Lawrence & Lorsch’s (1967) dimensions of differ-

entiation and integration contain an elemental dialecticthat challenges the very act of organizing. Thecomplexities of any modern organizational environ-ment require functional differentiation and division oflabor. Each function/department specializes in manag-ing its own part of the environment. To do so, eachfunction/department needs the autonomy to framethe nature of its sub-environment and develop thenecessary organizing mechanisms and structure todeal with it.

Uncontrolled differentiation, however, can result inan organization’s sub-units pulling away from eachother by pursuing their own independent agendas. Toreign in the different sub-units, an organization needsto develop integrating mechanisms that impose acommon sense of purpose and some standard operatingprocedures on the sub-units. Traditionally, the topmanagement of an organization plays the integrativerole by setting up control systems and creating thecontext for the development of a common organiza-tional culture. These integrative mechanisms, however,can result in excessive centralization of decision-making and loss of local flexibility. This problem isexacerbated in turbulent environments, where the needfor fast and frequent differentiation to face changingexternal complexities must co-exist with the integrativeneed to work together with the rest of the organization(c.f. Brown, Corley & Gioia, 2001).

Another dialectic that is fundamental to organizingis the need to achieve a workable balance betweencurrent efficiency and future effectiveness. In otherwords, organizations face two divergent modes ofexistence—one that is built on the successful exploita-tion of existing technologies, markets, and products,and the other that is built on a continuous explorationof new technologies and market opportunities. Suc-cessful exploitation of a specific technology requires anorganization to focus on the selection, execution, andimplementation of a limited set of technologies and/orbusiness models to maintain a stable equilibrium(March, 1991). Thus, a successful exploitative strategywould necessitate the creation of stable organizationalstructures and routines that persist over an extendedperiod of time (Nelson & Winter, 1982). Organiza-tional resources and energies are focused onmaintaining stability and predictability in internal andexternal environments. Although new ideas mightemerge, these are most likely aimed at improving theexisting technology and enhancing the efficiency of thecurrent organizational structure and processes.

An organization engaged in exploration follows adiametrically opposing approach, however. Its strategyand structure are characterized by variation, experi-mentation, uncertainty, and risk (March, 1991).Organizational resources and energies are focused onbringing new data into the organization, turning it intouseful information, and putting it to use with otherorganizational knowledge for the creation of new

Innovation Tensions: Chaos, Structure, and Managed ChaosChapter 4

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structures and processes. Change is treated as a naturalpart of the organization’s culture, while routines (to theextent that they exist) are often focused on facilitatingsuch change. Explorative organizations often can takethe form of ‘organized anarchies’ (Cohen, March &Olsen, 1972) that have unclear technologies andunstructured decision-making processes characterizedby solutions looking for problems.

These two elemental rivalries—‘differentiation ver-sus integration’ (Lawrence & Lorsch, 1967) and‘exploitation versus exploration’ (March, 1991)—mostaffect the design of an organization, especially whereinnovation is at issue. An organization that focuses onstable technologies and predictable routines and mech-anisms to reconcile or resolve these rivalriesrepresents, in our terms, an emphasis on ‘structure’. Anorganization that focuses on the proactive search fornew technologies and employs inventive mechanismsand decision-making styles to reconcile these rivalriesrepresents an emphasis on ‘chaos’. Although thisstructure-chaos dichotomy might be somewhat of anoversimplification, it provides a useful conceptual basisfor examining different approaches to organizing forinnovation.

Push-Pull TensionAlthough structure-chaos tensions are vital for under-standing the organizing process, they alone cannotprovide an explanation for the most common difficul-ties organizations face in trying to become innovative.In practice, the tension between ‘technology push’ and‘market pull’ orientations tends to be more funda-mental and to present the more difficult challenge foreffectively managing organizational innovation—mainly because the technology push, market pull issuemore strongly implicates organizational identity.Although the structure-chaos tension could bedescribed as raising questions about ‘how’ things aredone in an organization, the push-pull tension might bebest characterized as provoking questions about ‘who’the organization thinks it is—which is obviously amore deeply-rooted and central issue.

In high-tech companies, tension emerges as organi-zations attempt to balance the tendency to become aninwardly oriented ‘technology push’ organization (onethat values creative and innovative technological inven-tion over practical relevance and marketability) againstthe need to become an externally responsive ‘marketpull’ organization (one that values developing techno-logical inventions to meet market needs). The notion oftechnology push presumes the precedence of techno-logical innovation for its own sake, often on theassumption that new technologies can create a newmarket if one does not exist. If an organization seesitself as a ‘technology company’ (and many high-techcompanies use this label as part of their self descrip-tion), the tendency to focus on basic research orinnovative product development can be overwhelm-

ingly strong (‘It’s who we are as a company!’). Then,if the organization wants to profit from the technologyit develops from this strong internal (and identity-consistent) focus, it must actively push the technologyout into the marketplace and try to discover or developa market need.

Alternatively, the notion of market pull is charac-terized by technological design and developmentfocused on serving an identified need or filling a nichein the market (thus the idea of the market ‘pulling’ thetechnology out of the organization). In this case, theorganization brings its technical resources and capabil-ities to bear on a market problem to be solved.Innovative capacity is directed toward the creativesolution of externally identified needs. The organiza-tion does not need to spend as much time or as manyresources selling the technology, but also most likelyhas less discretion in designing and developing thespecific aspects of the technology itself.

The Organizational Identity IssueAs noted, the difficulty for the organization in adoptinga push versus pull orientation is found in the strong tiesthis tension has to the organization’s identity. Organiz-ational identity can be thought of as the shared theorythat members of an organization have about who theyare as a collective (Stimpert, Gustafson & Sarason,1998), or as the perception of the organization held byinsiders that helps to answer the question ‘Who are weas an organization?’ (Gioia, Schultz & Corley, 2000). Ifkey members of the organization see their identity asbeing a collection of top-tier scientists dedicated to theproduction or advancement of scientific knowledge,then the notion of market pull, or ‘science in the nameof profits’, is a tough sell and difficult to implement.Likewise, if the members of the organization are atheart business-oriented and see profit-generation as thedriver of R&D decisions, the notion of ‘science forscience’s sake’ seems not only foreign, but downrightdangerous for the organization’s survival. A furtherdifficulty might arise because often the organization issplit in its desires, with top echelons of the organiza-tion being more market pull oriented and the lowerechelons being more technology push oriented (e.g.managers versus scientists/engineers)—a case wherean organization might be said to have ‘multipleidentities’ (Pratt & Foreman, 2000).

In principle, neither pure state appears particularlyhealthy, especially over time. Pure technology pushmight lead an organization to be renowned for thetechnological advancement of the field, but it canendanger the company’s chances at survival (especiallyif there is inadequate internal marketing expertise).Pure market pull, on the other hand, can result in betterbusiness standing and more profits, but might easilyrelegate the company to the status of a ‘technologyreactor’, always behind the curve in terms of advancingtechnologies that can capture the markets’ interest

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(especially when the timeline for patents is con-sidered). Thus, some sort of middle range approachwould seem to be most effective, because an organiza-tion able to balance this tension would be able to takeadvantage of the benefits on both aspects of the tension.Because technology push/market pull tension is soclosely linked with organizational identity, however,the inertial pressures pushing an organization awayfrom the center and toward one end of the dichotomycan be great.

Juxtaposing the TensionsFigure 1 provides a graphical representation of fourprototypical innovation contexts that arise by arrayingthese two tensions in a 2 � 2 matrix. The horizontaldimension represents the tension between Structureand Chaos, or how an organization answers thequestion ‘How do we do things?’ The vertical dimen-sion represents the tension between Technology Pushand Market Pull, or how the organization answers thequestion, ‘Who are we?’ Of course, Fig. 1 representsan oversimplified visualization of the tensions under-lying organizational innovation. The bifurcations

between Structure/Chaos and Technology Push/MarketPull represent prototypes, whereas many organizationsmight find themselves straddling these extremities. Toemphasize the details of these underlying tensions,however, we present a graphical description of the purestates (details in Fig. 2), along with a brief descriptionof each innovation context.

Chaos, Structure, and Technology PushAs noted earlier, technology push involves an organiza-tion developing technologies and products withoutexpressed consideration of immediate or specificmarket requirements. The tensions involved in manag-ing structure/chaos tradeoffs within a technology pushorientation is one that has implications for the breadthand flexibility of the R&D agenda and ensuingorganizational mechanisms. A technology push R&Denvironment favoring a chaos approach is charac-terized by apparently haphazard endeavors focused onsolving a loosely-articulated scientific problem. Thereis usually a high level of ambiguity as to what researchquestions need to be asked and how the likely solutionsare to be generated and pursued. The search for

Figure 2. Inchoate’s underlying assumptions and change path.


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scientific breakthroughs is often serendipitous and isaided by an enhanced employee autonomy and decen-tralization of decision-making. Typically, adequateresearch funding is available to pursue a wide range oftechnological approaches. Politics, however, can befierce, as autonomous units vie for the funding andattention from the best technology people.

A technology-driven organization that favors astructured approach is one that usually channels itsfunding and efforts into specific technologies. Thisfocused technology push can arise for historicalreasons (‘this what we are good at’ or ‘this is what wedo here’), for external reputation reasons (‘this is whatwe are known for’), or even for prestige reasons (‘thisis what will bring us visibility’). The focal technolo-gies, however, often involve long term projects wherecentrally controlled processes and procedures ensurethat resources are applied to the right efforts at the righttime. Close controls and monitoring are usually inplace to guarantee that projects maintain efficiencies,possibly to the detriment of the groundbreakingdiscovery that a less focused process might produce.

Chaos, Structure, and Market PullThe other aspect of the vertical dimension in Fig. 2represents the market pull orientation. A market-drivenorganization is one that seeks to get closer to itscustomer’s needs, forecast changes in the market, andstay ahead of the competition by pre-empting themoves of other players in the market (Day, 1999). Inshort, the organization is externally aligned to thechanging requirements of the market. As depicted inFig. 1, a market pull orientation can be accompaniedeither by chaos or structure, but with different out-comes.

In a turbulent external environment, a market drivenorganization that favors a chaos approach engages in acontinuous search for new market opportunities. Theinternal organization is characterized by a continuousemergence of new commercial ideas that vie with eachother for top management support. The marketingorientation is most often decentralized across theorganization and the senior management encouragesindependent initiatives. The organization typically doesnot adhere to specific markets for a long term butengages in a continuous search for new and unexploredmarkets that might require completely different busi-ness approaches and technological expertise.

A market-driven organization that favors a structuredapproach is one that successfully positions itself in oneor more markets and follows a leadership strategy. Theemphasis on structure is meant to derive efficienciesfrom cross-pollination of creative ideas and commer-cial opportunities. The emphasis on structure alsoprovides a certain amount of direction and predict-ability to the organization’s developmental projects.Such an organization creates integrative mechanisms(Lawrence & Lorsch, 1967) in the form of well-defined

and widely-disseminated reporting systems and projectappraisal mechanisms. These systems in turn providean opportunity for the transfer of insights and learningfrom one part of the organization to the other. Structureenables a market-driven organization to successfullyexploit product ideas and to maintain a healthy streamof creative initiatives contemporaneously. It thus ableattempts to capitalize on the efficiency benefits ofexploitation and the creative advantages of explora-tion.

Changing Innovation Contexts: A Case Example ofStructure/Chaos, Push/Pull Innovation TensionsAlthough examining the four ‘prototypical’ innovationcontexts provides some insight into better under-standing organizational innovation in high-techcompanies, this static representation does not go farenough. Because modern organizations face rapidlychanging competitive environments and fast pacedtechnological shifts, different innovation contexts areoften required to help cope with these changes.Therefore, in addition to understanding the contextsthemselves, it also is necessary to understand some ofthe issues and implications of organizations movingamong these innovation contexts. To help highlightthese issues and implications, we employ a demonstra-tive case analysis of one organization’s attempts tomove within the innovation tension grid.

Innovation and Change at Inchoate Inc.In this section, we examine the ways in which InchoateInc. (a pseudonym), effectively managed the innova-tion tension between structure and chaos, but struggledin trying to manage the innovation tension betweentechnology push and market pull. It was Inchoate’sdifficulties in managing this more fundamental tensionthat characterized its problems as it attempted to adaptto changes in its competitive environments. Weacquired access to the organization as a group ofresearchers interested in understanding the linksbetween organizational identity, change, and innova-tion. During a span of six months, we carried outextensive one to two hour interviews with top execu-tives and senior managers, and analyzed varioussecondary data sources such as the organization’sintranet, internal corporate communications, andreports in the external media. Before examining thedetails of Inchoate’s change experiences, it is helpfulfirst to present an overview of its history and some ofthe more significant changes it underwent leading up toour study, because tracing Inchoate’s historical trajec-tory is important to understanding its currentinnovation tensions.

Historical RootsInchoate Inc. began as a dedicated research laboratoryof a large American electronics firm over a half centuryago, flush with funds to do basic scientific research. It

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maintained that status until the mid-1980s, when itsparent company was acquired by another large technol-ogy conglomerate that already had an R&D division,whereupon Inchoate was reconstituted as a free-standing R&D corporation wholly responsible for itsown strategic direction and performance. While study-ing the organization, we found that the structure/chaostensions faced by Inchoate Inc. were evident early in itshistory, even if they might be more prominent today.What has changed most dramatically for Inchoate is thenature and scope of its business and the level ofturbulence in the external environment that is in directconflict with its institutional heritage. During its timeas the R&D division of its parent company, Inchoate’skey human resources were composed almost solely oftalented scientists and engineers. In those early years,they were focused on carrying out basic scientificresearch and developing innovative technologies in thefields where its parent company competed. During thisinitial period, the emphasis on the type of researchalternated between basic research and applied research.Despite these fluctuations, though, the research armcontinued to be inwardly oriented, with its researchagenda defined either by the application requirementsof the parent company or by the top management’svisions of the future of science.

The next distinct period in the company’s history,spanning the 1950s and 1960s was a time when theemphasis on basic scientific research gained clearascendance over applied research. During this period,the research arm ventured into uncharted scientificareas that broadened its knowledge base and allowedits scientists to create new fields through their discov-eries. The subsequent period in the early 1970s,however, saw a return to the application of technolo-gies for solving problems for the manufacturingdivisions of the parent corporation. In the late 1980s,when the parent corporation was acquired by aconglomerate that already had its own R&D division,Inchoate was spun off as an independent entity. Thespinoff agreement stipulated that Inchoate wouldreceive five years of (progressively decreasing) fundingto help establish itself as an independent technologycompany. At the end of this period, Inchoate’sscientists and engineers would lose access to theformer parent’s funds for basic research, as well as theclosed, well-defined environment fostered by a parentcorporation. As the weaning period wound down,Inchoate Inc. had to redefine itself as a freestandingorganization, open to market and environmental forces,and responsible for seeking its own revenues and fundsfor further research.

The newly found independent status was a water-shed in Inchoate’s evolution. Suddenly, the samescientists and engineers whose main job for years hadbeen to work on projects that excited their intellectualdesires and aspirations were now forced to search forexternal clients and be sensitive to the needs of the

market. Funding for projects could now come onlyfrom Inchoate’s own success at developing productsand ventures. Only if a proposed project was deemed tohave potential for market success would its own topmanagement approve explorative funding. Science-for-science’s-sake was no longer a criterion for funding.Scientists were asked not only to think of thetechnological and scientific implications of their work,but also the marketing, development, and distributionimplications of any ideas they were pursuing. Theorganization began hiring market-savvy people withbusiness development backgrounds to join projectteams and help ensure that the scientists were focusingtheir efforts on technologies that could be turned intomarketable products. Top management pointedly char-acterized this shift as one that moved Inchoate frombeing a ‘technology push’ to a ‘market pull’ organiza-tion.

This change, and the tensions it produced, plainlytapped a much more fundamental dialectic than thehistorical tension between structure and chaos. Up untilthe late 1980s, the challenge for Inchoate’s precursor(the research arm) was to manage its irregular changesamong basic research (an activity that demanded anunfettered and chaotic internal environment to facili-tate serendipitous explorations in science andtechnology) and applied research (which required aperiodic focus on more structured and orderly proc-esses). From the early 1990s, however, this ‘traditional’dialectic was compounded by the need to transformfrom a new technology-generating orientation into amarket-responsive orientation—a transformation thatrepresented a profound shift in the organization’sidentity.

Identity change at InchoateIt is important to emphasize that most of Inchoate’s keyemployees (including most members of the top man-agement team) were scientists and engineers trained tocarry out trailblazing research without previouslyhaving to think about revenue streams and costefficiencies. Their only major constraint had been anoccasional emphasis on focused applications of tech-nology for meeting specific requirements of the parentcorporation’s manufacturing division. We believe thatthis state of affairs is, in principle, common to manyhigh-tech firms, who often tend to hire mainly fortechnical expertise and then find themselves confront-ing environments that demand more business acumen.

During our involvement with Inchoate, we foundthat it had had great difficulty giving up this orienta-tion, despite the demands of sometimes mercilessmarkets, and despite the very real possibility that theorganization might not survive unless a market pullorientation could be adopted. This difficulty in adapt-ing is directly traceable to the legacy of theorganization and the people who constitute it. Theresearch scientists, as well as the top management


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members (who rose from the scientific research ranks),are predominantly university-trained researchers,whose main reason to work in an organization likeInchoate is to engage in creative research that satisfiestheir intellectual curiosities and justifies their superiortraining. To this day there is still greater value in thecompany for scientific brilliance over commercialviability (and thus also a strong value for a morechaotic approach to discovery and innovation).

Inchoate engendered and exacerbated this chaos in adifferent way by creating many independent businessunits based on loosely defined technology areas. Highautonomy granted to the business units meant theycould chart their own research agendas and hire theirown research staff, although the central organizationhad broad control over profitability. This approach ledto the creation of what senior executives described as‘stovepipes’ out of the business units; i.e. there was noevident integration of resources and even little evidentsharing of knowledge across the units. The onlydistinct prior effort to structure the technology pushorientation in Inchoate that we found was a focus onlicensing of technologies to the government and othercompanies. The licensing process required the develop-ment of organizational mechanisms that would enablethe protection of Inchoate’s intellectual property andallow its appropriation. We also found no evidence thatthe licensing department of Inchoate, which is theorganization’s intellectual property repository, hasplayed a significant role in the creation and dissemina-tion of knowledge from the intellectual property to thebusiness units. For all these reasons, we classify theinitial state of Inchoate (at the inception of theirintended transformation) as being in the Chaos-Technology Push quadrant of Fig. 2.

The removal of virtually unlimited research funds aspart of their (not necessarily welcome) independenceled Inchoate into an era where it had to seek out newrevenue sources that would fund future researchagendas. To do that, Inchoate faced a new reality oflistening to customers in the market who were notnecessarily looking for brilliant technology but solu-tions to their specific problems. The change from‘creators of technologies’ to ‘marketers of solutions’has been a tough one for Inchoate’s scientists andengineers. It has demanded that they change their long-held views on how research should be carried out.More importantly, it has brought into their laboratoriesan unpredictable and dynamic element—the forces andvagaries of the market.

Inchoate’s top management, in its efforts to trans-form the organization into a market-driven organi-zation, tried to impose a ‘structure’ approach todisplace the existing ‘chaos’ approach. This distinctionis especially important when viewed in the light of thestarting point of this transformation (technology push/chaos) to its intended outcome (market pull/structure).Inchoate instigated this intended transformation by

bringing in a new group of senior professionals whowere not scientists and engineers, but rather peoplewho knew market trends and who had businessdevelopment, rather than technological skills. Thesesenior managers were brought in to design andimplement systems that would bring a high level ofpredictability to the research and developmental pro-jects and ensure that they had commercial viability andrevenue potential. Inchoate also adopted a novelstructural initiative that employed two person teamsmade up of a technological specialist and a businessdevelopment professional. These teams would cham-pion research projects that had market potential andsatisfied specific needs of customers.

Innovation, Change, and Inchoate’s ProblemsThe bold arrow in Fig. 2 depicts Inchoate’s path ofintended transformation. The attempted change was notjust in ‘how’ activities were carried out in theorganization (structure/chaos) but also ‘who’ theorganizational members thought they were (technologypush/market pull). Throughout its history, Inchoate andthe earlier research arm managed to shift between basicand applied research while still maintaining theessential character of the organization as based inscientific and technological excellence without muchregard to the commercial utility. The change fromtechnology push to market pull, however, required aredefinition of the fundamental character of the organi-zation and its people. This is the elemental intersectionof tensions that Inchoate’s top management did notsucceed in managing, even though they were able torecognize it.

Inchoate’s lack of success in their transformationattempt can be ascribed, at least in part, to the adoptionof a change in innovation strategy that tried to take adirect leap from chaos/technology push to structure/market pull. Inchoate’s problems with the hoped-forchange were hindered because the change effortimplicated two tensions simultaneously (both the‘how’ and ‘who’ dimensions). The end result of thechange effort was that although Inchoate was able toadopt a number of structural mechanisms to enhancethe commercial viability of its research projects, itcould not transform its fundamental organizationalidentity from technology push to market pull, thusrendering most of the structural changes ineffective.

Innovation as Managed Chaos—The Reverse ‘Z’The model presented in Fig. 3 highlights the keychallenges faced by organizations that are driven by thesimultaneous need to innovate and to alter theiridentities to accommodate dynamic innovation con-texts. These challenges, as previously noted, areexacerbated in high turbulence environments wherethe bases for competitive leadership and survivalconstantly shift (a condition that now arguably appliesto many R&D firms and high-tech industries). In this

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final section we attempt to enlarge our conceptualunderstanding of the link between organizationalstructure and organizational identity in the changingcontexts of innovation by employing a ‘structurational’lens (Giddens, 1984). The underpinnings of structura-tion theory provide a useful general framework foranalyzing issues arising from shifts within the innova-tion tension grid because of its focus on how socialactors continuously and reflexively create and recreatesocial structures. The key insight from structurationtheory is that the interactions among actors serve thedual roles of forming the basis of social structureswhile also creating the means for changing thosestructures.

In brief outline, the process occurs as follows:Initially there is ambiguity among organization mem-bers concerning appropriate modes of thought, action,and interaction. Over a period of time, however, actors’interactions coalesce into structured patterns governedby rules that in turn both reflexively constrain and yetalso enable further action. Structuration, or structuring(to highlight its inherent dynamism), can be viewed asa process by which patterns of interaction betweenmembers of a collective or an organization attain arule-like ‘objectivity’, and these rules (which form thebasis of the organization’s structure) in turn delimit thebehavior of the very organizational members whocreated them. Yet, rules and structured patterns can bereconstituted on an on-going basis via thought and

action to create revised structures (Nag, 2001). Thus,organizational structures are, by nature, fluid andcontinuously being shaped by organizational members,even as those structures act to constrain and guideorganizational behavior.

The idea of structuration, therefore, provides a linkbetween our previously discussed notions of chaos andstructure. Most organizations start with ill-definedactivities and nebulous goals and, over a period of time,develop relatively stable and temporally persistentcoordinating mechanisms that can be discerned as theirstructures. However, these structures can be disturbedby environmental jolts (such as the one faced byInchoate Inc. and many other modern high-techcompanies), bringing about a state where the organiza-tion’s activities and goals are again infused withambiguity and are better characterized as chaotic. Inhighly turbulent environments, then, a shift can occur,from a somewhat stable conception of ‘organizationalstructure’ to a more dynamic view of ‘organizationalstructuring’ to better capture the interplay betweenpresent chaos and prior structure. In Fig. 3, this notionis captured in the two horizontal ends of the reverse ‘Z’that represent the dynamic process of structuring/restructuring, linking the states of structure and chaos.

Although the concept of structuring provides a linkbetween chaos and structure, it still leaves us searchingfor a similarly useful fulcrum to track the secondtension between technology push and market pull.

Figure 3. Managing the innovation tensions via managed chaos.


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As noted before, we view this tension as morefundamental because it relates more directly to theorganization’s identity. Scholars of organizational iden-tity typically define identity as organizationalmembers’ collective understanding of features pre-sumed to be central, enduring, and distinctive for theorganization (Albert & Whetten, 1985). However,recent theoretical conceptualizations of organizationalidentity view it as less enduring and more flexible incharacter than previously presumed (Corley, 2002;Gioia, Schultz & Corley, 2000). These views highlightthe mutable character of an organization’s identity as itcontinuously interacts with perceptions of the organi-zation held by entities external to the organization.Although the labels used to describe organizationalidentity may remain stable during normal periods ofincremental change, the meanings underlying thoselabels nonetheless tend to change over time as theorganization attempts to adapt to changes in its externalenvironments. In this manner, organization’s enjoy thebenefits arising from a sense of stability aroundanswers to the question ‘who are we?’, while stillfinding it possible to adapt to fluctuating environ-ments.

Extending this notion of an adaptable organizationalidentity to our innovation grid, we contend that anorganization’s identity develops and evolves contem-poraneously with its structure. The process of struc-turing, which leads to the institutionalization ofinteraction patterns, can also lead to a crystallizationof organizational identity attributes, especially in thelabels used to describe the organization’s core attrib-utes. Although structure and identity might develop outof interactions between organizational members, how-ever, the latter exists at a more fundamental and tacitlevel. Therefore, it is likely that an organization willrespond to external changes, even dramatic environ-mental jolts, merely by changing its structures, withoutrealizing a corresponding need to change its identity.That is, a horizontal movement in the innovation grid(illustrating a change in structure) might seem like anappropriate response to competitive pressures in one’sindustry, but might not be successful without acorresponding vertical movement (illustrating an iden-tity change), as well. Thus, we posit that thestructure-chaos tension and the more fundamentalpush-pull tension implicating an identity change needto be seen as mutually interlinked and recursiveprocesses that should be carefully identified andmanaged by organizational leaders.

For us, the notion of ‘managed chaos’ captures thissense of simultaneously managing necessary changesin organizational structure and organizational identityto cope with changing innovation contexts. Somescholars have focused on the importance of viewingchaos as a reality of organizations in highly dynamicindustries and have prescribed continuously evolvingand flexible organizational structures and systems to

maintain an inherent instability (Brown & Eisenhardt,1998; Nonaka, 1988; Schoonhoven & Jelinek, 1990).The notion of managed chaos extends this thinking byrecognizing that it is necessary to surface and thenchallenge the present organizational assumptionsunderlying how the organization sees itself beforestructural changes can have a lasting impact.

The reverse ‘Z’ symbol encapsulates a plausibleprescription for attaining managed chaos in the face ofturbulent environments that force organizations tomanage the interlinked tensions between chaos, struc-ture, and organizational identity. In illustrating thisnotion of managed chaos in Fig. 3, we have replacedthe first-order concepts of ‘technology push’ and‘market pull’ with the labels ‘current identity’ and‘new identity’ to help generalize beyond those organi-zations dealing with the push-pull tension. Otheridentity-related tensions exist beyond the push-pulltension and it is important to illustrate how themanaged chaos model also applies to them.

After analyzing Inchoate’s experiences, we con-cluded that its problems stemmed fundamentally fromtop management’s attempts to change it from technol-ogy push to market pull through structural changesonly (for example, bringing in a new team of businessdevelopment professionals). That is, Inchoate failed torealize that the change it was undertaking involvedmore than just changing its internal structures; it alsorequired a corresponding change in its identity. Asdepicted in Fig. 2, the new business development teamtried to take Inchoate directly from a technology push/chaos mindset to a market pull/structure mindset. Thetechnology push/chaos mindset of Inchoate wasemblematic of the organization’s longstanding inwardfocus on pure technology research and lack of formalstructures to develop a specific set of technologicalideas for commercial success. The new businessdevelopment team tried to implement project evalua-tion processes that were meant to ensure the marketrelevance and commercial viability of the researchprojects carried out by Inchoate’s scientists. Thischange, however, remained only superficial in nature.There was strong evidence for this phenomenon in theorganization members’ comments during the processof change. We found that in spite of having madestructural changes towards the espoused market-pulltransformation, the organizational members nonethe-less continued to use identity adjectives that werereminiscent of their technology-push character.

Gioia et al. (2000) have argued that despite using thesame identity labels in the face of incremental change,nonetheless, there can be subtle changes in themeanings and actions underlying those labels, whichcan create an adaptive instability in organizationalidentity that facilitates organizational change. Cer-tainly, under conditions of incremental change thesubtleties involved in maintaining familiar labels whileunderlying meanings and actions change can be quite

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useful in managing non-disruptive change. This line ofthinking, however, can be modified and extended toaccount for more radical change. Inchoate’s experi-ences suggest that deliberate changes in actions andstructures (in the face of severe environmental jolts)might augur for a more pronounced change in identitylabels themselves in order to execute transformativechange. Changes in actions, structure, meanings andlabels are likely to be necessary to produce managedchaos under such conditions.

Inchoate failed to unearth, and change, the basicassumptions accompanying the technology push para-digm. It failed to realize that to become a market pullcompany, Inchoate and its members had to changeimportant aspects of their identity and fundamentalassumptions underlying the nature of their business.Thus, the fact that they attempted a direct leap acrossthe grid instead of the more steadying movement foundin the reverse-Z model of managed chaos ultimatelystymied the change effort, to the dismay of topmanagement and the change champions.

ConclusionOverall, on the basis of our recent research, we haveconcluded that to better understand effective innova-tion practices we need to expand the conceptualizationof the dynamics involved in organizational innovation.We have identified two dimensions that apply to manyhigh-tech firms and industries. Those dimensions focuson the tensions and tradeoffs involved in: (1) how anorganization pursues innovation (ranging from verystructured to relatively chaotic processes); and (2) howan organization conceives its identity (in terms oftechnology push versus market pull orientations). Theintersection of these dimensions suggests four proto-typical contexts, each with different requirements formanaging innovation processes. Furthermore, theframework developed in Fig. 3 acknowledges thatenvironmental shifts can and do demand changinginnovation assumptions and practices. Changes in themodern environment have produced an inordinatelycomplex innovation-management challenge—one thatmight be addressed by adopting a ‘managed chaos’approach.

The concept of managed chaos suggests that organi-zations facing turbulent environments need to exhibitflexibilities in both structure and identity. The linkagebetween these two basic notions is an importantfulcrum for understanding the tensions of managingorganizational innovation in contemporary organiza-tions. The reverse ‘Z’ model of managed chaospresented here beckons managers to proactively evalu-ate the organizational change initiatives in terms oftheir implications for both the emergent organizationalstructure and the organization’s identity. A correspond-ing change in an organization’s identity is essential forthe new structure to make sense to the organizational

members. If structure is the tool to achieve organiza-tional objectives then identity is the lens by whichorganizational members look at themselves and theworld outside. This hitherto underdeveloped relation-ship between ‘How we do things’ and ‘Who we are’ isan important dialectic that forms the crux of managinginnovation in the face of extreme turbulence.

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Brown, S. L. & Eisenhardt, K. M. (1998). Competing on theedge: Strategy as structured chaos. Boston, MA: HarvardBusiness School Press.

Cohen, M. D., March, J. G. & Olsen, J. P. (1972). A garbagecan model of organizational choice. Administrative ScienceQuarterly, 17, 1–25.

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Day, G. S. (1999). Creating a market driven organization.Sloan Management Review.

Ford, C. M. (1995). Creativity is a mystery: Clues from theinvestigators’ notebooks. In: C. M. Ford & D. A. Gioia(Eds), Creative Action in Organizations: Ivory TowerVisions and Real World Voices (pp. 12–49). Newbury Park:CA: Sage.

Giddens, A. (1984). The constitution of society: Outline of thetheory of structuration. Polity Press.

Gioia, D. A., Schultz, M. & Corley, K. G. (2000). Organiza-tional identity, image and adaptive instability. Academy ofManagement Review, 25 (1), 63–81.

Lawrence, P. R. & Lorsch, J. W. (1967). Organization andenvironment. Boston, MA: Harvard Business School.

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Nag, R. (2001). Toward group learning as group learning.Proceedings. 4th Organizational Learning & KnowledgeManagement Conference, University of Western Ontario,Canada, 415–424.

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Nonaka, I. (1988). Creating organizational order out of chaos:Self-renewal in. California Management Review.

Pratt, M. G. & Foreman, P. O. (2000). Classifying managerialresponses to multiple organizational identities. Academy ofManagement Review, 25 (1), 18–42.

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Stimpert, J. L., Gustafson, L. T. & Saranson, Y. (1998).Organizational identity within the strategic management

conversation: Contributions and assumptions. In: D. Whet-ten & P. Godfrey (Eds), Identity in Organizations:Developing Theory Through Conversations (pp. 83–98).Thousand Oaks, CA: Sage.

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Involvement in Innovation: The Roleof Identity

Nigel King

Department of Behavioural Sciences, University of Huddersfield, U.K.

Abstract: The impact of innovation on organizational members has been examined in variousways. However, relatively few studies have addressed how innovation processes shape people’swork-related identities (and vice versa). This chapter argues that the concept of identity is usefulfor considering the relationship of the person to the organization in the context of innovation. Itevaluates the potential contributions of Social Identity Theory and Constructivist/ Constructionistaccounts of identity to this area. Finally, it presents data from case studies of innovations in theBritish health service to illustrate the value of an interpretive approach to identity andinnovation.

Keywords: Innovation; Identity; Functionalism; Postmodernism; Interpretivism.

IntroductionInnovation has been a prominent research topic in workand organizational (w/o) psychology for over twodecades, and as such can claim many successes. It has,for example, shown how organizational structures canimpede or facilitate innovation (Miles & Snow, 1978);it has highlighted the problems that often occur throughautocratic leadership and the refusal to devolve deci-sion-making power (Bass, 1985, 1999a, 1999b; Kanter,1983); it has demonstrated the key role that climate andculture can play in shaping the innovative performanceof teams and organizations (Nystrom, 1990; West &Anderson, 1992). This is not to say that we now havedefinitive answers to the key questions in such areas—on the contrary, they remain the foci of debate, as newand competing ways of explaining the phenomena areadvocated. What we do have is a sense of a vigorous,constructive discussion capable of clarifying andrefining our understanding, and offering managers andpractitioners potentially valuable insights into the whatis happening in their own organizations. In some areas,though, there has been a paucity of theoretical andempirical research and as a result a failure to properlyaddress important real-world issues. For example, therehas been a much greater focus on the processes bywhich new ideas are generated and adopted inorganizations, and much less on the implementationand routinization of change (Kimberly, 1981; King &Anderson, 2002). I will argue in this chapter that

another major area of neglect has been the involvementin the innovation process of organizational membersother than those with direct managerial responsibilityfor it. After a critical overview of the relevant literature,considering the likely causes of this neglect, I willpropose that occupational identity is a useful concept toutilize in developing our understanding of involvementin innovation. I will illustrate this point with examplesof current and ongoing research into innovation inprimary health care settings in the United Kingdom.Finally, I will conclude by drawing some implicationsfor future research and practice.

Defining InnovationBefore turning to the main theme of this chapter, it maybe useful to consider how innovation should bedefined. The difficulty of defining innovation has longbeen recognized, and while there is consensus thatnovelty is a central feature of innovation, a range ofpositions are proposed regarding the degree of novelty(absolute or relative?), the scale of changes which‘count’ as innovations, and the way innovation shouldbe distinguished from related concepts such as crea-tivity and organizational change (Kimberly, 1981; King& Anderson, 2002; West & Farr, 1990; Zaltman et al.,1973). Nicholson (1990) argues that any attempt todefine innovation objectively is undermined by the factthat the phenomenon is intrinsically relative andsubjective—how innovative a new product, process or

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procedure is depends on the perspective of theobserver. He likens this to an audience at a jazzconcert; what may sound like novel and creativeimprovisation to one member, may be recognized asderivative and unoriginal by another. This position iscongruent with the central arguments of the presentchapter, in terms of its advocacy of non-positivistapproaches to the study of innovation. I wouldtherefore claim that the question of ‘what is innova-tion?’ is not one to be answered in the abstract prior tocommencing research, but should itself be a key focusof innovation research.

Involvement in the Innovation Process: Overviewof the LiteratureIn identifying it as a neglected topic within innovationresearch, I am not suggesting that writers have thoughtorganizational member involvement to be of no greatimportance. On the contrary, many of the commonrecommendations from innovation research are basedon assumptions about organizational memberresponses to, and participation in, change; fromLewin’s (1951) early work on democratic leadershipstyles to the literature championing transformationalleadership (Howell & Higgins, 1990). The rationale forpromoting participative leadership styles, for example,is that these will result in members feeling a greatersense of ownership in innovations, reducing thelikelihood of resistance (e.g. Kanter, 1983). Similarly,arguments for risk-tolerant climates and cultures arebased on a recognition that fear of the consequences offailure can inhibit individual and team propensity toinnovate (e.g. West, 1990). The problem is that most ofthis literature has either treated organizational memberinvolvement as a black box (between the ‘inputs’ ofstructure, leadership, resources etc. and the ‘output’ ofinnovation), or has reduced its complexities to thesingle issue of ‘resistance’. Exceptions include arelatively small number of studies that have sought tounderstand innovation involvement as a meaning-making process, related to values, relationships andinter-group dynamics (e.g. Meston & King, 1996;Symon, 2000).

Resistance to ChangeThe literature in this area has long been driven by astrong practical concern to offer reliable advice toorganizations on how to overcome resistance (e.g.Kotter & Schlesinger, 1979; Lawrence, 1969). Whilethis applied focus is in many ways commendable, toooften it is associated with a set of unacknowledgedassumptions that can result in a very blinkered view ofthe processes involved. First, work on resistance isoften infused with the kind of ‘pro-innovation bias’discussed by Rogers (1983), which portrays innovationin general as a ‘good thing’ and thus resistance as‘bad’. Second, the research agenda is commonlydominated by the concerns of senior management, who

control the researcher’s access to the organization, andin some cases may have directly funded the research.Finally, there is also a bias in much of the literaturetowards pluralistic models of change (as Glendon,1992, notes) which recognize that different groups inan organization will have different perspectives oninnovations, but assume that if the process is managedeffectively, all parties can benefit from positive out-comes.

The result of this combination of biases is to makequestions such as ‘how can organizations best over-come resistance to innovation?’ appear neutral andnon-contentious. I would argue on the contrary, thatapplied to any particular example this question isalmost inevitably controversial and politically-charged.As with the concept of innovation itself (e.g. Nichol-son, 1990), what counts as resistance depends on one’sperspective (Aydin & Rice, 1991; Burgoyne, 1994).Thus a group of staff perceived by managers to bewillfully resisting change may see themselves asstruggling to do the best they can with increaseddemands, or attempting to make sense of unclear aimsand expectations. Managers may believe that aninnovation is clearly for the general good, and thusinterpret opposition as irrational intransigence; staffmay view it as inevitably producing winners and losersin terms of power, status and/or material rewards. Thevery notion that resistance should be overcomebecomes dangerous if one accepts that some innova-tions may have a socially malign impact. If anadministrative innovation in a hospital was puttingpatient safety or confidentiality at risk, would we feelcomfortable in advising managers on how to overcomethe resistance of doctors and nurses?

It is not my intention here to dismiss as irrelevant thebulk of previous research for exhibiting the biases Ihave noted above. There is much valuable workshowing, for example, the contingencies that managersshould consider when adopting a change-managementstrategy (Kotter & Schlesinger, 1979), or the key roleof two-way communication in minimizing resistance(Plant, 1987). My point is rather that by failing torecognize the unspoken assumptions behind theirwork, researchers provide a very partial view of theway organizational members respond to, and areinvolved in, innovation. This needs to be complimentedby research taking different perspectives which moveaway from the ‘overcoming resistance’ framework intheir conceptualizing of involvement in the innovationprocess.

Meaning-Making in the Innovation ProcessRather than locating organizational members’ reactionsto innovation on a dimension of resistance—accep-tance, a small but growing number of researchers havefocused on the ways in which they construct theirresponses, and the purposes such constructions serve(e.g. Bouwen & Fry, 1996; King, Anderson & West,

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1991; Whetten & Godfrey, 1999). Although thisresearch comes from a variety of theoretical per-spectives, much of the empirical work uses aqualitative methodology, because of its suitability forexamining meaning-making in specific contexts. Oneexample of this kind of research is a study I carried outwith Carolyn Meston (Meston & King, 1996), lookingat the introduction of a staff training innovation(National Vocational Qualifications, or ‘NVQs’) ina residential care home for older people. We used agrounded theory methodology which seeks to builda theoretical account of a particular phenomenonthrough detailed qualitative analysis (Strauss & Corbin,1990). This ‘bottom-up’ approach to the developmentof theory is in contrast to the traditional ‘top-down’perspective of positivist social science. Data weregathered by participant observation over a six monthperiod, supplemented by semi-structured interviewswith key informants. The grounded theory modeldeveloped from our analysis is shown in Fig. 1. As canbe seen, personal values in the workplace, and theextent to which individuals felt a need for peer groupapproval, were important factors shaping the waymembers of staff evaluated the innovation andresponded to it in specific situations. To take oneexample; Michael (a Care Assistant) strongly valueshis social relationships at work, but also gains greatsatisfaction from a sense of doing his job well. Thesevalues pull him in two directions in his responses toNVQs. He perceives the peers whose company andesteem he values as largely hostile to the innovation,but at the same time he personally can see the benefitsof the new training for quality of care. The result is thatthe responses he exhibits to NVQs vary markedlydepending on which of his colleagues he is workingalongside on a particular shift.

A different perspective on meaning-making proc-esses in relation to innovation comes from scholarsdrawing on ideas from social constructionism. Thistradition has developed most strongly in EuropeanSocial Psychology, and is centrally concerned with theway our social reality is constructed through theeveryday use of language. It eschews any attempt to‘explain’ social phenomena in terms of such internalpsychological factors as ‘beliefs’, ‘attitudes’, ‘person-ality traits’ and so on. In terms of empirical research, itutilizes a range of methods—most prominently dis-course analysis (e.g. Burman & Parker, 1993)—whichseek to show in fine detail how language operates inparticular texts to construct particular versions of socialreality. (See Burr, 1995, for a good overview of thediffering strands of social constructionist theory). RenéBouwen and colleagues have argued that organiza-tional innovation can be seen as ‘a joint conversationalevent where new configurations of meaning areconstructed’ (Steyaert, Bouwen & Van Looy, 1996,p. 67). They use the term ‘logics’ to refer to thedifferent stances organizational members may take

towards an innovation, as encapsulated in their talkabout it (Bouwen, De Visch & Steyaert, 1992). Usinginterviews from case studies of organizational innova-tions, they show how old and new logics competewithin participants’ accounts; for instance, in oneFlemish high-tech company, an established ‘academic’,research-oriented logic is challenged by a ‘commer-cial’ customer relations-based logic in the wake ofinnovations introduced by a new, entrepreneurialmanagement (Steyaert et al., 1996).

A different social constructionist approach is takenby Symon (2000), who examines the use of rhetoric inthe construction and justification of responses toinnovation. The case she considers is the introductionof a networked PC system in a British public sectororganization. The professional staff (referred to as‘inspectors’) utilize a range of rhetorical devices tojustify their opposition to the innovation. They con-struct an identity for themselves as technically expert,and as the staff group who best understand the businessof the organization (they use the metaphor of working‘at the coal face’). This provides legitimacy for theircriticisms of the position of IT specialists who arechampioning the innovation. They also turn the mainargument of proponents of the change on its head—thatfar from saving resources, it will waste them, in theform of the valuable time of professional staff who willbe expected to do their own typing as one of theconsequences of the new system.

Qualitative research focusing on meaning-making inresponses to innovation has begun to make apparent thecomplex ways in which organizational members maybe involved in innovation processes (Symon, Cassell &Dickson, 2000). When we look closely at individualcases it becomes clear that traditional quantitativemethodologies such as attitude surveys present a staticand rather superficial view of the way people areaffected by and involved in innovation attempts. Assuch, they can only provide a partial view of whyorganizational members respond as they do, and offerlittle insight into how their responses are formed. Forcritics of qualitative approaches (e.g. Morgan, 1996),their principle limitation is that through their insistenceon the over-riding importance of context, they areunable to draw generalized conclusions about organiz-ational innovation, which may serve as the basis forrecommendations for practice. I will return to this issuein the concluding section of the chapter.

Innovation and IdentityAs the previous section shows, researchers have begunto move towards a more rounded and context-sensitiveview of involvement in organizational innovation(Bouwen & Fry, 1996; Carrero, Peiró & Salanova,2000; Meston & King, 1996; Symon, 2000). Longitu-dinal research examining the development of particularinnovations over time has also helped highlightthe complexities of the process, underlining the

Involvement in Innovation: The Role of IdentityChapter 5

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Figure 1. A grounded theoretical model of responses to the introduction of a training innovation at ‘Hazel Hill’nursing home.

Reproduced by kind permission of the authors and Psychology Press, from Meston, C. M. & King, N. (1996). Making sense of ‘resistance’: Responses to organizational changein a private nursing home for the elderly. European Journal of Work and Organizational Psychology, 5, 91–102.

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inadequacies of simple, uni-dimensional understand-ings of member responses; the Minnesota InnovationResearch Program (MIRP) is the most importantexample of this kind of work (Van de Ven, Polley,Garud & Venkataraman, 1999). I want to argue herethat to further develop our understanding in this area, afocus on issues of identity construction and changeshould play a central role.

Relevance to Innovation ResearchIdentity is a key concept in Social Psychology andSociology, and one which is utilized in a variety ofways to address a range of issues in those disciplines.Du Gay, Evans & Redman (2000) argue that ‘the term‘identity’ often provides only simple cover for aplethora of very particular and perhaps non-transfer-able debates’ (p. 2); it is therefore likely that anyattempt to provide a universally-acceptable definitionwill be in vain. For the purposes of this chapter I willpropose a broad working definition, taking identity torefer to a person’s relatively enduring sense of whothey are. Two points of elaboration are required here.Firstly, I assume identity to be intrinsically social; onecan only have a sense of oneself through a sense of howone relates to others—be they specific individuals andgroups (friends, family, workmates and so on), or widersocial categories (such as gender, class or ethnicgroup). Secondly, in saying that identity is relativelyenduring, I am not implying that there is necessarilyanything essential about it. Whether there are essentialand universal aspects to identity is a question ofvigorous debate amongst different theoretical tradi-tions, such as psychoanalysis (Frosh, 1989), socialconstructionism (Burr, 1995), symbolic interactionism(Blumer, 1969), and many others (Du Gay et al.,2000).

Despite its prominence in the social sciences gen-erally, the concept of identity has received relativelylittle attention in work and organizational (w/o)psychology. This is beginning to change now, forexample through the recognition that studying diversityat work inevitably means studying identities (Nkomo,1995). I would contend that the field of organizationalinnovation and change—and especially the topic oforganizational member involvement in innovationprocesses—is a particularly fruitful one in which toemploy it. This is principally due to the fact thatinnovation and change commonly have a strong impacton work-related identities. The nature of this impact ishighly varied. Because innovations often result inchanges to work roles and practices, people may feeltheir occupational identities to be under threat, and thusmay adopt a resistant stance—as was the case for the‘inspectors’ in Symon’s (2000) study, cited above.Innovations may also surface tensions between differ-ent aspects of identity; Michael’s ambiguous responseto NVQs in Meston & King’s study (1996) (op cit) maybe interpreted as resulting from a conflict between his

peer group identity and his occupational identity as acaring, competent Care Assistant. Finally, people mayreconstruct their identity in response to organizationalinnovation, a process exemplified in Steyaert et al’s(1996) (op cit) depiction of the shift from old to new‘logics’ in the course of change implementation.

Approaches to Identity and InnovationIn the recent edited volume ‘Identity in Organizations’(Whetton & Godfrey, 1998), Gioia (1998) distin-guishes three main types of theoretical approach to thearea, which he describes as ‘lenses for understandingorganizational identity’; functionalist, postmodern andinterpretivist. I will use the same categories to reviewthe main theories which have been, or could be, appliedto issues of involvement in innovation processes.

1. Functionalism: Social Identity TheoryOne of the most influential theories in Social Psychol-ogy over the last two decades has been Social IdentityTheory, as devised by Henri Tajfel (1978) anddeveloped by writers such as Turner (Turner, Hogg,Oakes, Reicher & Wetherell, 1987), Hogg & Abrams(1988), and Brewer (1991). The theory states that inorder to make manageable the huge amount of socialinformation available to us, we rely on a cognitiveprocess of categorization to simplify it. Because we aremotivated to view ourselves in a positive light, we seekways of comparing the groups we identify with (‘in-groups’) favourably with those we do not identify with(‘out-groups’). Note that the theory sees identity asplural, but with different identifications being salient indifferent circumstances. For example, a person’s identi-fication as a supporter of a particular soccer team maybe unimportant at work, and therefore not serve as abasis for in-group/out-group comparisons. In contrast,when attending a match, the supporter identity will behighly salient, and ‘superiority’ over the opposingteam’s supporters is likely to be symbolized incolourful verbal exchanges between the groups.

Social Identity Theory has had its greatest impact inw/o psychology in the area of diversity (see Jackson &Ruderman, 1995, for several examples). It is equallyapplicable to the study of involvement in and responsesto innovation. There are many situations in which thenature of group identifications is likely to influencehow people respond to innovations. The effectivenessof change agents, for example, may depend on theextent to which they are seen as representing a dislikedor distrusted out-group by staff in a position toeffectively undermine an innovation attempt. Innova-tions may make salient inter-group distinctions whichhad previously been unimportant, by seeming tobenefit some groups at the expense of others, raisingthe likelihood of inter-group conflict focused on theinnovation. Equally, innovations may stimulate peopleto change their identifications, in order to maintain apositive evaluation of the in-group. Social Identity


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Theory can thus provide a well-developed frameworkfor studying involvement in innovation, allowingpredictions to be made and tested about organizationalmember responses.

Despite these strengths, some important criticisms ofSocial Identity Theory have been made, which arepertinent to its application to the area of organizationalinnovation. Hartley (1996) has pointed out the sub-stantial differences between the kinds of groups used inmost experimental studies and the groups that exist inwork organizations. Experimental groups are, forexample, lacking in history, untroubled by internalpower and status issues, expect no long-term conse-quences as a result of their actions and decisions, andare usually composed of schoolchildren or students.She argues that the effects on identification producedby the most trivial manipulations of categorization insuch experimental groups may well not occur in workgroups where history, power, status, and anticipationsof future consequences are likely to be highly salient tomembers. She also suggests that there is a need todistinguish between ‘group identification’ which maybe relatively transient and open to change, and ‘socialidentity’ which is more enduring and resistant tochange.

2. Postmodern Critique: Social ConstructionistPerspectives on Social IdentityHartley’s warning against assuming that effects pro-duced in experimental groups will be found inreal-world ones raises the issue of the extent to whichgroup context shapes social identity processes.Because Social Identity Theory proposes a universalsocial-cognitive mechanism of categorization as under-lying social identity formation, it is open to thecriticism that it underplays the extent to which thecontext of a group influences the nature of socialidentifications within it. Social constructionist criticsemphasize that identities are constructed (and recon-structed) through everyday interactions, drawing on thediscourses of the person’s society and culture toachieve particular ends in particular contexts. Oneconsequence of this is that identities are more shiftingand unstable than Social Identity Theory suggests, aspeople use them for differing purposes—even in thecourse of a single interaction (Potter & Wetherell,1987). To give a hypothetical example relevant to thetopic of this volume; a middle-manager might in thecourse of a conversation with her superior identifyherself with the traditions of the company at one point,and as an enthusiast for innovation at another. Theapparent conflict between ‘traditionalist’ and ‘innova-tor’ identities does not imply she is being intentionallydeceitful; it simply reflects the multiplicity of identi-ties, which are available to this individual.

A further social constructionist critique of SocialIdentity Theory focuses on the role of argument andpersuasion in identity construction. Michael Billig

(1987) contends that Social Identity Theory presents amodel of identification processes which is rathermechanistic, operating as a kind of ‘bureaucratic filingsystem’ in which a particular ready-formed identity isretrieved or disposed of according to the prevailingsocial contingencies. In contrast, Billig (1987) claimsthat identity formation and change occurs chiefly in thecontext of dialogue and argument; we discuss, chal-lenge, debate each others’ identifications in a two-wayprocess through which both sides’ identities may shift.

Social constructionist approaches to identity haveplayed an important part in drawing attention to therole of everyday interaction in the forming andreforming of identities. This has been recognized evenby some influential adherents of Social Identity Theorysuch as Abrams & Hogg (1992), though they insist thatthere is still a need to understand the identities apparentin talk in terms of underlying cognitive mechanisms.Other critics who are more sympathetic to socialconstructionism’s rejection of universalism and cogni-tivism are troubled by its tendency to ‘lose the person’in its accounts (e.g. Crossley, 2000) and to deny thepossibility of personal agency.

3. Interpretivism: Symbolic Interactionist andPhenomenological ApproachesSymbolic interactionism (Denzin, 1989) and phenom-enology (especially phenomenological psychology;Giorgi, 1970; Moustakas, 1994), though distinct andbroad traditions, share common features in the waythey theorize identity. Like social constructionism, theyargue for identity to be understood in context, andemphasize its location in everyday interactions. Theyare, however, less exclusively focused on the minutiaeof language use and stress the importance of under-standing the personal and collective projects thatpeople engage in. Looking at an occupational groupsuch as lawyers or doctors, for example, interactionistshave examined how they have organized themselves inorder to achieve the status of ‘profession’, and at howindividual members of the group take on values anddevelop their careers in a way that enables them tobecome recognized as successful ‘professionals’ (Mac-donald, 1995). Although interactionist and phenom-enological writers do not see identity as in any sensefixed, they tend to represent it as less fluid and shiftingthan do social constructionists. Biography (individualexperience) and history (collective experience) con-strain the ways in which identity can be formed just asthe immediate social context does (Denzin, 1989;Moran, 1999).

Within this tradition, Butt (1996, 1998, 2001) hasdeveloped an approach to identity which has much tooffer to the study of organizational member involve-ment in innovation. He presents an interpretation ofKelly’s (1955) Personal Construct Psychology (PCP)as a phenomenological theory, also drawing strongparallels with G. H. Mead’s interactionism (Butt,

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2001). PCP states that each person has their ownunique set of constructs, representing the commonways they perceive themselves and their world. Con-structs are usually seen as cognitive entities locatedwithin the individual (e.g. Mancuso, 1996). In contrast,Butt (2001) emphasizes that construing should be seenas a form of social action, and constructs thereforelocated principally in our interactions with others.Furthermore, he supports the view of the phenomeno-logical philosopher Merleau-Ponty (1962) that wegenerally do not stop to reflect before acting; rather,that most of our engagement with the world is ‘pre-reflective’.

Organizational innovations commonly disrupt thepatterns of social interaction through which, accordingto Butt (2001), our identities are constructed. As aresult, organizational members may be drawn todeliberate upon aspects of identity, which are usuallytaken for granted and not the subject of reflection (i.e.normally remain pre-reflective). Precisely how any onemember construes the implications of organizationalchange for their identity at work is neither the result ofpurely individual cognitions nor is it determined bysocial structural forces. Instead, it is mediated by theirpersonal construct system, which—though unique toeach person—will inevitably reflect the constraints of aparticular organizational and professional/occupationalcontext. Butt (2001) uses the example of fashion toillustrate how context moulds and limits personalagency;

Our personal constructs do not arise in a vacuum, butin the context of the social constructs whichsurround us. Just as it is difficult to choose clotheswhich are not manufactured and on offer, so wecannot easily be a particular man or woman that isnot sketched out in our culture (p. 90).

Burr & Butt (1997) have used this approach to examinepersonal change at work following role change fromshopfloor worker to Supervisor, a situation which hassome parallels with that of innovations that result inrole change. In the next section of this chapter I willprovide an outline of research from this perspectivelooking directly at how organizational membersrespond to innovation and change.

Innovation and Identity in a Primary Health CareSettingIn the British National Health Service (NHS), mostpatients’ initial access to healthcare is through theprimary care system. This consists principally ofcommunity-based General Medical Practitioners (GPs)and the teams of other health professionals employedby or attached to their Practices, including CommunityNurses and a range of therapy services. Most inci-dences of ill-health are dealt with within the primarycare system, with only a minority being referred on tospecialist secondary care in hospitals. Primary care is

an especially fruitful area for the study of innovationand identity for two main reasons. Firstly, over the lastdecade or so it has been subject to an unprecedentedseries of radical policy, organizational and practicechanges, instigated by successive Conservative andLabour governments (e.g. Department of Health, 1989,1998, 2000). Secondly, the work force consists ofseveral professional groups with strong—and in thecase of medicine and nursing, long-established—identities, which are likely to be challenged by therecent and current macro-level changes in the sector.Particularly challenging is the increased emphasis onmulti-disciplinary collaborative working, both amongstdifferent health care professions and between thehealth and social care sectors (Burch & Borland, 2001;Poxton, 1999). (Note that in the U.K., social care is theresponsibility of Local Authority Social Servicesdepartments).

The Primary Care Research Group at the Universityof Huddersfield has been involved in evaluating anumber of multi-disciplinary service innovations (e.g.King, Roche & Frost, 2000; King & Ross, in press). Iwould like to draw on data from two of these ongoingprojects to illustrate how identity (defined from aninterpretivist position) can be a valuable concept inunderstanding organizational members’ experiences ofand involvement in innovation processes. Both innova-tions were concerned with the provision of care outsideof the normal working daytime hours, and were led byCommunity Nursing services; however, to functioneffectively both required good collaboration with socialservices staff, with other primary health care pro-fessions, and with the secondary care sector. I willprovide some brief background to each innovation,before focusing on some specific points relating toidentity. Pseudonyms for places and people will beused throughout, to protect confidentiality.

Case One: Fast Response Service, BucktonBuckton is a large city in the north of England, with apopulation of around half a million which includes asubstantial ethnic minority population (especially fromthe Indian sub-continent, but also Afro-Caribbean andEastern European). It encompasses several significantareas of high deprivation. The Fast Response Servicewas set up in 1998 to provide short periods (up to twoweeks) of support for patients in their own homesduring episodes of acute illness. A major aim was toprevent admission to hospital in cases where this wasnot medically imperative, but without a service like theFRS would be necessary because of a lack of support inthe home. The lead role in the service was played byDistrict Nurses (community nurses with a specialistpost-registration qualification); they oversaw the provi-sion of care in the home (for up to 24 hours per day) byHealth Care Assistants (who are not professionallytrained). Many of the patients receiving the servicewere already receiving support from social services;


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those who were not at the time FRS went in oftenneeded to do so once discharged. Because of theseissues of co-ordination and hand-over between serv-ices, social service managers were closely involved inthe development of FRS. It should be noted that theboundaries of community nursing and social servicesteams within the city were not co-terminous at the timeof the study.

Case Two: Out of Hours District Nursing Service,Spilsdale

Spilsdale is a borough in the north west of England,covering a mixture of urban, suburban and rural areas.Almost half of its 200,000 population live in the maintown, Wigglesworth, which has a sizeable ethnicminority (like Buckton, predominantly from the Indiansub-continent) and some areas of high deprivation. Themore rural parts of the borough comprise a number ofsmall towns and villages, some relatively isolated andall with a strong sense of local identity. The Out ofHours District Nursing Service (OHDNS) was set up toaddress the lack of night-time and weekend DistrictNursing cover. As with the FRS, the introduction of thenew service had important implications for co-ordina-tion with social services, and close liaison between thetwo was planned, as well as with medical out of hoursservices. Unlike Buckton, Spilsdale’s community nurs-ing and social service boundaries were co-terminous.

Method

In both cases a qualitative methodology was used toexamine the experiences of staff involved in the serviceinnovations. The principal data collection method usedwas focus groups, supplemented by individual inter-views. While a wide range of primary care, secondarycare and social services staff were included, for thepurposes of this chapter we will concentrate on the twomain professional groups concerned; District Nursesand Social Workers. Summary details of participantsfrom these professions are given in Table 1, below. Allinterviews were transcribed and analysed thematically,using the ‘template’ approach (King, 1998). For fullerdetails of the methodology see King & Ross (inpress).

Identity Issues in the Two Service InnovationsAs noted earlier, interpretivist views see identity asconstructed in the interactions we undertake in oureveryday lives, though inevitably also shaped by widerhistorical and cultural forces. The interactions ofprofessional nurses and social workers are stronglyrelated to the roles associated with the two groups. Inour case studies the innovations impacted on pro-fessional identities because they led to perceptions oranticipations of changes to professional roles and therelationships with colleagues and patients/clients asso-ciated with them. I want to highlight below three waysin which this was manifest: in experiences of roleuncertainty, in perceptions of role erosion, and inperceptions of role extension.

1. Role UncertaintyIn the two cases, some staff from both professionscomplained about uncertainty and confusion surround-ing their roles in the wake of the innovations. Veryoften it was not the new service per se that causeddifficulty; rather it was a whole range of changes, bothlocal and national, of which the FRS and OHDNS wereparticular instances. This is typified by a commentfrom Abbie, a social services team leader in Spilsdale;

It’s very confusing. I know there are a lot of thingscoming up, new initiatives. Everybody is left withtheir heads spinning and thinking ‘where does that fitinto that?’ I think we need to be sure who’s doingwhat, and how we access it, and who provideswhat!

Role uncertainty was not inevitably seen as a threat tovalued aspects of professional identities (though insome instances it could be), but it was often associatedwith perceptions that identities were in some degree offlux. The following two sections show how thismutability could be interpreted in quite different ways,even by members of the same professional group in thesame innovation case.

2. Role ErosionA number of participants described perceptions of ‘roleerosion’; that valued aspects of their role were beinglost because of national and local changes in primary

Table 1. Participants in the two primary care innovation studies.

Innovation example Management of service Community Nursing staff Social Services staff

Buckton CityFast Response Service

1 focus group (n = 6)

1 individual interview

2 focus groups (n = 5, n = 7) 3 focus groups (n = 3, n = 3,n = 4)

SpilsdaleOut-of-hours DistrictNursing service

1 focus group (n = 2) 5 focus groups (n = 7, n = 3,n = 5, n = 5, n = 9)

4 focus groups (n = 3, n = 12,n = 7, n = 5)

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health care and/or social care. These perceptions couldcolour their responses to the particular changes wewere focusing on, leading to a suspicious stance evenwhere the innovation itself did not appear to offer anyreal threat to their role. Often, the ‘other’ professionalgroup were blamed for this erosion, potentially makingcollaboration in new services more difficult;

Don’t get me on about Social Services! I just feeltotally out of my role. They have skimmed off thetop, I feel left with the odd jobs (Julie, DistrictNurse: Spilsdale).

This perception of role erosion was shared by some ofthe community nursing managers. Sheri, Co-ordinatorfor the Out-of-hours scheme in Spilsdale, and aqualified District Nurse herself, expressed anxietyabout the way further developments in the servicemight impact on her staff;

I do worry—it does concern me the way they’vealways been devalued really and undermined ummand its like, you know, to give you (an example) todo with the out of hours or 24-hour DistrictNursing—at the moment the Trust are looking atdeveloping a sort of crisis intervention team inaddition to the 24-hour District Nursing service—but that is going to be led from the hospital . . . . andso you are going to have staff who don’t have theDistrict Nursing qualification assessing patients inthe community to decide whether or not its appro-priate for their care to be maintained at home, orwhether they need to go into hospital, or into theintermediate care bed or discharged form hospitalout into the community—well, you know in myopinion that should be a role for—that is for atrained District Nurse really.

Perceptions of role erosion related to service changeswere probably more prominent amongst DistrictNurses than Social Workers. This may reflect the factthat nursing is a more developed profession than SocialWork, with a generally more positive public and mediaimage; they thus may have more to lose if newcollaborative arrangements alter the boundariesbetween the two professions.

3. Role Extension

In direct contrast to those participants who felt theirprofessional identity was threatened by role erosionwere those who construed organizational changes asenhancing or extending their roles. In these cases, staffshowed a willingness to redefine what it means to be aDistrict Nurse or a Social Worker;

We do things now that we wouldn’t have done 20years ago, it’s a progression—things are still chang-ing—it’s exciting really (Tina, District Nurse:Spilsdale).

Note that the above quote is from the same professionalgroup in the same case as the previous example ofperceived role erosion. This illustrates an importantpoint in interpretive approaches, although the construc-tion of identities is strongly shaped by immediate andwider social contexts, it is also personal—reflecting theparticular pattern of relationships and experiences of anindividual professional. Divergences in interpretationsof what organizational innovations imply for identitiesare therefore to be expected. However, in definingidentity as a personal and social construction, we mustbe wary of slipping back towards an individualisticaccount which reduces social context to a set of‘external variables’ having a secondary influence onessentially individual processes of identity formation(Social Identity Theory’s emphasis on cognitive mech-anisms of categorization is an instance of this). Theperson does not and cannot exist independent of theirsocial context, even though she is not simply deter-mined by it. In relation to our innovation examples, thiswas evident in the way that public perceptions of thetwo professions limited the scope for identity change;for example, Social Workers pointed out that manyservice users expected certain care tasks to be carriedout by a uniformed nurse, and were uncomfortablewith any redefinition of traditional roles and bounda-ries.

ConclusionI have argued in this chapter that the involvement oforganizational members (other than senior managersand other key decision-makers) in innovation processesis a neglected research area. There is a considerableamount of work on resistance to change, but thispresents a rather partial and restricted view of organiza-tional members’ experiences. In responding to thisneglect, I have proposed that the concept of identity isa particularly useful one to employ. I have outlinedsome of the main theoretical approaches to identity andillustrated my favored interpretivist position withexamples from current research in a British PrimaryHealthcare setting. I want to conclude by suggestingfuture directions for research and practice in the area ofidentity and involvement in innovation.

Implications for ResearchThe particular interpretivist approach to identity aninnovation that I have advocated, has two mainstrengths as a basis for further research. First, itemphasizes the need to examine organizational mem-ber involvement in the context of specific innovationsand specific organizations. This is in keeping withother important developments in related areas oforganizational research, such as the attention to thedetail of innovation processes in the Minnesota Innova-tion Research Program (Van de Ven et al., 1999), orthe arguments for sensitivity to context in research onwork group diversity (Triandis, 1995). Second, it


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recognizes the essentially social nature of humanbeings, but also accepts that within bounds theindividual organizational member has scope for adistinctive personal construal of an innovation. Thisapproach therefore avoids the individualism of SocialIdentity Theory, and the denial of personal agencyinherent in Social Constructionism.

In terms of an agenda for empirical work, theimmediate need is for more interpretivist case studiesof innovations from a widening range of types oforganization. As more such material is published,fruitful areas for attention in future studies will becomeapparent. In the work on collaboration between healthand social services in progress at Huddersfield, forexample, our findings so far have led us towards a morefine-grained examination of the dynamics of personalrelationships in the context of organizational change.From this we hope to gain a deeper understanding ofthe way professional identity is constructed in theeveryday working lives of our participants, resulting inthe kind of widely-varying perceptions of innovationand change that we noted above (‘role erosion’ versus‘role extension’).

So far I have discussed identity in terms of the wayorganizational members perceive themselves. The con-cept can, however, be applied at a different level, toexamine the way the organization as a whole sees andpresents itself. As Fiol, Hatch and Golden-Biddle(1998) state;

An organization’s identity is the aspect of culturallyembedded sensemaking that is self-focused. Itdefines who we are in relation to the larger socialsystem to which we belong (p. 56).

Identity understood at this level is also relevant toorganizational member involvement in innovation.Significant organizational innovations very commonlyinvolve cultural change (whether intended or not),impacting on the identity of the organization. Memberresponses to innovation will be shaped by the nature ofthe relationship between personal identities and thechanging organizational identity. For instance, wherethere is perceived to be an incongruity between theorganizational identity and the professional/occupa-tional identities of members, the result could behostility, obstructiveness, or feelings of disempower-ment and disengagement. Future research couldusefully begin to examine these kinds of relationshipsbetween different levels of organizational identity.

Implications for PracticeInterpretive research into identity and innovation doesnot seek to produce general theories, which explainexperience and behavior regardless of context. Forsome critics, this makes such work of limited value forinforming practice. I would contend, however, thatinterpretive studies can make a strong contribution topractice, in several ways. Most directly, they can

provide powerful insights for the organizations inwhich the actual research is carried out; insights notavailable to less contextually-informed approaches.They can also achieve transferability to other settings,through ‘naturalistic generalization’ (Stake, 1995), as Ihave argued elsewhere;

The transferability of findings is based on therecognition of parallels between the research settingand other contexts, and must be on the basis of thereader’s own understanding of other cases. Thisprocess must be facilitated by the researcher describ-ing the setting, methodology and findings insufficient depth to give the reader a strong grasp ofthe nature of the research context—what is com-monly referred to as ‘thick description’ (Geertz,1973) (King, 2000, p. 595).

Rather than seeing the reluctance to make general-izations as a weakness, I would view it as a usefulcorrective to the tendency in the innovation and changeliterature towards over-generalized prescriptions formanagers (e.g. Kanter, 1984; Peters & Waterman,1983). The one piece of general advice I am comfort-able in giving is that innovation leaders should takeinto account the identities of organizational memberswhen planning and implementing change. Interpretiveresearch cannot tell them how to resolve specific issuesin specific circumstances—that must be based on theirown knowledge of their organization and its members.What it can do is sensitize them to areas, which may beof significance and warrant careful scrutiny.

To conclude, it is not my purpose here to claim thatonly the interpretive approach can advance our knowl-edge of organizational member involvement ininnovation—although I do see it as having considerablestrengths, as I hope I have shown. I would like to seegrowing emphasis on this area from a range oftheoretical perspectives, because it is through dialogueand debate between different positions that we sharpenour concepts and enrich our understanding. Identity is,of course, not the only concept of relevance to this area,but it is one which is centrally important and which upto now has been largely overlooked by w/o psycholo-gists investigating innovation.

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Managers’ Recognition of Employees’Creative Ideas: A Social-Cognitive Model*

Jing Zhou and Richard W. Woodman

Department of Management, Mays Business School, Texas A&M University, USA

Abstract: The recognition and support of employee creative ideas is a crucial component inorganizational creativity. In this paper, we explore a social-cognitive approach to explaining theconditions under which a manager is likely to consider an employee idea as creative. Our modelposits that the manager’s ‘creativity schema’ dictates recognition of creative ideas in the worksetting. This creativity schema is influenced by personal characteristics of the manager, by aspectsof the manager’s relationship with the employee, and by a number of organizational influences.Implications of this approach for research and for practice are discussed.

Keywords: Creativity; Creativity schema; Organizational creativity; Creative ideas.

Introduction

Employee creativity plays an important role in thesurvival and growth of organizations (Amabile, 1988;Staw, 1984; Woodman, Sawyer & Griffin, 1993).Recognition of this dynamic has led to an increasingresearch interest in understanding what contextual ororganizational factors facilitate employee creative per-formance (e.g. Amabile, Conti, Coon, Lazenby &Herron, 1996; George & Zhou, 2002; Oldham &Cummings, 1996; Shalley, 1995; Shalley & Oldham,1997; Tierney, Farmer & Graen, 1999; Zhou, forth-coming (a); Zhou & Oldham, 2001).

Management support and encouragement have con-sistently been shown to have main or interactive effectsin promoting employee creativity (Madjar, Oldham &Pratt, 2002; Oldham & Cummings, 1996; Scott &Bruce, 1994; Tierney, Farmer & Graen, 1999). How-ever, we know relatively little about what factorsinfluence when and why an idea, event, behavior, oroutcome is considered creative from a manager’sperspective. To fully understand the nature and dynam-

ics of management support for creativity, we first needto understand the factors that determine the recognition(or non-recognition) of employee creativity.

In this chapter, we will develop a conceptual modeldesigned to explain the recognition of creative ideas inthe work setting. We posit that a manager’s creativityschema provides a useful heuristic to explain therecognition of behavior, events, ideas, and outcomes ascreative. We develop a social-cognitive model toidentify and describe the personal characteristics, workrelationship factors, and organizational factors asso-ciated with the formation and use of such schema.

Conceptual BackgroundEmployee creativity may be defined as the generationof novel and useful ideas (Amabile, 1988). We embedthis individual creativity within the broader issue oforganizational creativity which may be defined as thecreation of a valuable, useful new product, service,idea, procedure, or process by individuals workingtogether in a complex social system (cf. Woodman etal., 1993). Within the context of organizational crea-tivity, three assumptions make research on managers’recognition of employee creative ideas particularlymeaningful. First, employees seldom produce creativeideas in social isolation. Creative performance is asmuch of a social process as a cognitive process(Amabile, 1983). Second, employees typically do notgenerate creative ideas overnight. Instead, there is a

* Earlier versions of this paper were presented at ‘The 21stCentury Change Imperative: Evolving Organizations &Emerging Networks’ Conference, Center for the Study ofOrganizational Change, University of Missouri-Columbia,June 12–14, 1998, and the Academy of Management AnnualMeetings, Chicago, 1999.

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process by which a novel and useful idea getsdeveloped and refined (Basadur, Graen & Green, 1982;Rogers, 1983; Wallas, 1926). This process is likely toinvolve interaction, communication with others, and isoften subject to evaluation and feedback from others(Amabile, 1996; Shalley & Perry-Smith, 2001; Zhou,forthcoming (b)). To the extent that managers areimportant elements of the social environment inorganizations, their reactions may have substantialinfluence on the creative idea generation process.Third, whether an idea or event is creative is notcompletely objective. Individuals attach meanings andinterpretations to ideas or events. Thus, the degree towhich an idea, event, or outcome is creative is, to avery real extent, subjective. What one person sees as acreative idea another person may or may not agreewith.

There has always been a subjective, social dimen-sion to the judgment of creativity. As defined above,creativity implies, at a minimum, an assessment of twodimensions; novelty and usefulness. These dimensionsare sometimes further broken down into componentsub-dimensions (e.g. MacCrimmon & Wagner, 1994),but these are the two most commonly accepteddimensions of creativity and will suffice for ourpurposes here. In terms of measurement, novelty hastraditionally posed less of a problem than usefulness.Novelty or originality is a (sometimes) simple matterof counting and comparing. (Although lacking theknowledge base to accurately recognize novelty could,of course, be a constraint.) The dimension of useful-ness, however, has proven to pose the most trickyconstruct validity issues. At some level of abstraction,it seems impossible to assess the value or usefulness ofa product, idea, outcome, and so on without a judgmentabout such value or usefulness. We see this mostclearly in the world of art. What one person regards asan attractive, valuable painting for example, anotherindividual may view as without merit. At first, it mightseem that the usefulness criterion would be lessdaunting in judging creativity in the organization. Anewly invented product is useful if people buy it; anewly created process is useful if it is more efficientthan the process it replaces, and so on. However, at the‘idea’ stage, usefulness depends very much upon ajudgment about a future state of value. Also, an ideajudged as creative (both novel and potentially useful)by one manager may leave another unmoved. Indeed,Epstein is so vexed by the problems plaguing theusefulness criterion that he avoids altogether thelanguage of creativity when dealing with ‘generativephenomena’ (his preferred term). ‘Behavior calledcreative by one group might be harshly judged byanother’ (Epstein, 1990, p. 139). Epstein even arguesthat, due largely to these judgmental disagreements, thecreative product may ultimately provide a poor indexfor measuring or understanding the creative process.Nevertheless, we wish to focus on a particular type of

creative product in this chapter; specifically, thecreative idea and the factors that influence managerialrecognition of the idea as creative.

Csikszentmihalyi goes much further than mostobservers when he argued that “. . . creativity is not anattribute of individuals but of social systems makingjudgments about individuals” (1990, p. 198). In hissystems view of creativity, Csikszentmihalyi conceptu-alizes creativity as the result of the interaction amongthree sub-systems: a domain, a person, and a field(Csikszenthihaly, 1990, 1996). It is within the ‘field’sub-system that judgments concerning creative ideas,behavior, and outcomes are made. Within the worksetting, the manager would appear to be a crucialcomponent of Csikszentmihalyi’s ‘field’. In sum, weview the manager as a key actor in the social systemthat ‘makes judgments’ about the creativity of individ-ual employees.

Recognition of Creative IdeasWhat is the most useful way to understand or to explainmanagerial recognition of creative ideas? In thischapter, we will argue that creativity schema dictatemanagers’ recognition of a creative idea. In thissection, we will: (a) describe the nature and potentialfunction of a creativity schema; (b) identify personalcharacteristics that are associated with the formation ofa creativity schema; (c) identify aspects of themanager’s relationship with the employee that arerelated to the specific dimensions of the manager’screativity schema; and (d) identify the organizationalinfluence factors that are related to the specificdimensions of the schema. Because we are still at avery preliminary stage of developing a social-cognitiveperspective of managers’ recognition of creative ideas,we emphasize presenting new research ideas instead ofempirical support. In addition, in identifying thevariables that affect the formation and utilization of acreativity schema, we shall attempt to be illustrativerather than exhaustive.

Figure 1 displays the hypothesized linkages betweencreativity schema and the personal, relationship, andorganizational influences factors that are associatedwith the schema. An important aspect of the modelshown in Fig. 1 should be noted at this point. We see acritical difference among these three categories (i.e.personal, relationship, and organizational) in terms oftheir relationship to the schema. Specifically, we positthat personal characteristics of the manager are relatedto, or help to explain, the formation of creativityschema. In contrast, the dyadic relationship andorganizational influence variables help to explain howthe schema is utilized in judging the creativity of theemployee’s idea. In each category, however, we willpropose relationships to specific dimensions of theschema. In developing our propositions, we willspecify the nature of the association (i.e. a positive ora negative relationship) whenever possible. In some

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Jing Zhou and Richard W. Woodman Part VIII

cases, however, the nature of the relationship betweenthe variables is difficult to predict given the currentstate of our knowledge. Also, it is possible in a fewinstances that the directionality of the relationshipmight vary across situations. (Potential moderators arenot explored in this paper.) In these cases, we choose tomake a more general statement concerning the rela-tionship.

Creativity Schema

Fiske & Taylor define a schema as a “cognitivestructure that represents organized knowledge about agiven concept or type of stimulus. A schema containsboth the attributes of the concept and the relationshipsamong the attributes” (1984, p. 140). In essence, aschema is a mental representation of an external targetthat helps the individual to make sense of the target ina simplified and organized way.

Previous theory and research in social cognitivepsychology and organizational change suggests that aschema concerning a specific target can be mean-ingfully thought of as including three components;causality, valence, and inferences (e.g. Lau & Wood-man, 1995; Markus & Zajonc, 1985; Taylor & Crocker,1981). Causality refers to the aspect of a schema thatmaps the sequential relations between events. Itenables individuals to make causal attributions and tounderstand the connections between causes and effects.For a creativity schema, causality provides managerswith some explanations concerning where the creativeidea has come from, why it has been produced, andhow it fits in the particular context within which theemployee and manager work. Valence refers to thesignificance of the target idea or event. For a creativityschema, valence allows the manager to decide whetherthe idea produced by the employee is significant andmeaningful. Finally, inferences allow the individual to

predict what is going to happen in the future, and howlikely these events or outcomes are to take place. Thiscomponent in a creativity schema allows the managerto make inferences about whether the focal idea can besuccessfully implemented, and what the consequencesof implementation are for the organization and for themanager.

Although little research has been conducted toexamine the role of schema on managers’ recognitionof creative ideas generated by employees, previousresearch in organizational change sheds light on theconstruct validity of this concept. For example, Lau &Woodman (1995) investigated the content and develop-ment of change schema. Using both qualitative andquantitative data-collection methods across three dif-ferent samples, they found that the measurement ofchange schema had satisfactory construct validity.Also, empirical work has successfully employed thechange schema construct to explore changes in organ-izational culture and further demonstrated the constructvalidity of change schema as well as the utility of acognitive approach to measuring culture change (Lau,Kilbourne & Woodman, 2003). In addition, theory andresearch, in general, has supported the notion that aschema has the function of directing an individual tomake sense of an external target or event. Further, aschema can be useful for explaining or understandingan individual’s possible attitudinal and behavioralreactions to the target or event (e.g. Markus & Zajonc,1985; Schank & Abelson, 1977; Taylor & Crocker,1981).

Formation of Creativity Schema: Relationships toPersonal Characteristics

As shown in Fig. 1, we posit that three variablesconcerning the focal manager’s personal characteristicsare related to the formation of a creativity schema.

Figure 1. Managers’ Recognition of Employee Creativity: A Social-Cognitive Model.

Managers’ Recognition of Employees’ Creative IdeasChapter 6

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While we are prepared to argue for the likelyimportance of these three personal characteristics, wecertainly are not suggesting that these individualdifferences are the only ones associated with thecomponents of a creativity schema.

The first of these, openness to experience, is one ofthe Big Five personality dimensions (e.g. McCrae &Costa, 1985, 1999). It captures intellectual curiosity,imagination, aesthetic sensitivity, and wide interests,among other traits (John & Srivastava, 1999). Individ-uals scoring highly on the openness to experiencedimension tend to seek and enjoy varied experiencesfor their own sake. Scratchley & Hakstian (2000–2001)found that openness to experience (operationalized asopenness to change, openness to risk, and openness toambiguity) was positively correlated with divergentthinking and could be used to successfully predictmanagerial creativity (operationalized as devising newideas, work methods, and modes of operation useful tothe organization). George & Zhou (2001) found thatindividuals high on openness to experience were themost creative when provided with positive feedbackand when performing heuristic (as opposed to algo-rithmic) tasks. They concluded that openness toexperience may support creative behavior when thesituation allows for the manifestation of the possibleinfluence of the trait. In addition to the production ofnew ideas, by extension openness to experience couldbe related to the recognition of new ideas as well. Thus,it appears that managers who are more open toexperience would be more likely to perceive an idea tohave high valence. Thus, we posit:

Proposition 1: Openness to experience is positivelyrelated to the valence component of creativity schema.

A second variable that might be related to creativityschema is the manager’s functional background. Thisbackground is an indicator of the type of knowledge heor she possesses with regard to the domain of theproposed creative idea. Amabile (1988) identified‘domain-relevant skills’ as being important for crea-tivity. By extension, knowledge and skills relevant tothe domain of some proposed idea could also be afactor in the recognition and appreciation of an idea asbeing novel and valuable. Functional background alsoresults in a manager’s selective perceptions of ambientstimuli (e.g. Dearborn & Simon, 1958; Waller, Huber& Glick, 1995). That is, the cognitive map derivedfrom experience in a particular functional backgrounddirects a manager’s attention to certain events in thesurrounding environment, and to certain attributes ofthe creative idea. Functional background would appearto be associated with how the manager makes sense ofwhy an idea might be developed, how it fits into theemployee’s work, whether the idea is significant in thiscontext, and whether or not it is likely to besuccessfully implemented. Therefore, we suggest:

Proposition 2: Functional background is related to allthree components—causality, valence, and infer-ences—of creativity schema.

A third individual difference construct that may berelated to the formation of creativity schema iscaptured by the broad notion of cognitive complexity.Research has identified a number of cognitive abilitiesthat are related to creativity (Hayes, 1989; Woodman etal., 1993). For example, the cognitive ability ofdivergent production, consisting of processes of flu-ency, flexibility, originality, and elaboration, has longbeen considered as a cognitive key to creativity(Guilford, 1984; Mumford, 2000–2001). Interestingly,with regard to the recognition as opposed to theproduction of creative ideas, the cognitive ability ofconvergent production or thinking may be morecritical. Whereas divergent thinking might allow theindividual to find or produce numerous original ideas,it is convergent thinking that allows the individual toselect from among the ideas those that might be mostuseful or valuable. Runco (1999) explores the role ofcritical thinking in creativity and his conclusionssuggest that this aspect of cognitive functioning mightbe crucial for the recognition of the value of an idea,particularly when the idea is one among many. In asimilar vein, Hogarth (1987) suggested that the abilityto reason and to understand the causality of events wasa crucial component of creativity. In general, we wouldexpect managers with higher cognitive complexity(with the attendant cognitive abilities implied by thatconstruct) to be better able to process the complexinformation that might be needed for judging an idea ascreative. Specifically, we expect:

Proposition 3: Cognitive complexity is positivelyrelated to the causality and inferences components ofcreativity schema.

Utilization of Creativity Schema: The Role ofRelationshipsFour variables in our model concern the dyadicrelationship between the focal manager and theemployee who has generated the creative idea (seeFig. 1). The first of these, liking, refers to the extent towhich the manager has positive affect toward theemployee (e.g. Judge & Ferris, 1993; Tsui & O’Reilly,1989). Previous research showed that when a managerliked an employee, the manager tended to respondmore positively to him or her, for example by givinghigher performance ratings (e.g. Cardy & Dobbins,1986; Judge & Ferris, 1993). Although we know of noresearch directly focused on the relationship betweenliking an employee and the response to ideas producedby the employee, related evidence concerning therelationship between liking and manager assessmentsand responses in general suggests that liking might berelated to the creativity schema components of valenceand inferences. When a manager likes an employee, he

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or she is more likely to think positively about theemployee’s ideas, believe such ideas are meaningfuland significant, and also judge that the idea is likely tobe successfully implemented. Therefore, we propose:

Proposition 4: Liking is positively related to thevalence and inferences components of creativityschema.

Trust is another important variable that defines thequality of the relationship between a manager and anemployee. Following Mayer, Davis & Schoorman(1995), trust is defined as “the willingness of a party tobe vulnerable to the actions of another party based onthe expectation that the other will perform a particularaction important to the trustor, irrespective of theability to monitor or control that other party” (Mayer etal., 1995, p. 712). Because any potentially creative ideais accompanied by some degree of uncertainty, and is,as previously discussed, somewhat subjective, whetheror not the idea is congruent with other aspects of thejob and work setting, as well as its significance andconsequences are subjected to the manager’s personalinterpretations. If the manager trusts the employee, heor she should be more likely to favorably interpretquestions and issues surrounding why the employeehas produced the idea, the meaning and significance ofthe idea, and the consequences that may stem fromimplementation of the idea. In sum:

Proposition 5: Trust is related to the causality, valence,and inferences components of creativity schema.

Wegner and his colleagues (e.g. Wegner, 1986; Wegner,Erber & Raymond, 1991) maintain that transactivememory is a system of encoding, storing, and retriev-ing information that is shared by more than one person.This is an intriguing concept with potential implica-tions for understanding the utilization of creativityschema from the perspective of the dyadic relationshipbetween a manager and her subordinate. When amanager and an employee have developed this memorysharing system, they have knowledge about eachother’s memories, and have shared responsibility thatcould enable one person to remember something theother might not (Wegner et al., 1991). Due to thepotential utility of transactive memory, a managerhaving such a relationship with an employee shouldhave a good understanding of the causes, nature, andconsequences of the ideas produced by the employee.Therefore, we posit:

Proposition 6: Transactive memory is related to thecausality, valence, and inferences components ofcreativity schema.

A final aspect of the dyadic relationship between themanager and the employee that could impact manage-rial recognition of employee creative ideas is capturedby the notion of relative power in the relationship(Hellriegel, Slocum & Woodman, 2001; Kramer &

Neale, 1998). The ability of one actor in this dyad toinfluence the other could range from the managerhaving all of the power, with the employee beingvirtually powerless to a relationship where power ismore balanced between the actors, to, at the otherextreme, the employee having strong ability to influ-ence the manager while the manager has little ability toinfluence the subordinate. This latter scenario seemsthe least likely of the three. One might speculate that abalanced relationship (regardless of the source orreasons for that balance) might be the one where themanager is most likely to focus on the ‘merits’ ofthe creative idea in judging it. It seems conceivablethat the circumstance where the manager’s power isdramatically stronger than the employee’s power mightresult in a tendency for the manager to be less willingto acknowledge the potential value of any employee’sidea, creative or not. In any event, we expect that therelative power positions characterizing this dyadicrelationship may influence managerial recognition ofemployee creative ideas. Thus:

Proposition 7: The relative power of the manager inrelation to the employee is related to all threecomponents of creativity schema.

Utilization of Creativity Schema: The Role ofOrganizational InfluencesFinally, as shown in Fig. 1, we hypothesize that fourorganizational influences are related to creativityschema. The first of these factors, communicationopenness, refers to the extent to which there are openchannels of communication in the organization. Asignificant body of research on organizational innova-tion supports the notion that the availability ofinformation is a crucial variable in the creative process(Damanpour, 1991; Kanter, 1988; Payne, 1990). Thiswork further suggests that constraints on informationand communication have a negative impact on crea-tivity. When a manager can openly and freely exchangeideas and acquire information, he or she is more likelyto understand how an idea produced by an employee isrelated to other aspects of the job that the employee isdoing, and how the idea might be related to the workperformed by other employees across various jobs andorganizational functions. As such, the manager mayalso have a better understanding of the meaning andsignificance of the proposed idea. Finally, the manageris also likely to have a better and richer understandingof whether the idea can be implemented, and the likelyconsequences of this implementation. Thus, we pro-pose:

Proposition 8: Communication openness is related toall three components of creativity schema.

The second variable in the organizational influencescategory is creativity orientation. In general, the‘climate for creativity’ has long been considered a


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crucial component in fostering creative behaviors andoutcomes (cf. Amabile, 1983, 1996). It would seem tobe intuitive that when an organization is orientedtoward finding, promoting, and supporting employeecreativity, a manager is more likely to see thesignificance of the idea generated by the employee, andis more likely as well to believe that the idea has a goodchance to be successfully implemented. Therefore:

Proposition 9: Creativity orientation is related to thevalence and inferences components of creativityschema.

The third variable in this category is managerialdiscretion. This discretion reflects the extent to whichthe focal manager has control over many operationaland production related issues such as, choices ofprojects, work procedures, outcome criteria, and time-lines. Greater autonomy, in general, allows for anexploration of alternative methods for completingwork. An increase in experimentation, in turn, appearsto be related to more creative outcomes (Amabile,1996; Shalley, 1991; Zhou, 1998). By extension, itseems logical that the greater the discretion a managerhas, the more likely that he or she is to believe that theimplementation of the idea and its consequences arepredictable and manageable. Thus:

Proposition 10: Management discretion is positivelyrelated to the inferences component of creativityschema.

Finally, tolerance of mistakes refers to the extent towhich the organization’s reward policy, practices, andculture encourage risk-taking and forgive innocentmistakes committed in the course of trying to accom-plish the organization’s tasks. In such an organizationalculture, producing and implementing new and usefulideas would typically be well regarded and valued.Evidence supports the notion that creative behavior andoutcomes are enhanced when risk-taking is bothencouraged and supported, particularly by an absenceof punishment (Amabile, 1988; Burnside, 1990;Nystrom, 1990). When there is a tolerance for mistakesmade in the pursuit of organizational goals, the focalmanager may be more likely to feel confident andcomfortable about new ideas proposed by employees.In other words, organizational tolerance of mistakesmay be related to the component of the manager’screativity schema related to the consequences of theidea. That is:

Proposition 11: Tolerance of mistakes is related to theinferences component of creativity schema.

Discussion and SuggestionsIn summary, the focus of the model shown in Fig. 1 anddeveloped here has been to explore the key role of themanager in the judgment and recognition of employeecreativity. A social-cognitive approach to understand-

ing the manager’s role in the creative process inorganizations suggests several implications for organ-izational research and management practice.

Implications for ResearchIn terms of our model in Fig. 1, there are a number ofimplications for needed research as well as furthertheory development. While the dimensionality ofschema (causality, valence, and inferences) has beensupported by previous research (e.g. Lau & Woodman,1995), it will be important to develop and demonstratethe construct validity of a measure of creativityschema. Further, we have posited only a limitednumber of personal characteristics of the manager thatmight be related to the formation of creativity schemaand hence to the recognition of employee creativity. Itseems reasonable to suppose a much larger list ofpossibilities in this regard. In addition, it could well bethat greater explanatory power could be added to thesocio-cognitive model shown in Fig. 1 by includingspecific characteristics and behavior of the employeegenerating the creative idea. For example, an obviousaspect of the employee’s behavior that could impact therecognition of creative ideas would be past employeeperformance. That is, an idea generated by anemployee with a history of exemplary performancemight be likely to be judged as creative. At the otherextreme, an employee with a checkered past in terms ofjob performance might more readily have his ideasdismissed or ignored by his manager or supervisor.

Finally, in terms of theory development, we haveproposed a model in the most straightforward mannerpossible, i.e. that manager personal characteristics,characteristics of the dyadic relationship betweenmanager and employee, and organizational influencesare viewed as ‘antecedent conditions’ to managerialrecognition of creativity. Research may show that amore complex configuration provides greater explana-tory power. For example, some contextual variables(e.g. organizational influences) may well moderate therelationship between personal characteristics and man-agerial recognition.

There is some recent research on organizationalcreativity that can inform theoretical development ofthe model represented by Fig. 1. In addition, ourperspective may contribute to the further developmentof several on-going lines of inquiry. For example,Shalley, Gibson & Blum (2000) investigated the degreeto which work environments were structured tocomplement or be congruent with the ‘creativityrequirements’ (i.e. level of creative behavior) requiredby a particular job. Among other notions, they positedthat the level of creativity required in a job waspositively associated with organizational support.While they showed only partial support for thishypothesis across a series of potential relationships, thehypothesis would seem to capture a crucial dynamic. Interms of our posited relationships, it would be valuable

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to know to what extent managerial recognition ofemployee creativity represents an important compo-nent of ‘organizational support’. Further, the creativerequirements of a specific job represent anotherexplanatory variable that could be added to our model.It is logical that managerial recognition could be moreimportant in situations where the jobs have a relativelyhigh ‘creativity requirement’ than in situations where itis less important for employees to engage in creativebehavior.

Organizational support for creativity was posited byZhou & George (2001) to interact with job dissatisfac-tion and continuance commitment to predict creativity.Continuance commitment refers to employees beingcommitted to their organizations not because ofaffective attachment or identification with the organiza-tions’ values and goals, but because of necessity (e.g.not being able to find jobs elsewhere) (Allen & Meyer,1996). They found that, employees with high levels ofjob dissatisfaction and continuance commitment weremore likely to exhibit creativity when perceivedorganizational support for creativity was high. In termsof our model, the findings from the Zhou & Georgestudy are potentially related to both the manager-employee set of variables as well as the organizationalinfluences antecedents. Their work suggests that ourstraightforward diagramming of these explanatoryvariables, as we discussed earlier, may be far toosimple. The potential for interactions among thesevariables across levels of analysis is very real.

Another line of inquiry with implications for oursocial-cognitive model is represented by the investiga-tion performed by McGrath (2001). In a study of 56new business development projects, she found thatorganizational learning was more effective when theprojects were operated with high degrees of autonomy.Learning effectiveness, as operationalized in this study,is related to exploration behaviors leading to creativityand innovation. This line of inquiry suggests that themanagerial discretion variable, which we include as animportant component of organizational influences onmanagerial recognition, may be a particularly impor-tant explanatory variable. Research is needed to isolatethe potentially crucial role of autonomy and discretionin the perception of employee creativity. Here again,possible interactions between autonomy and variablesin our model seem likely.

Finally, a theoretical perspective advanced byUnsworth (2001) suggests yet another potentiallyuseful avenue in developing research to explore ourmodel. Unsworth recently suggested that treatingcreativity as a unitary concept has hampered develop-ment of a richer understanding of organizationalcreativity. Unsworth advanced a matrix of ‘creativitytypes’ consisting of the following categories; expectedcreativity (required solution to discovered problem),proactive creativity (volunteered solution to discoveredproblem), responsive creativity (required solution to

specified problem), and contributory creativity (volun-teered solution to specified problem). These typesdiffer along dimensions of driver (why engage in thecreative process?) and problem (what is the initial statetriggering the need for creativity?). Unsworth (2001)posits that there may be important and interestingdifferences in factors contributing to creativity and thecreative process itself across these categories. In termsof the model in Fig. 1, an interesting research questionconcerns whether the classes of antecedents wouldvary with the type of creativity involved (e.g. would theimportant organizational influences vary acrosstypes?). Further, might managers in general, regardlessof the constellation of antecedents, more readilyrecognize some types of creativity than others? Ofcourse, the construct validity of Unsworth’s creativitycategories has yet to be established.

Implications for PracticeThe major approach taken organizationally in terms ofutilizing knowledge about creativity in the work settinghas probably been through training programs. Whileresults are somewhat mixed, in general a number ofstudies have shown positive results from attempting to‘train’ employees to be more creative. As mentionedearlier, divergent thinking has long been considered acognitive key to creativity. Thus, training programshave frequently targeted divergent thinking whenattempting to improve the creative performance ofemployees and managers (e.g. Basadur, Graen &Scandura, 1986; Basadur, Wakabayashi & Graen,1990). The widely-used training programs designed toimprove the ability of groups and teams to brainstormcreative solutions to problems and/or teaching individ-ual decision makers to engage in ‘lateral thinking’ (i.e.looking for alternative ways to define and understandproblems) also fall into the divergent thinking arenaconceptually (Ripple, 1999). A second approach hasbeen to target creative performance related to a specificdomain, for example strategic planning (cf. Wheatley,Anthony & Maddox, 1991). Based on the logicdeveloped in our paper, a potentially fruitful arena forcreativity training might be to focus this activity onenhancing the ability of key managers and decisionmakers to recognize creativity when they see it. Suchcreativity training would have the advantage of arelatively specific focus, might have wide-spreadapplicability across a large number of individuals ofvarying cognitive abilities, personalities, attitudes, andvalues and thus could be instrumental in helping tofoster the supportive climate and acceptance con-sidered important for organizational creativity.

Harrington (1999) suggests the crucial importance ofthe environmental context for creative behavior. Thereis a tradition of research in the psychological sciencesthat has focused on the effect of the environment on thecreativity of gifted individuals. In general, opportuni-ties to learn and create, ready access to needed


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information, the presence of a supportive social systemincluding appropriate rewards for creative behavior,and the like are considered to be instrumental forcreativity. Research on the ‘climate for creativity’ hasbeen successfully extended into organizations (e.g.Amabile, 1996; Isaksen, Lauer, Ekvall & Britz,2000–2001; Tesluk, Farr & Klein, 1997). As oneexample of this body of work, Tesluk et al. (1997)identified a supportive organizational culture, theutilization of appropriate goal-setting and rewards forcreativity, a host of organizational characteristics (e.g.design features, human resource practices and poli-cies), and socioemotional support as being particularlycrucial in fostering individual creativity in the work-place. In sum, we have reasonable insight into many ofthe contextual factors (both at the organizational leveland the group level) that can enhance organizationalcreativity and, conversely, inhibit it (cf. Woodman, etal., 1993). Based on the approach represented by themodel of Fig. 1, and consistent with the systems viewof creativity advanced by Csikszentmihalyi (1996), weargue that managerial recognition of creative ideasshould be considered a crucial component of thenecessary organizational climate, socioemotional sup-port, appropriate reward structure, and so on needed fororganizational creativity. Woodman (1995) has longargued that the high-payoff strategy for managers interms of impacting creative behavior and outcomes inthe work setting is to learn how to design, and tomanage, the context affecting creativity, rather thanfocusing on attempting to directly manage eithercreative persons or the creative process. To thisargument we would now add the notion that themanager needs to learn how to manage herself in termsof the ability to identify, nurture, and reward creativeideas when they appear.

Concluding CommentsWe have argued that the managerial recognition ofemployee creative ideas can be meaningfully explainedby an examination of the manager’s creativity schema.This schema is influenced by personal characteristicsof the manager, by the relationship that exists betweenthe manager and the employee who creates the ideathat will be judged by the manager, and by certainorganizational or contextual influences that character-ize the particular work setting. We have proposed anumber of relationships between these sources ofexplanatory variation and the causality, valence, andinferences components of the manager’s creativityschema.

Certainly, our proposed social-cognitive model has anumber of limitations. We have made no attempt toaddress the exact process through which a manager’screativity schema is developed. Further, we have madeno attempt to specify how much time might be neededfor the formation and development of the schema.Pettigrew, Woodman & Cameron (2001) have argued

strongly that notions of time need to be incorporatedinto theory development concerned with any changeprocesses in organizations. The model developed herewould fit that broad context and will eventually need toinclude the dimension of time. An additional limitationconcerns the explanatory variation included in Fig. 1or, more to the point, the variables that have beenomitted. The model could be expanded in several waysas was noted above.

Despite the obvious limitations at this point oftheory development, the proposed model has thepotential to make important contributions to organiza-tional creativity research. To the extent thatmanagement support is crucial in fostering employeecreativity, then understanding the initial conditions formanagement support—how and why managers mightrecognize an idea as creative—would seem to be avaluable line of inquiry. This approach has the potentialto broaden our understanding of how to fosteremployee creativity. Creative idea generation is an on-going process in organizations, and managers’recognition of and responses to potentially creativeideas put forth by employees may affect whether theseideas will be accepted, nurtured, and implemented.

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Venture Capital’s Role in Innovation: Issues,Research and Stakeholder Interests

John Callahan and Steven Muegge

Department of Systems and Computer Engineering and Eric Sprott School of Business, CarletonUniversity, Canada

Abstract: The purpose of this chapter is to review the role of venture capital in innovation. Thechapter begins with the history and current state of venture capital. We also describe the processof venture capital financing and how it relates to the innovation process. Then, we review theresearch literature related to venture capital investment decision-making, the venture capital-entrepreneur relationship, and the fostering of innovation by venture capital. Finally, using astakeholder perspective, we outline the usefulness of current research for different stakeholdersand call for more qualitative, longitudinal research that contributes better stories and richer dataon variable interrelationships.

Keywords: Innovation; Venture capital; Research.

IntroductionThe purpose of this chapter is to provide a review of therole of venture capital in the innovation process.Excellent reviews of the investment issues of venturecapital already exist (Gompers & Lerner, 2001a,2001b). There are no reviews, however, that focus onventure capital’s role in innovation. The presentchapter aims to fill this gap.

Innovation is an ancient activity in human history.The pace of innovation, however, has acceleratedsignificantly in the last 50 years (Agarwal & Gort,2001). Innovation is now commonly regarded as thebasis for competitive advantage between enterprisesand between whole communities (Porter, 1990). Ven-ture capitalists make high-risk equity investments innew entrepreneurial ventures. Innovation by venturecapital financed start-ups is felt by many to contributesignificantly to modern economic development. Thefall of 2002, as this chapter is being written, is actuallya good time to ask about venture capital’s role ininnovation and economic development. The last 15years provide a complete up-and-down cycle.

There are many natural sources of conflict betweenventure capitalists and entrepreneurs. As equity inves-tors, venture capitalists want the companies in whichthey invest to be successful. There are many versions of‘success’, however, in any situation as complex asbuilding a new company. For the founding team of

entrepreneurs, successful innovation can be the crea-tion of a company of which they can be proud, thatprovides a good living, and may provide real equityvalue at some time in the future. This process mighttake 10, 15, even 20 years and still be successful.Entrepreneurs are normally not diversified—theirentire fortunes will be tied up in their companies. Onthe other hand, a venture capitalist will have areasonably diversified portfolio of a dozen or moreinvestments. Moreover, for the venture capitalist,success is very specific and clear cut. A VC investsonly with the prospect of realizing real equity valuethrough a liquidity event like acquisition or an initialpublic offering—generally within a period of five toseven years (Lerner, 1994). The conflicts that naturallyexist in this relationship are captured by a quote froman article in an online engineering journal (Tredennick,2001):

VCs know how to deal with engineers, but engineersdon’t know how to deal with VCs. VCs takeadvantage of this situation to maximize the return forthe venture fund’s investors. Engineers are gettingshort-changed.

In reviewing the role of venture capital in innovation,we cover the management issues and the research todate. Our focus is on independent venture capital firmsbut we also review corporate venturing for comparison.

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The chapter begins with an overview of venturecapital—its history and current state. We then describethe process of venture capital financing and how itrelates to the innovation process. Next we review theresearch literature related to venture capital investmentdecision-making and the venture capital-entrepreneurrelationship. We also ask the question again using theresearch literature—does venture capital foster innova-tion? We find that the jury is out on this question.Finally, using a stakeholder perspective, we outline theusefulness of current research for different stake-holders and call for more qualitative, longitudinalresearch that contributes better stories and richer dataon variable interrelationships.

Overview of Venture Capital

Venture capital (VC) is a specialized form of financing,available to a minority of entrepreneurs in attractiveindustries. Many venture capital success stories havebecome household names—Amazon, Cisco, Compaq,eBay, Federal Express, Intel, Lotus, Netscape, SunMicrosystems, and Yahoo all received VC funding.Venture capital is not exclusive, however, to thetechnology sector. The growth of Staples, Starbucks,and TCBY—all ‘brick and mortar’ retailers withinnovative business models—was also fueled by ven-ture capital investment. In the words of VC researchersPaul Gompers & Josh Lerner (2001b, p. 83):

No matter how we look at the numbers, venturecapital clearly serves as an important source foreconomic development, wealth and job creation, andinnovation. This unique form of investing brightensentrepreneurial companies’ prospects by relievingall-too-common capital constraints. Venture-backedfirms grow more quickly and create far more valuethan nonventure-backed firms. Similarly, venturecapital generates a tremendous number of jobs andboosts corporate profits, earnings, and workforcequality. Finally, venture capital exerts a powerfuleffect on innovation.

In addition to funding, venture capital investors(venture capitalists, or VCs) can provide specializedknowledge of a particular industry, experience success-fully growing a business from start-up to publiclytraded company, and access to a network of contactsthat may include seasoned managers, partners, andcustomers. The venture capitalist brings terms, con-trols, expertise, and financial strength that helps form awell-managed and well-financed company that is morelikely to succeed. In exchange, the venture capitalistdemands a preferred equity share of the new venture,along with favorable upside and downside investment

protections.1 The founding entrepreneurs relinquishequity and agree to contractual restrictions intended toprotect the venture investment. In doing so, thefounders give up exclusive ownership of the whole piefor the possibility of owning a small slice of a muchlarger pie, when the firm is taken public or acquired.

Venture capitalists are able to effectively exit theirinvestments only at a liquidity event—an initial publicoffering of stock (IPO) on a public stock exchange,acquisition of the firm by another firm, or bankruptcy.The IPO is the most lucrative result for all investors(Cumming & MacIntosh, 2002), so in principle, theinterests of the founders and venture capital investorsalign in this regard.

Joseph Schumpeter (1934) first proposed that smallentrepreneurial firms are most likely to be the source ofmost innovation. Modern research supports the notionthat large established firms have great difficultymanaging innovations that fall outside of their previousexperience, including architectural innovations (Hen-derson & Clark, 1990), competency-destroyinginnovations (Tushman & Anderson, 1986), and dis-ruptive technology that changes the basis forcompetition in an industry (Christensen, 1997). Estab-lished firms may partially overcome these limitationsthrough ambidextrous organizational structures (Tush-man & O’Reilly, 1997), radical innovation hubs(Leifer et al., 2000), and corporate venturing programsthat emulate venture capital (Chesbrough, 2000).Nonetheless, new firms would appear to have somenatural advantages at realizing some innovations.

Innovation in small firms is difficult to financebecause of four fundamental problems (Gompers &Lerner, 2001b):

(1) high uncertainty;(2) information asymmetry;(3) intangible soft assets;(4) sensitivity to volatile market conditions.

High uncertainty is a fundamental trait of innovationthat no amount of study or due diligence can entirelyeliminate. The future is not only unknown, it isunknowable (Christensen, 1997). Information asym-metry refers to the large information gaps possiblebetween innovators and investors. Because of theirparticular specialized expertise, innovators are likely tohave a superior understanding of their innovation,

1 According to Zider (1998) and Kaplan & Stromberg(2000a), these restrictions may include preferred and convert-ible securities to ensure that VCs are paid first if the firm isliquidated, anti-dilution constraints, prevention of earlyliquidation by entrepreneurs, mandatory redemption rights toforce liquidation by VCs, restrictions on the sale of assets,restrictions of sales of stock that would alter ownership, non-compete and vesting provisions that make it expensive for theentrepreneur to leave the firm, and loss of control rights if thefirm performs poorly.

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while investors are likely to have a superior under-standing of financing. Intangible soft assets includepatents and trademarks, human capital, and futureopportunities. The real value of these assets is difficultto measure; they may have great value to a particularowner, but negligible value to others. The value andliquidity of innovative firms is highly sensitive tovolatile market conditions. During an economic boom,it may be relatively easy and lucrative to complete anIPO of a promising firm on the public stock markets; ina depressed market, it may be impossible.

These four fundamental problems make it difficultfor many entrepreneurs to raise high levels of fundsthrough traditional debt financing.2 Venture capital fillsthis void by providing high levels of funding toopportunities with high uncertainty and large informa-tion asymmetries—in other words, ventures that maynot otherwise have been funded.

Venture capital is neither available nor necessarilydesirable to all entrepreneurs. Most start-ups do notemploy venture capital, nor would they be attractivecandidates for venture funding.3 The vast majority ofentrepreneurial start-ups are sole proprietorships in theservice industry with limited opportunity for growth(Bhidé, 2000, p. 13). Venture capitalists do not fundlaundries, family-run restaurants, or hair salons.

Some founding entrepreneurs that would qualify forventure funding may prefer to bootstrap—self-financefrom personal savings, debt, and re-invested revenue. Anumber of significant Fortune 500 firms, includingsuch technology notables as Hewlett-Packard,4 Micro-soft,5 and Dell,6 have grown to dominate theirindustries without early venture capital funding. Ineach example, the original founders retained significantownership and control of their innovation.

Venture capital emerged in the United States in theyears following World War II (Gompers & Lerner,2001a). In 1946, founders from the MassachusettsInstitute of Technology and Harvard Business Schoolpartnered with local businesses leaders to establishAmerican Research and Development (ARD), the firsttrue venture capital firm. ARD invested in emergingcompanies seeking to commercialize wartime technol-ogies. ARD was a publicly traded closed-end mutualfund.7

Many early venture capital organizations wereorganized as closed-end funds or Small BusinessInvestment Companies (SBICs).8 In 1958, the firstventure capital limited partnership was formed (Dra-per, Gaither & Anderson). Limited partnershipsbecame more prevalent throughout the 1970s and1980s, and are now the most common venture capitalstructure. Unlike mutual funds, limited partnerships areexempt from American Securities Exchange Commis-sion (SEC) regulations, including exacting investmentdisclosure requirements.

Until 1979, investment in limited partnership ven-ture capital funds was restricted to a limited number ofinstitutions and wealthy individuals. In that year,changes to U.S. Department of Labor regulationsopened up venture fund investment to pension funds, arich new source of capital to fuel new growth.

Venture capital is not equally available to entrepre-neurs in all countries. The U.S. venture capital poolremains the largest in the world by either absolute sizeor relative comparison to other economic data. In 1995,the ratio of the venture capital pool to the size of theeconomy was 8.7 times higher in the United States thanin Asia, and 8.0 times higher in the United States thanin continental Europe (Gompers & Lerner, 1999a,p. 326). In 2001, 62% of global private equity9 wasinvested in North America, 21% in Western Europe,12% in Asia Pacific, 2% in the Middle East and Africa,

2 Qualification for a bank loan, for example, may requiretangible collateral and agreement to a fixed repayment plan.As the perceived risk of the investment rises, the terms of thefinancing would become more expensive and restrictive.3 Bhidé (2000) reports that only 5% of 1989 Inc. 500companies start with VC funding, while 80% bootstrap withmodest funds. The Inc. 500 is a compilation of the fastestgrowing privately held companies in the United States.4 HP was founded in 1938 and taken public in 1957—aninterval of 19 years. It achieved Fortune 500 status in 1962. Incontrast, the typical VC-backed company that went publicbetween 1984 and 1994 did so in just five years (VentureCapital Journal, February 1995, p. 45).5 Microsoft was founded in 1975 and taken public in 1986. In1975, personal computers were restricted to a small numberof hobbyists; neither founder had management experience,and both had dropped out of college. Microsoft did acceptsome late-stage funding prior to IPO, although Bhide (2000,p. 164) suggests that this decision was motivated by the desireto improve the legitimacy of the IPO to institutional investorsrather than a need to raise capital.6 Dell was founded in 1984, taken public in 1988, andachieved Fortune 500 status in 1992, all without venturecapital financing.

7 A closed-end fund is a mutual fund whose shares are issuedinitially and subsequently trade on an exchange much likecommon shares. Because of their liquidity, securities regula-tion did not preclude them being marketed to averageinvestors—this fact lead to brokers too commonly sellingthem to investors not really suited to their high risk (Gompers& Lerner, 2001a).8 The SBIC program was set up by the American federalgovernment to encourage the development of venture capitalfor innovation after the shock of the Soviet launch of theSputnik satellite in 1957. The program, however, was badlydesigned and the organizational form is no longer significant(Gompers & Lerner, 2001a).9 Statistics on global private equity are more widely availableand better standardized than international venture capitalstatistics. Private equity includes venture capital, buyoutfunds, mezzanine debt funds, and special situation funds.Venture capital is a substantial component of private equity. Inthe United States in 2001, the $59.7B pool of private equityincluded $41.9B of venture capital.

Venture Capital’s Role in InnovationChapter 7

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and less than 1% in Central and Eastern Europe(PricewaterhouseCoopers, 2002). Table 1 ranks the toptwenty countries for disbursements of private equityinvestment.

Timing is significant—the supply of venture capitalmoney and the willingness of venture capitalists toinvest are strongly dependent on the state of the equitymarkets and other market forces (Sahlman, 1990b).Over the long run, the pool of venture capital, andventure disbursements to portfolio firms, have grownsignificantly (see Fig. 1). Disbursements from U.S.venture funds have grown from just over US$1B in1981 to nearly US$42B in 2001, a compound annualgrowth rate of nearly 19%. The cyclical fluctuationscan be very large—disbursements in 2000, the peakyear for venture capital, exceeded US$100B. WhileU.S. 2001 venture capital investment declined sharplyfrom 2000 levels, 2001 was still the third-highestdisbursement year in the history of the industry, trailingonly the two exceptional preceding years. Internation-ally, the recent declines were less precipitous, withCanadian disbursements declining 27% in 2001 com-pared to the 65% decline in the United States. Periodswith a rapid increase in capital commitments favor theentrepreneur, with less restrictive partnership agree-ments, larger and more frequent investments inportfolio firms, and higher valuations for investments(Gompers & Lerner, 1999a, p. 326). Periods of declinereduce the supply of VC money and favor the venturecapitalist.

At the time of this writing, the technology industry isin a downturn following an exceptional period ofrecord fundraising, IPOs, and acquisitions. Technologyspending by businesses, the target customer base ofmany technology ventures, is in decline. Market

Table 1. Global private equity investment (2001).*

Rank Country Investment(US$B)

1 USA 59.72 United Kingdom 6.23 Germany 4.04 Canada 3.25 France 3.06 Japan 2.17 Italy 2.08 Sweden 1.89 Korea 1.8

10 Hong Kong 1.811 China 1.812 Netherlands 1.713 Israel 1.614 Australia 1.315 India 1.116 Singapore 1.117 Spain 1.118 Taiwan 0.819 Belgium 0.420 Denmark 0.3

* All dollar values in billions of U.S. dollars.Source: PricewaterhouseCoopers (2002).

Figure 1. U.S. venture capital investment (1980–2002).

* Data from 2002 is incomplete, accounting only for the first nine months of the year.Source: PricewaterhouseCoopers/Venture Economics/National Venture Capital Association MoneyTree Survey, Q3 2002Quarterly Statistics.

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evaluations of technology firms are low. Based on datafrom the first three quarters, U.S. disbursements in2002 are expected to further decline from 2001 levels.Such ‘boom and bust’ cycles are not new. VCdisbursements previously declined in the late 1970s,and again in the mid-1980s. Figure 2 expands the ten-year period between 1982 and 1991 to illustrate thedepth and duration of the previous disbursementdecline.

Despite impressive long-term growth, venture capi-tal remains a very small fraction of the total equitymarkets. Gompers & Lerner (1999a) estimate that inthe United States, there are a hundred dollars ofpublicly traded equity for every dollar of venturecapital.

Venture capitalists strongly favor particular high-growth, technology industries. Tables 2 and 3 show thedistribution of U.S. venture capital investment acrossindustry classification.10 In 2001, nearly three-quartersof total venture capital disbursement dollars went tofirms in only six of the seventeen industries classifica-tions: software (20%), telecommunications (15%),networking and equipment (14%), retailing and dis-tribution (10%), biotechnology (8%), and InformationTechnology services (7%). The retailing and distribu-tion category includes traditional ‘brick and mortar’retailing as well as Internet businesses; the other fivecategories are exclusively technology industries. In the

most recent data available at the time of this writing,55% of U.S. venture capital investment was awarded toInternet-related businesses, including E-commerce,Internet software, services and tools, hardware, andinfrastructure.11

These tables also demonstrate the short-term trendscommon in venture capital investment. As an example,consider the retailing and distribution category. Duringthe height of the dot-com boom of 1999 and early2000, the fraction of venture money invested inretailing rose from typical levels of 5–10%, to 23% and18% respectively, as venture firms invested heavily ine-commerce. In 2001, the fraction of venture invest-ment in retailing had returned to 10%.

According to Zider (1998):

The myth is that VCs invest in good people and goodideas. The reality is that they invest in goodindustries. Regardless of the talent or charisma ofindividual entrepreneurs, they rarely receive backingfrom a VC if their businesses are in low-growthmarket segments.

Tredennick (2001) summarizes this differently:

VCs either all fund something or none of them will.If you ride the crest of a fad, you’ve got a goodchance of getting funded. If you have an idea that’stoo new and different, you will struggle for funding.

10 The seventeen industry classifications are defined by thePricewaterhouseCoopers/Venture Economics/National Ven-ture Capital Association MoneyTree Survey.

11 From the MoneyTree survey, Third Quarter 2002,$2464.1M of 4475.9M total VC investment in Q3 2002 wasdisbursed to Internet-related businesses.

Figure 2. U.S. venture capital investment (1982–1991).

Source: PricewaterhouseCoopers/Venture Economics/National Venture Capital Association MoneyTree Survey.


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Venture capitalists strongly favor particular geograph-ical regions. Figure 3 shows the distribution of U.S.venture capital by state. In the United States, venturecapital investment is strongly concentrated in Cal-

ifornia (particularly in Silicon Valley, Orange County,and San Diego) and Massachusetts (particularly nearRoute 128 that circles Boston). In the third quarter of2002, these two states together represented over half of

Table 2. U.S. venture capital investment by industry classification (1995–2001).*

1995 1996 1997 1998 1999 2000 2001

Biotechnology 854 1,249 2,136 1,556 2,222 4,300 3,236Business Products and Services 171 321 269 405 1,347 2,005 496Computers and Peripherals 421 464 527 521 1,185 2,954 1,139Consumer Products and Services 601 450 566 578 630 1,053 468Electronics/Instrumentation 154 273 383 300 352 907 412Financial Services 177 287 358 622 790 741 492Healthcare Services 387 672 1,187 818 666 593 437Industrial/Energy 652 614 942 1,387 1,500 2,256 1,336IT Services 187 463 671 1,247 4,216 9,120 2,994Media and Entertainment 382 939 985 1,613 5,428 8,808 2,235Medical Devices and Equipment 705 652 987 1,200 1,438 2,543 2,047Networking and Equipment 346 626 1,013 1,511 4,367 11,122 5,716Other 29 11 56 128 178 249 172Retailing/Distribution 360 827 852 2,155 12,572 19,420 4,164Semiconductors 203 218 483 631 1,222 3,298 1,809Software 1,081 2,308 3,256 4,228 9,348 20,402 8,545Telecommunications 1,007 1,313 1,684 2,729 8,076 17,536 6,241

Total 7,717 11,687 16,356 21,630 55,537 107,306 41,940

* All dollar values in millions of U.S. dollars.Source: PricewaterhouseCoopers/Venture Economics/National Venture Capital Association MoneyTree Survey, Q3 2002Quarterly Statistics.

Table 3. U.S. relative venture capital investment by industry classification (1995–2001).

1995 1996 1997 1998 1999 2000 2001% % % % % % %

Biotechnology 11 11 13 7 4 4 8Business Products and Services 2 3 2 2 2 2 1Computers and Peripherals 5 4 3 2 2 3 3Consumer Products and Services 8 4 3 3 1 1 1Electronics/Instrumentation 2 2 2 1 1 1 1Financial Services 2 2 2 3 1 1 1Healthcare Services 5 6 7 4 1 1 1Industrial/Energy 8 5 6 6 3 2 3IT Services 2 4 4 6 8 8 7Media and Entertainment 5 8 6 7 10 8 5Medical Devices and Equipment 9 6 6 6 3 2 5Networking and Equipment 4 5 6 7 8 10 14Other 0 0 0 1 0 0 0Retailing/Distribution 5 7 5 10 23 18 10Semiconductors 3 2 3 3 2 3 4Software 14 20 20 20 17 19 20Telecommunications 13 11 10 13 15 16 15

Source: PricewaterhouseCoopers/Venture Economics/National Venture Capital Association MoneyTree Survey, Q3 2002Quarterly Statistics.

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all U.S. venture capital investment.12 There appear tobe two closely related factors to explain geographicalclustering.

First, venture capitalists tend to invest close to home.In the United States, venture capital firms are highlyclustered in California and Massachusetts.13 Lerner(1995) reports that over half the venture-backed firmsin a biotechnology sample had a venture director withan office within 60 miles of their headquarters. Powellet al. (2002) reports that more than half of all U.S.biotech firms received locally based venture fundingbetween 1988 and 1999.

Second, regions with large venture capital activitydevelop agglomeration economies that further favor

venture capital through a virtuous circle of improvedprocess efficiency. Intermediaries familiar with theworkings of the venture process, particularly lawyers,accountants, and real estate brokers, reduce the transac-tion costs associated with forming and financing newfirms (Gompers & Lerner, 1999b).

Each venture capital firm has a style and characterunique to itself (Nesheim, 2000, p. 187). Firms differfrom one another by reputation, age, experience of thegeneral partners, preference for lead or follow-oninvestment, and their record of past success. Manychoose to specialize in a particular industry (such astelecommunications or biotechnology), or a particularfunding stage (seed, early or late-stage investments). Afew firms can boast of significantly higher perform-ance. According to Nesheim (2000, p. 180), whileapproximately 60% of funded firms go bankrupt,several VC partners claim to have one-third to one-halffewer bankruptcies per portfolio company. The IPOs offirms backed by VCs with strong reputations attracthigher quality underwriters and are more widely heldby institutional investors (Megginson & Weiss, 1991).

The Venture Capital Investment Cycle

In the dominant limited partnership form, a venturecapital firm sets up one or more separate investmentfunds as limited partnerships. The firm becomes the

12 From the birth of the VC industry, venture capital hasfavored California and Massachusetts. In the third quarter of2002, California and Massachusetts respectively captured41% and 11% of U.S. venture capital investment. In the late1960s, Silicon Valley and Route 128 had similar levels of bothhigh-tech employment and venture capital investment; today,Silicon Valley has significantly outpaced Route 128. For ahistory and analysis of regional advantage, see Saxenian(1994).13 Nearly 40% of U.S. venture capital firms qualifying for the2002 MoneyTree Survey had head offices in either Californiaor Massachusetts (251 of 668 VC investment firms). Ten morefirms had offices in the New England area close toMassachusetts.

Figure 3. Venture capital investment by region (U.S. only).

Source: PricewaterhouseCoopers/Venture Economics/National Venture Capital Association MoneyTree Survey, Q3 2002Quarterly Statistics.


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general partner in these funds and then sells units ofinterest in these funds to limited partners (wealthyindividuals, pension funds and corporate investors).14

The VC firm manages these funds as a general partner.A condition of limited partnership is that the limitedpartners play no role in managing the funds. Whenopening a fund, a VC firm will specify both asubscription target and an investment policy for thefund.

Limited partners pay VCs annual ‘carrying’ ormanagement fees generally between 1% and 3% of thetheir investment. Once a fund is terminated (usuallywithin ten years), the general partner receives ‘carriedinterest’ of around 20% (Gompers & Lerner, 1999b) ofthe capital gains realized by the fund over its lifetimewith the limited partners receiving the rest. It is only atfund termination that the limited partners realizeliquidity on their investment. A VC firm opens andterminates different funds, some appealing to retailinvestors and others to institutional investors, on aregular basis.

Note that VCs do not participate directly in lossesalthough losses certainly lead to a loss of futurebusiness. Bhidé (2000, p. 144) argues that this asym-metry leads VCs to take excessive risk in theirinvestments.

Insiders in the companies in which VCs invest, havetacit knowledge of their opportunities that is very hardto make available to outsiders. Because of informationasymmetries and the related lack of ‘efficient pricing’,venture capital investing is very labor intensive. VCfirms do not handle the volume of invested fundsregularly handled by fund managers of liquid estab-lished stocks. $100 million is large for a VC firm,whereas funds over a $1 billion are common for liquidinvestments. As a result the fees charged to investors byVCs are correspondingly higher (Lerner, 1995; Sahl-man, 1990a, p. 508).

Once the venture capital firm has received moneyfrom subscription to a fund, it sets about investing thefunds. This process of raising money, and then placingit, creates a time lag that has given venture capital firmssignificant difficulties in recent years. Firms raisedfunds during good years for investing, and then whenmarkets turned down in 2000 and 2001 they did nothave good opportunities in which to invest. As a result,many firms had significant ‘overhang’ during thisperiod. Some even returned funds to investors—a verycostly proposition.

The investor returns on a venture capital fund aretypically generated by a small fraction of theirinvestments. One study of venture capital portfoliosreported that about 7% of investments accounted for

more than 60% of the profits, while fully one-thirdresulted in a partial or total loss (Bhidé, 2000, p. 145).Such skewed returns across the portfolio have been anattribute of venture capital investing throughout thehistory of the industry. ARD, the first professionallymanaged VC firm, generated over half of its annualizedrate of return from a single $70K investment in DigitalEquipment Corporation (out of total investments of$48M).15

Venture Capital’s Role in New Venture FinancingWhen thinking about venture capital’s role in innova-tion, keep in mind the relatively small percentage ofinnovative start-up companies that use venture capitalduring their development.

The Financing SequenceFor those start-up companies that do have businessmodels that require significant up-front expenditureson product/service development and business infra-structure creation, the normal sequence of equityfinancing is as follows:

(i) Personal funds of the entrepreneurs

The entrepreneurs who start a company are thefirst to invest in the company. This may be asignificant amount in the case of a companystarted by entrepreneurs successful from previousventures. Normally, the amounts raised this waywill be tens of thousands of dollars. This equitywill likely include personal debt raised by theseindividuals that is invested in the start-up asequity. It may include ‘sweat equity’ in the formof under-compensated work. This type of initialinvestment can extend to employees as well. Theproposition becomes: ‘If you think that a job hereis attractive, you should want to invest in theopportunity’.

(ii) Friends and family funds

A new venture will seldom be able to proceed toraise equity investments from organized sources ifit is not able to raise equity from the friends andfamily of the founding entrepreneurs. The abilityto go to friends and family and convince them toinvest is regarded as a sign of commitment by thefounding entrepreneurs to a real, quality opportu-nity. The family and friends round is again likelyto be in the tens of thousands of dollars range.

(iii) Angel investors

Angels are wealthy individuals who invest theirown money (Fenn & Liang, 1998). They are oftenentrepreneurs who have been successful in the

14 A typical distribution of limited partners includes pensionfunds (50%–60%), endowments and foundations (20%–30%),other financial institutions (6%), and high net worth individ-uals (4%). Source: Venture Economics.

15 The twenty-five year annualized rate of return from 1946 to1971 was 15.8%. Excluding the DEC investment, theannualized rate of return would have been 7.4% (Bhidé, 2000,p. 162).

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same area of business as that in which they invest.Angels often keep a low profile in their commu-nities, not wanting to be pestered by start-upslooking for money, but preferring to find invest-ment opportunities through their personalbusiness networks. They usually invest between$100K and $500K. Although seldom organized,there are exceptions including the Band of Angelsin Silicon Valley, Zero Stage Capital in NewEngland and Purple Angel in Ottawa, Canada.16

Many more firms receive funding from angelsthan from venture capitalists, but the level offunding is much lower (Freear & Wetzel, 1990).

Angel investment is important to a start-up formore than the risk capital that angels provide.They often have deep knowledge of the industryand of the entrepreneurs that drive them. As aresult, they bring credibility and contacts withtheir investments. Start-ups that have beenfinanced by angels have a much greater successrate in attracting subsequent venture capital. In arecent questionnaire survey study, Madill et al.(2002) found that “57% of the firms that hadreceived private investor financing also receivedfinancing from institutional venture capitalists;only 10% of firms that had not received angelfinancing obtained venture capital”.

(iv) Venture capital

The minimum amount invested in a venture byorganized venture capital companies is generallyover a million dollars. On the high side, VCinvestments up to $100 million are possible.

VC investments are very commonly syndi-cated—there will be a lead VC that organizes agroup of VC firms to invest in a start-up (Lerner,1994). For example, when the computer securitycompany, Zero Knowledge, went to the venturecapital market for financing in 1999, they hadserious discussions with 10 venture capital firmsin both Canada and the United States. In the end,they raised $12 million in equity from threeAmerican firms: Platinum, Aragon and StrategicAcquisitions.

VC investments are also commonly staged, sothat multiple rounds of venture capital investmentmay be required to take an early-stage firm toliquidity (Gompers, 1995). Each funding round isnegotiated at the current valuation of the firm, anddilutes the ownership of existing investors.17

Staging is a control mechanism that allows VCs tomonitor the progress of firms and maintain theoption to abandon under performing projects.

Venture capital firms supply many other thingsto a new venture in addition to financing. Verycommonly they bring a deep knowledge of thetechnologies and markets, and as a result can addsignificant value in terms of business model andmarketing strategy. Some VCs have large net-works of contacts—with other investors,customers, potential partners, and managers.These contacts can be of great value to a newventure. Investment in a start-up by a prestigiousVC also brings credibility in both the financial andproduct markets.

(v) Merchant bank financing

As a startup grows and proves its business model,investment risk can decrease. At this point theneed for capital can increase substantially. Underthese circumstances, a start-up can look to institu-tional investors called ‘merchant banks’ forfinancing. Investments at this stage are called latestage venture capital or mezzanine financing.Merchant banks have large amounts of fundsavailable to them, and the lower risk and likelyshorter horizon until liquidity of late stage venturefinancing can be attractive to them. They generallyinvest in the form of debt, sometimes convertibleto equity. As debt investors, one of their principalconcerns is that the company has the cash flow toservice the debt.

(vi) LiquidityVenture capitalists look to a liquidity event likedivestiture (i.e. acquisition by another company)or an initial public offering (IPO) to cash out. Asa result, a venture capital backed new venturemust plan and work towards such a liquidity eventfrom the start if they wish to raise venturecapital.

In some cases, venture capitalists may exercisecontrol rights to force bankruptcy of an underperforming venture. This may allow the VC torecoup some investment through ownership ofpreferred shares that are paid out before commonshares.

The investor view of this financing sequence is shownin Fig. 4, which is an adaptation of the funnel modelcommonly used in new product innovation manage-ment (Wheelwright & Clark, 1992, 111–132).

Opportunities arise out of ideas at the ‘fuzzy frontend’ of innovation. Many opportunities enter thefunnel; few exit to sustained profitability. The timing inthe diagram is meant to be descriptive of commonpatterns. The line segment marking the timing ofventure capital investment is dotted at both ends toindicate the variable entry points of venture capital

16 The Purple Angel partners are former executives of NortelNetworks. Purple was the corporate color of Bell-NorthernResearch, the Nortel research and development subsidiary,until the mid-1990s.17 Venture capital investors commonly demand anti-dilutionprotections on their investments, shifting this burden tofounders and other investors.


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Figure 4. The venture capital investment process.

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John Callahan and Steven M

ueggePart V

III

during different investment eras: for example, veryearly in 1998 and 1999, and much later in 2001 and2002.

New venture opportunities emerge from the ‘fuzzyfront end’ propelled by the drive, and personal funds,of the entrepreneurial team. As they enter the funnel,opportunities proceed through standard milestonessuch as having a business plan, having a prototype oftheir product or service, making a first sale, becomingcash flow positive, and reaching profitability. At eachstage, a portion of these opportunities fail—the narrow-ing of the funnel represents the attrition ofopportunities. Early stage investors regard success ashaving an opportunity exit the funnel through aliquidity event, at which time they can cash out. Veryfew startup ventures ever reach a VC satisfying exitlike divestiture or IPO.

The Venture Capital Investment Process

Table 4 outlines the main stages of a model of theventure capital investment process (Tyebjee & Bruno,1984). The model was developed on the basis of aquestionnaire survey of 87 U.S. VCs and commentsreceived from managers of seven of them. Tyebjee andBruno make an important point about their results:

The diversity of the responses, both in content andstyle, demonstrates the heterogeneity in practices ofdifferent venture capital firms. This heterogeneitycautions against too rigid a specification in anymodel describing venture capital management.

Tyebjee & Bruno’s model was corroborated by Sweet-ing (1991) for U.K. venture firms.

Venture capital firms are interested in learning earlyabout potential investments, and use their personalnetworks to locate such opportunities. In rare instances,a venture capitalist may become involved in thedevelopment of a new venture before it is ready forinvestments of the size and type appropriate for VCs.More commonly, however, the deals seek out VCs, whooften maintain a high profile in their investmentcommunity—spending significant amounts of time atbusiness events and conferences. The timing of VCfinancial entry into an opportunity can depend greatlyon the supply of and demand for good opportunities byVCs. During the bubble years of 1998 and 1999, veryearly entry—before real sales—was the norm. Sincethe bubble burst in 2000, many VCs have beeninvesting more conservatively and later in the opportu-nity development cycle.

VCs refer to ‘deal flow’ to describe the flow ofinvestment opportunities that they see. Deal flow is thelifeblood of a VC firm. Because they normally see somany business plans, they have tough filters to controltheir workload. Of the business plans that they see, theyfinance only a very small percentage (Nesheim, 2000).Just reading a business plan can take hours, and VCscan receive hundreds per month. Some VCs do notaccept any unsolicited business plans. They do takeseriously, business plans brought to them by personalcontacts and individuals that they know and trust(Shane & Stuart, 2002). This is one of the reasons thatangel investment can be so important for a new ventureintent on raising venture capital. A well-connectedangel can personally introduce the founding entrepre-neurs and their business opportunity to potential VCinvestors (Fenn & Liang, 1998).

Table 4. A processual model of U.S. venture capital fund activity.

Stage Features

I. Deal origination • Most deals are referred by third parties.• Referrals by other VCs are often invitations to join syndicates.• VCs are rarely proactive in searching out deals.

II. Deal screening • Most frequently used screening criteria are: technology and/or market; stage of financing.

III. Deal evaluation • Decision to invest based upon expected return compared with level of risk. Factors consideredinclude:

Market attractivenessProduct differentiationManagement team capabilitiesProtection of business from uncontrollable factors, e.g. competition, product obsolescence.

IV. Deal structuring • VC funds use a wide range of approaches. An aim can be to help motivate managers toperform.

• Price can be determined by: quality of opportunity; past experience with similar deals and so on.

V. Post-investmentactivities

• Venture funds provide management guidance and business contacts.• Representatives of venture funds normally sit on boards of operating businesses; they assist with

development of business strategy.• Venture fund representatives can act as ‘sounding boards’ for operating business management.

Source: Sweeting (1991, p. 603).


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VCs screen deals initially based on such factors asinvestment stage, investment size, industry sector andgeography. If a deal gets through this screening, thefirst questions asked of the entrepreneurs driving anopportunity are of the form, “So what? Who cares?Why you?”18 In other words: What is the core of theopportunity and why is it important? Who are thecustomers and what value is the start-up going toprovide to them? And, what competitive advantagedoes the start-up bring to the table that will ensure thatthey can make money with the opportunity? Sub-sequent discussions elaborate on these themes. Thedecision criteria that VenGrowth uses in evaluatingopportunities are the following: people, market, cus-tomer traction, competition, product idea, technologyand timing. Other VC firms may have somewhatdifferent criteria, but the core elements—experiencedmanagers,19 proprietary products, minimum investmentthresholds, and extensive due diligence—are fairlyuniform across VC firms (Bhidé, 2000). The researchon venture capital deal evaluation is surveyed in a latersection.

If a VC is still interested in investing after reviewingthe company’s business plan and talking with theprinciples, the VC will issue a term sheet to thecompany. This term sheet outlines what the VC sees asthe basis for a financing deal. If the company acceptsthe term sheet, then due diligence by the VC begins inearnest on the company, the entrepreneurial team, andthe opportunity. During this period of due diligence,the company is normally restricted from ‘shopping thedeal around’ to other investors—in a sense, acceptanceof the term sheet gives the VC an option to invest. Thisdue diligence period can last several months, and isalways a period of high stress and high cost in terms ofmanagement attention for the company.

Valuation of a startup, required as part of any deal, isa complex task (Timmons, 2001, Ch. 14). Quantitativemodels are used—multiples of sales, discounted multi-ples of future earnings, comparison with previous andconcurrent deals, previous valuations at angel seedrounds—but many of the factors are qualitative.Qualitative factors focus on the match between what isrequired to be successful and the strength of the coremanagement team, and of future market and technol-ogy trends.

Structuring the deal is the last stage before closingthe investment. A good deal structure is one in whichthe goals of the VCs and of the entrepreneurs are

aligned to the greatest extent possible. Importantconsiderations include the equity share allocated toeach party, the investment instruments used, and thestaging of disbursements to the company.

The investment instruments used in VC deals havechanged over the last few years. In the past, it wasusual for VCs to purchase common shares of thecompanies in which they invested. They becameinvestors on the same level as the founding entrepre-neurs, family and friends, and angels. In the last fewyears, VCs have taken to insisting on convertiblepreferred shares and the senior liquidation rights thatcome with them (Kaplan & Stromberg, 2000a). Theseshares generally have minimum conversion values oftwo to three times the original sums invested. Thismeans that when a liquidity event occurs, the VCs getpaid before the common shareholders at a minimumpayout that is a multiple of their initial investment.These are very tough terms.

When a deal has been signed, the start-up firm getsa check for the initial ‘tranche’ of the VC funds to beinvested. It is rare for the full deal amount to be paid inone lump sum.20 As part of the contract, the start-upmust meet defined milestones to get successive tran-ches of the deal. These milestones take a variety offorms such as product development events, hiring keypersonnel, and meeting sales targets.

VCs are very active investors. Commonly, theyparticipate as active members of the board; recruitingmanagement and key technical personnel; developingbusiness strategies; monitoring the company’s per-formance; and facilitating subsequent financing rounds(Kaplan & Stromberg, 2000b). VC firms have evenbeen know to function much like the chief financialofficers of their client companies if these companies donot yet have adequate internal financial controls andcompetencies. This is usually short lived, however, anda VC will actively aid in recruiting such competenciesfor a company. VC-financed firms are more likely andfaster to professionalize by adopting stock option plansand hiring external business executives, such as a vice-president of sales, or an external CEO (Hellman &Puri, 2000b).

As stated earlier, VCs will only invest in anopportunity if there is a good likelihood of someliquidity event within their five to seven year invest-ment horizon.

Corporate VenturingCorporations have also experimented with fundinginnovation directly through corporate venturing pro-grams that seek to emulate the venture capital industry.The popularity of corporate venturing appears to riseand fall in approximately ten-year cycles with the

18 These questions have actually been copyrighted by anOttawa consulting company, Reid-Eddison.19 There is a saying in the venture capital community that ‘thethree important things about a deal are people, people andpeople’. A variation on this is that ‘the five most importantthings about a deal are people, people, people, market andproduct’. Good people will find good opportunities, and moreimportantly, be able to execute on them.

20 This was not the case during the Internet and dot.com‘bubble’ when VCs commonly paid out the full amounts of aninvestment stage in one check.

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venture capital industry and the broader equity markets(Block & MacMillan, 1995). Activity peaked inthe late 1960s (Fast, 1978), mid-1980s (Yost, 1994)and late 1990s, declining again each time at the nextmarket downturn (Chesbrough, 2000).

Corporate venturing includes ‘intrapreneurship’ pro-grams to incubate and spin-off new entrepreneurialfirms from within the corporation, as well as cor-porate venture capital funds (CVC) that investcorporate money directly in external start-ups inexchange for equity and control rights.

Examples of corporate venturing programs includeXerox Technology Ventures (1989–1995) documentedby Hunt & Lerner (1995), Chesbrough & Smith (2000),and Chesbrough & Rosenbloom (2002); the LucentNew Ventures Group documented by Chesbrough &Socolof (2000); and the Nortel Networks New BusinessVentures program (1997–1999), documented byO’Connor & Maslyn (2002), Leifer et al. (2001) andHyland (2002).

Corporate venturing can provide favorable returnswhen compared to the returns from independentventure funds. During its eight-year lifetime, the $30MXTV fund invested in over twelve ventures, deliveringcapital gains of $219M. Hunt & Lerner (1995) estimatethat $175M returned to Xerox, suggesting a 56%internal rate of return compared to a mean net return of13.7% by independent VC funds over the same timeperiod. Nonetheless, the program was discontinued,21

underscoring the significant challenges of fosteringentrepreneurship within large corporations.

Chesbrough’s (2000) survey of the corporate ventur-ing literature identifies several specific challenges thatthese initiatives face, including adverse selection,resource allocation conflicts, conflicts of interestbetween the new venture and parent sponsor, andpotential conflict of objectives between financial andobjectives.

Von Hippel (1977) identified the problem of adverseselection. Over time, the best performing ventureseither spin-off or migrate to other divisions, leaving thecorporate venturing organization with the under per-forming ventures. Fast (1978) noted that managers ofestablished businesses can view successful corporateventures as threats which compete for scarce resources.Rind (1981) explored possible conflicts of interestwithin new venture organizations between the successof the parent sponsor and the success of the newventure. The sponsor may constrain the marketingoptions of the new venture in order to preventcompetition with existing businesses. Siegel, Siegel &MacMillan (1988) explored the potential conflictbetween two frequently cited rationales for newventure businesses. Strategic investments seek to

exploit the potential for additional growth latent in theparent sponsor—in other words, improve the perform-ance of existing businesses. Financial investments aimto create additional revenue and profit in the newventure itself. According to Siegel et al., parentalintervention to align the venture with strategic interestsreduces the autonomy of the new venture, and likelyreduces financial performance.

Chesbrough (2000) proposes that corporate venturestructures and venture capital structures have somesignificant differences. Compared to venture capital,corporate venturing provides weaker incentives forsuccess, weaker financial discipline on the downside(i.e. slower to terminate under performing ventures),internal (rather than external) monitoring, and con-straints on the discovery of alternative business models(Chesbrough & Rosenbloom, 2000). Potential advan-tages include longer investment time horizons(unconstrained by the fixed lifetime of a VC fund),larger scale of capital investment, management ofstrategic complementarities, and the retention of grouplearning. Chesbrough argues that for corporate ventur-ing to succeed and persist through the less exuberantmarket cycles, it must leverage these potential advan-tages to deliver strategic benefits to the sponsoringfirm.

Von Hippel (1977) showed that corporate ventureswere more likely to succeed when the parent firm hadsignificant prior experience in the target market.Experience with the technology, however, did notcorrelate to increased likelihood of success. Athey &Stern (1997) introduced complementarity—the notionthat corporations can benefit from closely relatedactivities. Research suggests that intrapreneurship andCVC programs are both more effective when investingin businesses that are closely related to the corecompetencies of parent. In a comparison of VC andCVC investments, Gompers & Lerner (1999c) foundthat corporations may be able to select better venturesusing information from their related businesses andprovide greater value to those firms once the invest-ments are made. CVC programs without a well-definedstrategic focus have less investment success and lessstability than well-defined programs. Likewise, thesuccessful investments of the Xerox Technology Ven-tures program were concentrated in industries closelyrelated to corporate parent’s business (Hunt & Lerner,1995).

Venture Capital Decision CriteriaEarly venture capital studies established that venturecapitalists make investment decisions based on analysisof financial fundamentals rather than intuition or ‘gutfeel’ (Pence, 1982). Subsequent work has madeprogress towards elucidating the details of this proc-ess.

MacMillan et al. (1985) conducted a frequentlyreferenced study on the decision criteria of U.S.

21 For an analysis of the motivations behind this decision, seeHunt & Lerner (1995).


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venture capital firms. The criteria most frequently ratedare shown in Table 5.

The quality of the people is the most importantfactor; six of the top ten criteria in Table 5 relate tocharacteristics of the founding entrepreneurs. Experi-enced founders significantly increase the attractivenessof a venture (Bhidé, 2000). Having people on the teamthat ‘have done it before’—that is, who have previouslybuilt a start-up opportunity to create shareholdervalue—is regarded very positively by VC investors.

Other considerations include the size and accessi-bility of the total available market, the growth rate ofthis market, customer traction, and the technology ofthe product. Customer traction is highly valued andpaying customers are best. VCs typically bring inresident or contract technical experts to evaluate theproduct design and technology. They also interviewprospective customers.

Kaplan & Stomberg (2000b) analyzed investmentmemoranda and subsequent status reports from ten VCpartnerships for 58 investments in 42 portfolio com-panies. Their results confirm that VCs expend a greatdeal of time and resources evaluating and screeningtransactions. VCs explicitly consider the attractivenessof the opportunity (market size, strategy, technology,customer adoption, and competition) and risk. Manage-ment risk is cited in 60% of sample investments, mostoften related to a need to complete the team withseasoned executives. Management risk is correlatedwith contract restrictions (particularly voting and boardseats) and cash flow restrictions based on performance.The early appraisal of the management team wasrelated to subsequent performance; in particular, port-folio companies with strong management teams weremore likely to go public.

Shane & Stuart (2002) studied the social capital ofcompany founders. New ventures with founders havingdirect and indirect relationships with venture investorswere more likely to receive venture funding and lesslikely to fail. They conclude that founder social capitalrepresents an important endowment for early-stageorganizations. Either the venture capital decision

criteria are partially subjective or they objectively placevalue on established relationships beyond that observedin previous studies.

Shepherd & Zacharakis (2002) suggest that researchinto decision aids can potentially improve the venturecapital decision process, decision accuracy, and speedup the acquisition of expertise. This is a promisingavenue for future research.

The Venture Capitalist—EntrepreneurRelationshipThere has been a significant amount of research doneon the venture capitalists entrepreneur relationship.22

Frequent calls are heard for more since the area is oneof great importance (Sapienza & Korsgaard, 1996;Steier & Greenwood, 1995). The theoretical frame-works used for most of this research have come fromeconomics—for example, agency theory and incom-plete contracts.23 These economic theories focus on thenatural conflicts that exist between investors andentrepreneurs, and remedies based on factors such asgovernance structure, restrictive covenants, stage dis-bursement of funds and investor oversight (Giudici &Paleari, 2000).

The usual application of agency theory to theventure capitalists—entrepreneur relationship modelsthe venture capitalist as the principle and the entrepre-neur as his agent. Once the venture capitalist invests ina new venture, he or she is interested in financialsuccess that results in an early liquidity event such asacquisition or IPO. The entrepreneur shares thisobjective to a certain extent but has other interests aswell such as compensation, management perks, career,survival of the business, and building the businessbeyond liquidity.

Just differences in risk exposure can lead tosignificant agency type conflicts. Venture capitalists arerelatively well diversified as investors; entrepreneursare not. Based on financial theory, this means thatventure capitalists are concerned with systematic riskrelated to the market as a whole, whereas entrepreneursare concerned with the total risk of their investment intheir venture. Callahan & Sharp (1985) show analyt-ically that this difference in risk exposure leads togrowth objectives that differ between entrepreneursand venture capitalists.24 As part of their model,

22 (Bhidé, 2002; Baker & Gompers, 1999; Bratton, 2002;Gifford, 1997; Gompers, 1999; Gompers, 1995; Gompers &Lerner, 1996; Jog et al., 1991; Kaplan & Stromberg, 2001,2002; Lerner, 1995; MacMillan et al., 1989; Steier &Greenwood, 1995; Sahlman, 1990; Sapienza & Korsgaard,1996; Sweeting, 1991; Sweeting & Wong, 1997.)23 The first important article on agency theory was Jensen &Meckling (1976). See Hart (2001) for recent review of theagency theory literature. See Hart & Moore (1990) for anintroduction to incomplete contracts.24 Callahan & Sharp’s simple analytic argument is excerptedand shown in Appendix A.

Table 5. Opportunity evaluation criteria.

Criteria Percent

Capable of sustained effort 64Thoroughly familiar with market 62At least 10 � return in 5–10 years 50Demonstrated leadership in the past 50Evaluates and reacts well to risk 48Investments can be made liquid 44Significant market growth 43Track record relevant to venture 37Articulates venture well 31Proprietary technology 29

Source: MacMillan et al. (1985, p. 123).

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Callahan & Sharp also show that conflicts betweenentrepreneurs and venture capitalists are likely to ariseover such issues as managerial compensation andperquisite consumption. Jog et al. (1991) showed theseconflicts to exist empirically using a questionnairesurvey of Canadian VC partners.

The book, The Venture Capital Cycle, by Gompers &Lerner (1999) contains excellent examples of researchon the venture capitalist—entrepreneur relationshipbased on agency theory. In Chapter 7, Why areInvestments Staged?, they conclude that VCs stagetheir investments in new ventures because of a concernthat entrepreneurs with inside information will con-tinue spending investor money even when faced withlosing prospects because they stand personally to losesalary, perks, and reputation. In Chapter 8, How DoVenture Capitalists Oversee Firms?, they examine therole of VCs as directors of their portfolio companies.They conclude that the representation of VCs, unlikeoutside board members, increases around the time ofCEO turnover.25 They also find that geographic prox-imity is important for VC board members. In Chapter9, Why Do Venture Capitalists Syndicate Investments?,they find that established VCs syndicate with eachother in first round financing. In subsequent rounds,they involve less-established VCs. They attribute theseresults to the uncertainty in the first round—havinganother established VC also willing to invest is animportant decision factor. They also find support forthe contention that syndication is a way around theunfair information advantage that would accrue to thelead VC in subsequent rounds, that will probablydemand syndication because of amounts of moneyrequired, if they go in alone at first.

Procedural justice theory provides another theoret-ical framework for Sapienza & Korsgaard (1996) toexamine entrepreneur—investor relations.26 They car-ried out two studies: a simulation study using studentsand a survey questionnaire study of VC partners. Theirprinciple conclusion is that ‘timely feedback promotedpositive relations between entrepreneurs and investors’.They suggest that entrepreneurs yield a level of controland share information so that investors will eschewmonitoring, and trust and support the entrepreneurs.This suggestion is congruent with advice that comesfrom agency theoretic analyses of the venture capital-ist—entrepreneur relationship.

Game theory also provides a different perspective onthe venture capitalist—entrepreneur relationship.Cable & Shane (1997) use a prisoner’s dilemma

approach to develop a number of testable hypothesesthat emphasize the cooperative alternatives to mutualvalue creation by venture capitalists and entrepreneurs.This perspective builds on Timmons & Bygrave’s(1986) finding that “an ongoing cooperative relation-ship between entrepreneur’s and venture capitalists ismore important to the performance of the venture thanthe provision of venture capital itself” (Cable & Shane,1997, p. 143). Cable & Shane (1997, p. 168) maintainthat:

Modeling venture capital relationships as a princi-pal-agent problem appears unduly restrictive giventhe potential for opportunistic, non-cooperativeactions by venture capitalists as well as entrepre-neurs. For example, while the agency approachfocuses on venture capitalists’ adverse selectionproblem when evaluating entrepreneurs, an adverseselection problem also exists for entrepreneurs sincethey must locate venture capitalists who can providecomplimentary managerial experience, access torelevant networks, and legitimacy.

Two recent empirical papers (Schefczyk & Gerpott,2001a; Schefczyk & Gerpott, 2001a) have referencedCable and Shane’s paper but do not build specificallyon their hypothesis structure.

Shepherd & Zacharakis (2001) emphasize the neces-sity of a balance between trust and control in theventure capital—entrepreneur relationship. They pro-pose that the entrepreneur can

build trust with the VC (and vice versa) by signalingcommitment and consistency, being fair and just,obtaining a good fit with one’s partner, and withfrequent and open communication.

They regard their study as a counter weight toeconomic approaches like agency theory in whichcontrol is emphasized.

Venture Capital and InnovationDoes venture capital foster innovation? There are threepopular arguments:

(1) venture capital unleashes innovation. VCs freeinnovative firms from capital constraints and addgenuine value that helps them become successful;

(2) venture capital is neutral to innovation. VCsidentify the best new ventures, and are theintermediary gatekeepers for funding;

(3) venture capital stifles innovation. VCs back onlyconventional ideas. Unconventional innovativeventures are screened out as too risky, and neverreceive funding.

The research to date is inconclusive. The jury is stillout on this very important question.

The venture capital research is clear in one regard—VC-backed firms are more successful than non-VCbacked firms, both before and after IPO. Venture-

25 A similar conclusion is drawn by Gabrielsson & Huse(2002).26 Procedural justice theory (Lind & Tyler, 1988) examines theimpact of the process of decision-making on the quality ofexchange relationships. See De Clercq & Sapienza (2001) fora theoretical application of both agency and procedural justicetheories to venture capitalist—entrepreneur relationships.


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backed firms bring product to market faster (Hellman& Puri, 2000b), ‘professionalize’ earlier by introducingstock option plans and hiring external business manag-ers (Hellman & Puri, 2000a), time IPOs moreeffectively to the market (Lerner, 1994), and havehigher valuations at least five years after IPO (Gompers& Brav, 1997). Venture-backed IPOs pay lower feesand are less under priced. (Megginson & Weiss,1991).

Causation, however, is more difficult to establish. Doventure capitalists add value that makes it more likelyfor their portfolio firms to succeed, or are they simplygood at picking winners?

Research suggests that VCs do have some on impacttheir portfolio firms. Hsu (2000) compares a group ofVC-backed start-ups with a control group of start-upsthat obtained government funding through the U.S.Small Business Innovative Research (SBIR) program,which does not impact ownership or governance. Thestudy concludes that venture capital changes the pathof funded projects, by altering the commercializationstrategy and making the firm more sensitive to thebusiness environment.

Other studies imply that there are limitations to thevalue added by VC influence. Ruhnka, Feldman &Dean (1992) investigated the strategies employed by 80venture capital firms to deal with the ‘living dead’investments in their portfolios—ventures that wereself-sustaining but failed to achieve levels of growth orprofitability necessary for attractive exits such as IPOor acquisition. Venture managers were able to achievea successful turnaround or exit in 55.9% of living deadsituations, regardless of the age of the VC firms, theirsize, or the relative availability of investor personnelfor monitoring investees. From the invariance of thisresult, the authors argue that that causal factors areoutside VC control.

Some promising recent work supports the notion ofa causal link between VC and innovation. Kortum &Lerner (2000) investigated trends in patent rates as ameasure of innovation. Statistical analysis showed thatthe rate of U.S. patent filing was correlated with early-stage venture capital disbursements, when controllingfor corporate research and development expenditures.In particular, the rate of patent applications declinedduring the 1970s and early 1980s while corporateresearch and development spending increased steadily.The rate of patent applications steadily increased after1985, following the rapid rise of early-stage venturecapital disbursements in the late 1970s and early1980s.

Some anecdotal accounts, however, present a differ-ent picture. According to Tredennick (2001), venturecapitalists and their technical experts actually favorvery conventional and proven ideas: “If you step too farfrom tradition, (the VC) will not understand orappreciate your approach . . .. Just as Hollywood wouldrather make a sequel than produce an original movie,

VCs look for a formula that has brought success”.According to Bhidé (2000):

VC-backed entrepreneurs face extensive scrutiny oftheir plans and ongoing monitoring of their perform-ance by their capital providers. These distinctiveinitial conditions lead them to pursue opportunitieswith greater investment and less uncertainty, relymore on anticipation and planning and less onimprovisation and adaptation, use different strategiesfor securing resources, and face different require-ments for success.

Both accounts suggest that the venture capital processmay actually screen out the most significant innova-tions in favor of minor variations of what has comebefore.

Other research suggests that venture capitalistsfrequently engage in ‘herding’—making investmentsthat are very similar to those of other firms—or whatTredennick (2001) calls ‘riding the crest of a fad’.Devenow & Welch (1996) show that a variety of factorscan lead to investors obtaining poor performance.Social welfare may suffer because value-creatinginvestments in less popular technology areas may havebeen ignored.

During the Internet and dot-com ‘bubble’ of the late1990s and early 2000, many startup ventures receivedlarge disbursements of very early venture capitalfunding. Since the collapse of the bubble, anecdoteshave emerged describing the destructive effects of suchlarge amounts of early money. The business model of astartup venture is like an untested hypothesis—the realtest is making a profit from paying customers.Availability of early money can hide problems in abusiness by delaying such a test. Some very early stagestartups redefined success in terms of financing—achieving the first (or the next) venture capitalinvestment round. Bootstrapping, the creation of asignificant business without significant outside financ-ing, is again becoming popular because of therelatively limited supply of venture capital money—and it may not be a bad development. Importantbootstrapped success stories include household nameslike Dell, Gillette, Heinz, HP, Mattel, Nike, Oracle,UPS, and Walt Disney.

The differences between bootstrapped and ‘bigmoney’ startups are summarized in Table 6. Boot-strapping forces focus on cash flow and the immediateneeds of customers in niche addressable markets. Freedof cash flow constraints, big money startups can try forhighly engineered product ‘home runs’ with a view ofstriking it rich and cashing out. Big money allows forsignificant compensation packages, so the personalsacrifice of principals can be very low. When one readsabout big money startups, the news all too often centerson their financing progress rather than success with realcustomers.

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In conclusion, venture capital would appear to atleast help bring innovation to market. However, theselection process may not always identify and fundthe most significant innovations, and especially intimes of abundant supply, there may be disadvantagesto ‘big money’. Over all, venture capital may be apositive force to drive innovation—but the jury is stillout.

The substantial body of financial and econometricsresearch offers few insights on innovation. More workis needed to specifically isolate and disentangle theinfluence of venture capital from that of other marketforces, and relate that influence to innovation and thepublic good. Anecdotal accounts from seasoned practi-tioners and observers on possible limitations anddrawbacks of venture financing remain untested withaccepted research methods.

As Zider (1998) states:

The (venture capital system) works well for theplayers it serves: entrepreneurs, institutional inves-tors, investment bankers, and the venture capitaliststhemselves. It also serves the supporting cast oflawyers, advisers, and accountants. Whether it meetsthe needs of the investing public is still an openquestion.

Stakeholders and ResearchResearch on the role of VCs in innovation is carried outbecause there is a market for it. Researchers areproducers. Consumers—the stakeholders in the out-put—are varied. They include entrepreneurs, venturecapitalists, investors, policy makers, and researchersthemselves.

Entrepreneurs need guidance on how to:

• approach VCs so as to maximize their chances ofgetting a good deal;

• appraise and judge VCs one from another;• negotiate and structure equity investment deals;• structure the participation of VCs on their boards and

manage this participation subsequently;

• maintain control of their companies as they acceptoutside ownership;

• negotiate and work with VC set milestones;• get through a liquidity event like an acquisition or an

IPO effectively.

VCs are interested in how to:

• find good opportunities;• screen and appraise good opportunities;• negotiate and structure equity investment deals;• support and control client companies;• terminate client company relationships.

Investors need help in how to:

• judge the investment records of VC firms;• appraise VC competencies in specific fields of

investment;• appraise the risk/return potential of VC investments;• negotiate investment terms and covenants.

Policy makers include government officials and partici-pants in quasi-government bodies such as the U.S.Federal Reserve Bank. They need guidance in how to:

• set tax policy;• regulate investor access to VC funds;• support VC activity that increases entrepreneurial

value creation.

Researchers and educators, mainly in universities,want:

• research tools;• interesting testable hypotheses;• theories for creating new testable hypotheses;• theories for explaining and teaching.

We can see that the interests of these consumerstakeholder groups are not the same.

Most research relevant to the role of venture capitalin innovation follows the normal cycle of positivistscientific inquiry developed in the natural sciences:description of some phenomenon; theorizing thatresults in interesting, testable hypotheses; data gather-ing based on the hypotheses; hypothesis tests; and thenrenewed theorizing. Another approach to theory build-ing is qualitative methods (Bailyn, 1977; Schall, 1983).Researchers using this methodology gather largeamounts of text data, often interview transcripts, andthen analyze the data for generalizable wisdom. Theydo not build theory in terms of testable hypotheses.Qualitative theory building and theory testing areclosely interrelated—rather than being rigidly distinctas in positive theory building and testing. A variant ofqualitative method based on case studies (Yin &Campbell, 2002) is used in the initial, descriptive stageof positive theory building.

Most large sample hypothesis tests are cross-sectional. They gather and analyze data on cases at apoint in time. As a result there is little evidence of

Table 6. Differences between bootstrap and big money start-ups.

Bootstrap Big Money

Cash earn it other people’sInitial focus customers exitProduct incremental fully featuredMarkets niche $1BOrg. Structure fluid rigidTime Horizon near term long termMedia Profile low highPersonal Sacrifice high low

Source: Presentation by Ken Charbonneau, Partner, KPMG,Ottawa, Carleton University, Magic from a Hat entrepreneur-ship lecture series, November 11, 2002.


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interrelationships between variables in each case. Suchinterrelationships are investigated across cases usingmultivariate statistical methods like regression.Qualitative methods can be used more easily to gatherlongitudinal data on a case over time. Then variableinteractions within a single case can be investigated.Qualitative longitudinal data gathering, however, isvery resource intensive. Processual research (Dawson,1997; Hinings, 1997; Pettigrew, 1997; Woiceshyn,1997) is one specific form of qualitative analysis.

The total effort of a research study is usuallyconstrained by the availability of some resource liketime or money. Given such a constraint, there is anatural trade-off between the number of cases in asample and the amount of information gathered foreach case. When testing hypotheses, it is normal togather small amounts of very specific data on a largenumber of cases. Hundreds, even thousands, of casesabout which little is known. When using case studies todevelop theory rather than test it, large amounts ofinformation are gathered on a very small number ofcases. Research studies with one case can be published(Steier & Greenwood, 1995). Nested studies of severalcases are more rigorous (Yin & Campbell, 2002) butare rare since they are very resource intensive.Qualitative studies tend to lie between these twoextremes—tens of cases with significant amounts ofdata on each one.

These different research approaches can be com-plementary, but they appeal to stakeholders in differentways. Investors, policy makers and researchers can besatisfied with averages because they are interestedeither with long run effects or with large numbers ofsituations. The covariance-based techniques of largesample statistics can satisfy them. This is not true ofentrepreneurs and venture capitalists. A typical entre-preneur would only be involved in a few startupventures in his or her entire lifetime. Entrepreneursdeal with particular, specific, negotiated situations.They create new ventures, and are seldom interested inaverages. In fact, they quite explicitly do not referencetheir situations to the average. Even venture capitalistsdeal with few enough investments that they do notreally trust in averages or the law of large numbers toassist them. Both entrepreneurs and venture capitalists,as is common for business decision makers, testtheories against their experience and intuition ratherthan using large scale statistical methods. Researchmust work to assist these decision makers to improvetheir intuition.

There is a striking lack of research on the role ofVCs in innovation that provides rich insights intospecific situations, and that can form the basis foreffective theorizing as a result. Steier & Greenwood(1995) provide a notable exception. They document indetail the experiences of a single entrepreneurial newventure in the deal structuring and post-investmentstages of venture capital involvement. Another is the

Zaplet Inc. case study by Leonard (2001). Leonarddescribes how the lead VC, Vinod Khosla, played anatypically active—even dominant—role in reformulat-ing the business strategy and management structure ofa very early start-up. There is a need for the equivalentof the studies by Burns & Stalker (1994, originallypublished in 1961) and Poole et al. (2000) that havebeen carried in innovation. Consider even the book,Startup, by Kaplan (1994). The book is a breathless,first person account of the story of GO Corporation, astart-up that tried and failed to develop and commer-cialize a hand held computer operated with a peninstead of a keyboard in the early 1990s. The book isnot research—but the chapter on financing (Kaplan,1994, pp. 59–81) contains more information useful toan entrepreneur than does the MacMillan et al. (1985)VC decision investment model referred to earlier.

We do not pretend that this call for more qualitative,processual research, and with it better stories, is new.27

It does seem particularly important in the area ofventure capital’s role in innovation.

Financial Data SourcesThe financial and economic data for this chapterwas taken from the following sources: global privateequity from PricewaterhouseCoopers (2002); venturecapital investment in the United States from thePricewaterhouseCoopers/Venture Economics/NationalVenture Capital Association MoneyTree Survey, availa-ble online (http://www.pwcmoneytree.com), VentureEconomics (http://www.ventureeconomics.com), andthe National Venture Capital Association (NVCA)(http://www.nvca.com); venture capital investment inCanada from Macdonald and Associates (http://www.canadavc.com); venture capital investment inEurope from the European Venture Capital Association(http://www.evca.com); venture capital investment inAsia from the Asian Venture Capital Journal (http://www.asiaventure.com).

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AppendixEntrepreneurs and Venture Capitalists: Differencesin Growth ObjectivesExcerpted from Callahan & Sharp (1985)

The ProblemThe typical entrepreneur is not diversified; as far asfinancial risk is concerned he has most of his personalfortune and human capital in the new venture. As anactive manager, he receives a salary and fringe benefitsfrom the company during its start-up and initialstages.

The typical venture capitalist is well diversifiedfinancially. Unlike the entrepreneur, he has a financial,rather than a personal relationship with the company.The venture capitalist is interested in the overallperformance of a portfolio of investments. He is not anactive manager, nor does he want to be involved in day-to-day management. He exerts control by constraintthrough protective covenants and membership on theboard of directors. The typical venture capitalist


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receives no immediate cash flow from an investmentbut rather is interested only in capital gains.

The points of conflict between the entrepreneur andthe venture capitalists are twofold:

(i) the amount of salary and fringe benefits consumedby the manager/entrepreneur; and

(ii) the growth rate of the company, resulting fromstrategy decisions that affect growth such asproducts and markets, pricing and the relativefocus or diversification of the company.

We outline a model that illustrates how these twoconflicting points of interest arise in a natural andlogical way given the differences in diversification ofthe entrepreneur and the venture capitalist, and the factthat the entrepreneur receives salary and fringe benefitsas a manager.

The ModelThe complete model comprises three sections:

The Basic Valuation ModelThe basic valuation model used is an adaptation of thecapital asset pricing model of Stapleton (1971). Thebasic idea behind Stapleton’s model is that the presentvalue of an uncertain cash flow can be considered asthe certainty equivalent of the value of the cash flowdiscounted to the present value at the risk free rate.Stapleton further argues that in a capital asset pricingmodel world this certainly equivalent is a linearfunction of the expected value and the standarddeviation of cash flow.

In Stapleton’s notation, DV is the discounted valueoperation using the risk free rate. Thus if C is someuncertain cash flow anticipated at time n and I is therisk free rate

DVC = C/(1 + i)n (1)

The present value of C for a diversified investor is

E(DVC) � S · RCM · �(DVC) (2)

where RCM is the correlation between C and the returnon the market (as defined in the capital asset pricingmodel) and S is a market determined risk aversionfactor. In a sense, RCM · �(DVC) is the relevant riskmeasure for a diversified investor since he is concernedonly with systematic risk, being able to eliminateunsystematic risk through diversification. For an undi-versified investor though, the relevant risk factor is�(DVC) and the present value of C is

E(DVC) � S�(DVC) (3)

We shall assume that the company goes public after nperiods and that both entrepreneur and venture capital-ist can (but do not have to) wind up their positions atthis time. We also assume that the net value of thecompany at this time, V, increase with the averagegrowth rate, g, of the company. This is a weak

assumption implicit in most conceivable companyvaluation models.

The Entrepreneur ModelWe assume that the manager/entrepreneur receives aperiodic wage, w, that is not random. It is possible toconsider a random wage, or one that is performancerelated, but that would add little to the analysis. Wefurther assume that the entrepreneur owns a portion �of the equity of the company and that � remainsconstant. Then the present value of the entrepreneur’sclaims on the company, V1, is a function of g and wgiven by

V1 = V1(g,w) (4)

= w · {[(1 + i)n � 1]/i} + � · {E/(1 + i)n

� S · �/(1 + i)n} (5)

= w · {[(1 + i)n � 1]/i} + {�/(1 + i)n}

· {E � S · �} (6)

where E and � are the mean and standard deviation ofV respectively and the relevant risk for the entrepre-neur as an undiversified investor is the total risk.

The Venture Capitalist ModelThe present value of the venture capitalist’s claim onthe company, V2, is given by

V2 = V2(g,w) (7)

= {(1 � �)/(1 + i)n} · {E � S · RVM · �} (8)

where RVM is the correlation between V and the returnon the market as a whole given that the venturecapitalist is a diversified investor.

The AnalysisTaking partial derivatives we have

�V1/�w = {[(1 + i)n � 1]/i} + {�/(1 + i)n}

· {1 � S · ��/�E} · �E/�w (9)

�V1/�g = {�/(1 + i)n} · {1 � S · ��/�E} · �E/�g (10)

�V2/�w = {(1 � �)/(1 + i)n}

· {1 � S · RVM · ��/�E} · �E/�w (11)

�V2/�g = {(1 � �)/(1 + i)n}· {1 � S · RVM · ��/�E} · �E/�g (12)

We assume that

�E/�w < 0 (13)

i.e. the higher the wages paid to the entrepreneur thelower the expected terminal net value of the company.By virtue of our assumption that a higher growth rateincreases the expected terminal value of the company,we also have

�E/�g > 0 (14)

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We shall examine the extent to which the objectives ofthe venture capitalist and entrepreneur are congruentby considering whether a wage (w) and growth rate (g)exist that simultaneously optimize V1 and V2. Becausethe power of the venture capitalist at the stage ofventure capital financing of the company is usuallygreater than that of the entrepreneur, we start with theventure capitalist’s point of view.

Now the venture capitalist wants values of w and gsuch that

�V2/�w ≥ 0 and �V2/�g ≥ 0

i.e. such that from (11) and (12)

�E/�w ≤ 0, {1 � S · RVM · ��/�E} ≤ 0 and

{1 � S · RVM · ��/�E} ≥ 0

or

��/�E = 1/{S · RVM} (15)

In all probability, RVM will be small so that the optimalvalue of E for the venture capitalist will be large.

For the entrepreneur, if we set

��/�E = 1/{S · RVM}

we have from (9)

�V1/�w = {[(1 + i)n � 1]/i} + {�/(1 + i)n}

· {1 � 1/RVM} · �E/�w > 0 (16)

and from (10)

�V1/�g = {�/(1 + i)n} · {1 � 1/RVM} · �E/�g < 0 (17)

In other words, when the entrepreneur’s wage andgrowth rate of the company are set at values that areoptimal for the venture capitalist, the entrepreneurwants a higher wage and a lower growth rate for thecompany.

As can be seen there is no prospect of simultaneousoptimization of V1 and V2. The values of the growthrate and wage that optimize the venture capitalist’svalue, V2, certainly do not optimize the entrepreneur’svalue, V1.

Equations (16) and (17) present the entrepreneur’sview of the dilemma. Equation (16) merely confirmsthe intuition that the entrepreneur is better off with ahigher wage. Equation (17) conceals a little more.Venture capital is most likely to be used as a method offinancing for companies in new and innovative indus-tries. There is a case for arguing that the earnings ofsuch industries, unrelated as they are to the matureindustries whose earnings represent the bulk of themarket index, will show an unusually low correlationwith the market index. It is plausible then that the term1/RVM is much greater than 1 so that the term{1 � 1/RVM} is considerably less than zero. In otherwords the optimum growth rate as seen by the venturecapitalist is way above that perceived by the entrepre-neur as ideal. At a growth rate that appears attractive tothe venture capitalist Eq. (17) shows that the value tothe entrepreneur is decreasing rapidly.

ReferenceStapleton, R. C. (1971). Portfolio analysis, stock valuation

and capital budgeting decision rules for risky projects. TheJournal of Finance, 26 (1), March, 95–117.


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Encouraging Innovation in Small FirmsThrough Externally Generated Knowledge

Edward Major1 and Martyn Cordey-Hayes2

1 Bedfordshire Police Force and Middlesex University, U.K.2 Cranfield University, U.K.

Abstract: This chapter examines the conveyance to small firms of externally generatedknowledge. Successful innovation requires firms to draw on multiple sources of knowledge.Many small firms take little note of external sources, thus restricting their potential innovativebase. We develop the concepts of knowledge translation and the knowledge translation gap toillustrate why so many small firms fail to access externally generated knowledge. Findings fromresearch into U.K. small firms and national innovation schemes show how intermediaryorganisations can be used to bridge the knowledge translation gap. Implications follow forgovernment innovation schemes, for intermediaries and for small firms.

Keywords: Innovation; Small firms; External knowledge; Knowledge translation; Inter-mediaries.

IntroductionThe rise to prominence of the field of knowledgemanagement (see Farr, Sin & Tesluk, 2003) giveswitness to the centrality of knowledge in fosteringinnovation. Knowledge underlies innovation. It is theprecursor, in many ways the lubricant, of innovation.Firms need knowledge to build innovative potential.

Innovation is ‘the introduction of novelties’ or ‘thealteration of what is established by the introduction ofnew elements or forms’.1 It thus concerns introductionand progression rather than invention. Innovation hasbeen adjudged the main determinant by which com-mercially successful organisations derive competitiveadvantage (Tidd, Bessant & Pavitt, 1997). Successfulinnovation must involve several parties. A knowledgesource is needed to support the innovating party (i.e. toprovide the new invention/idea). Innovation within afirm might be based on knowledge held by that firm.Even so, the source of the knowledge contributingtowards an innovation may be separated from theactual innovating party. Alternatively, the innovatingfirm might need to go to external sources for

the knowledge it needs. In this case the distancebetween knowledge source and innovating party isincreased. For successful innovation, knowledge needsto be successfully conveyed. The conveyance, ortransfer, of knowledge forms a third element in theprocess, between the knowledge source and theinnovating party. This chapter considers the convey-ance into small firms of externally generatedknowledge. It thus compliments Hadjimanolis’s study(2003) of the internal and external barriers to innova-tion that firms face. Understanding this conveyanceprocess has implications both for the presentation ofgovernment innovation schemes and policy initiativesand for the small firms themselves.

The chapter begins with a brief discussion of thebarriers to innovation in small firms (the innovatingparty). Consideration then turns to the knowledgesources contributing to innovation. Sources of exter-nally generated knowledge are emphasized, includinggovernment innovation schemes and policy initiatives.The concept and models of knowledge transfer providethe basis for examination of the conveyance of externalknowledge. The chapter develops knowledge transferinto a model of knowledge translation that enables aclear understanding of the conveyance process to bebuilt. Findings are then reported from research into

1 As defined in the Oxford English Dictionary, OxfordUniversity Press, 1933, reprinted 1978.

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small firms and innovation schemes in the U.K.Applying the knowledge translation model to thisresearch reveals how intermediary organizations can beused to reach different types of small firms. Implica-tions are drawn for the conveyance process itself, forgovernment innovation schemes and policy initiatives,for intermediaries and for small firms. Recommenda-tions are made which are aimed at enhancing the flowof externally generated knowledge, therefore encourag-ing successful small firm innovation.

Barriers to Innovation in Small Firms

Statistics show that in 1999, 99.8% of all U.K.businesses employed less than 250 people. These smalland medium sized enterprises (SMEs) accounted for55% of national employment and 51% of nationalturnover. The 0.2% of businesses rated as largecontributed less than half of the nation’s employmentand turnover (Department of Trade and Industry,2001). Other developed economies are similar. Storey(1994) records that businesses with less than 500employees account for between 62% and 91% ofnational employment in all member states of theEuropean Community. Figures for Japan and theUnited States are of the same order (Small BusinessAdministration, 2000; Storey, 1994). Whichever waythey are looked at, small firms make an immensecontribution to wealth and employment.

Innovation is favored by the small firms’ advantagesof flexibility and reduced bureaucracy. But theirinnovative potential is hampered by inherent problemsnot faced by large firms. Rothwell & Zegvelt (1982)identify four weaknesses particular to small firms.First, limited manpower restricts their ability toperform competitive R&D. Small firms are likely tohave a much lower proportion of their personnel,possibly none, concerned exclusively with R&D thanlarge firms (Storey, 1994). Many small firm personnelhave more than one role. Second, small firms havelimited time and resources to devote to externalcommunications. This limits the information base fromwhich decisions can be made. Low in-house employ-ment of specialists restricts communication andnetwork formation with outside sources of expertise(Rothwell, 1991). Third, is excessive managementinfluence. Small firms are much more prone todomination by a single manager or team that may useinappropriate skills or strategies. Fourth, small firmscan have difficulties raising finance. Financial barriersto innovation in small firms are variously reflected as alack of capital (Garsombke & Garsombke, 1989;Keogh & Evans, 1999) and unfavorable bank policieson credit (Hadjimanolis, 1999). Small firms of courseare varied and different, as will be shown below. Theirinherent weaknesses (and advantages) will be presentto varying degrees.

Sources of Externally Generated Knowledge

The traditional way for a small firm to build itsknowledge base is to generate knowledge internally.Firms use formal and informal research and develop-ment to grow their internal knowledge. In this internalprocess, research may be considered the knowledgesource and development the means of conveyance tothe firms’ innovation centre (its decision-makers).Internally generated knowledge has the advantage ofclose proximity to the firm’s decision-makers. Bydefinition it lies within the firm’s boundaries. Ofcourse, close proximity does not ensure that internalknowledge will have any impact. It also suffers fromRothwell & Zegvelt’s (1982) first weakness, limitedmanpower to devote to internal R&D.

By concentrating on internal R&D many firmsneglect the role of externally generated knowledge.Small firms, with their limited resources are partic-ularly prone to neglecting external knowledge. Theyare disadvantaged when compared to larger com-petitors who may be able to dedicate specific resourcesto examining external knowledge sources. Woolgar,Vaux, Gomes, Ezingeard & Grieve (1998) present aframework showing the environment surrounding asmall firm. Their ‘SME-centric universe’ (Fig. 1) givesthe basis for a categorization of a firm’s externalknowledge sources.

The Immediate Business EnvironmentSmall firms interact most often and most closely withtheir immediate business environment: their customersand suppliers, and to a lesser extent their competitors.Research has shown that customers and suppliers, thesupply chain, are always the most extensive andimportant external contacts that a firm has (Moore,1996; Woolgar et al., 1998). Customer feedback is aconstant source of external information, and thereforepotentially usable knowledge. Suppliers can be a majorsource of component and production knowledge.Competitors, through their behavior, can be a furthersignificant source of knowledge.

IntermediariesBeyond the immediate business environment, a smallfirm will have communications with trade associations,colleges and schools, TECs (training and enterprisecouncils) and consultants, will read the trade press andmay attend exhibitions. Other organizations within thebusiness support community can be added: Chambersof Commerce, innovation and technology centres,small business agencies such as the U.K.’s BusinessLinks and USA’s Small Business Development Cen-ters, professional institutes and research associations.Here, all of these organizations are grouped under thecollective term intermediaries. Often a firm’s interme-diary contacts will be very cursory, where they canprovide only a minor source of external knowledge. As

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Edward Major and Martyn Cordey-Hayes Part IX

will be seen intermediaries have a significant role toplay in conveying externally generated knowledge.

Universities and Outside Research

In Woolgar et al.’s (1998) framework, universities fallwell outside of a small firm’s focus of attention.Universities are repositories of knowledge and exper-tise, but partly due to the problems considered above,many small firms have no significant contact withthem. Evidencing the differences between small firms,Woolgar et al. (1998) found that those that did havewell-developed links with universities had a greaterappreciation of external knowledge sources than hadthose lacking such links.

Governments; Policy Initiatives and InnovationSchemes

Governments, like universities, generally fall beyond asmall firm’s normal sphere of attention. Governmentstry to influence small firms through business services,innovation schemes and policy initiatives. The U.K.Government’s Small Business Service (SBS) is organ-ized: to help small businesses realize their potential, toenhance small business performance through worldclass business support, to promote small businessenterprise across society and to provide high standard,value for money service (Department of Trade andIndustry, 2001). In the USA, the Small BusinessAdministration (SBA) provides financial, technical andmanagement assistance to help Americans start, runand grow their small businesses.

As an example of a national policy initiative, recentyears have seen many governments running nationalForesight programs. These are structured programmestaking a forward look to identify and prepare foremerging trends in markets and technologies in themedium to long term. Foresight activities aim to:

(a) identify events and seek opinions in order toprioritize future events;

(b) contribute to the development of a well-informedsupport environment for resource allocation andfunding prioritization; and

(c) promote cooperation between actors from differentfields so as to incorporate a variety of viewpoints(Cabello et al., 1996).

The U.K. Government’s national Foresight programwas initiated in 1993 to identify emerging techno-logical trends and market opportunities. When appliedto individual firms, Foresight is about generating andgrowing the capabilities to envision and look forward,enhancing the firms’ future innovative potential. Gov-ernment schemes and initiatives have a poorimplementation record. For example despite substantialcommunicative effort, the U.K. Foresight program hashad minimal effect on U.K. firms (Major & Cordey-Hayes, 2000a). The small firms’ inherent problemsdiscussed above compound the difficulties of distance.Government initiatives and innovation schemes lieoutside of the firms’ normal sphere of attention inmuch the same way as the government itself. Manysmall firms are simply not aware of support servicesand initiatives.

Figure 1. Woolgar et al.’s SME-centric universe.

Source: Woolgar et al. (1998), page 578.

Encouraging Innovation in Small Firms Through Externally Generated KnowledgeChapter 1

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External NetworkingThe final external knowledge source is a method ratherthan a location. An inquisitive manager will search theexternal knowledge sources, be they in the immediatebusiness environment, intermediaries, universities orgovernment schemes and initiatives. Through thisexternal networking he or she will be better positionedto access new externally generated knowledge.

Conveying External KnowledgeHadjimanolis (2003) notes that information and knowl-edge must be transferred throughout a firm, for the firmto learn from it. But externally generated knowledgelacks the advantage of close proximity to the firm’sdecision-makers. Unlike internally generated knowl-edge it must first be brought into the firm’s internalsphere before it can be used. The growing subject ofknowledge transfer informs our understanding of howexternally generated knowledge is conveyed to thesmall firm.

The Concept of Knowledge TransferThe concept of knowledge transfer derives from thefield of innovation (Gilbert & Cordey-Hayes, 1996).Knowledge transfer is the conveyance of knowledgefrom one place, person, ownership, etc. to another(Major & Cordey-Hayes, 2000b).

Any transfer must involve more than one party.There has to be a source (the original holder of theknowledge) and a destination (where the knowledge istransferred to). When used to describe movement ofknowledge, the term transfer is perhaps inappropriate.As defined, it implies that for the transferred item to begained by (conveyed to) the destination it must be lostby (conveyed from) the source. But as an intangibleasset, knowledge does not necessarily have to be givenup by one party to be gained by the other. Theframework developed below proposes alternative ter-minology that more accurately describes the process.

Models of Knowledge TransferResearch into knowledge transfer has focused onseparating the overall transfer from source to destina-tion into comprehensible sub-processes. Trott, Seaton& Cordey-Hayes (1996) develop an interactive modelof technology transfer describing four stages in theknowledge transfer process: for inward technologytransfer (knowledge transfer) to be successful, anorganization must be able to:

(1) search and scan for information which is new tothe organization (awareness);

(2) recognize the potential benefit of this informationby associating it with internal organizational needsand capabilities (association);

(3) communicate these to and assimilate them withinthe organization (assimilation); and

(4) apply them for competitive advantage (applica-tion)

Non-routine scanning, prior knowledge, internal com-munication and internal knowledge accumulation arekey activities affecting an organization’s knowledgeacquisition ability (Trott et al., 1996). These findingscorrespond with those underlying Cohen & Levinthal’s(1990) absorptive capacity, the ability of an organiza-tion to recognize the value of new information, toassimilate it and to commercially apply it.

In addition to the works of Trott et al. (1996) andCohen & Levinthal (1990), the knowledge transferliterature includes frameworks and models by Cooley(1987), Slaughter (1995) and Horton (1997, 1999).Two streams of models can be distinguished: Nodemodels describe nodes, discrete steps that are eachgone through. Process models describe knowledgetransfer by separate processes that are each undertaken.Node models are presented by Cooley (1987), Slaugh-ter (1995) and Horton (1997). Cooley discussesinformation systems in the context of the informationsociety:

Most of such systems I encounter could be betterdescribed as data systems. It is true that datasuitably organized and acted upon may becomeinformation. Information absorbed, understood andapplied by people may become knowledge. Knowl-edge frequently applied in a domain may becomewisdom, and wisdom the basis for positive action(Cooley, 1987, p. 11).

Cooley thus raises a progressive sequence of fivenodes. He conceptualizes his model as a noise-to-signal ratio. The signal being transmitted (i.e.conveyed) is subject to noise, but as the informationsystem moves from data towards wisdom, noise isreduced and the signal increases. Knowledge acquiringorganizations will find low-noise wisdom more usefulthan high-noise data.

Slaughter (1995) presents a four-node hierarchy ofknowledge, which he uses to describe a wise culture.He uses the same first four terms as Cooley (1987):

(1) Data; raw factual material;(2) Information; categorized data, useful and other-

wise;(3) Knowledge; information with human significance;(4) Wisdom; higher-order meanings and purposes.

Slaughter’s descriptions indicate how stages further upthe hierarchy are subject to less noise, and are moreuseful to knowledge acquiring organizations. Data,information and knowledge are stages on the path towisdom.

While Cooley (1987) and Slaughter (1995) set theirmodels in a macro-context, Horton (1997) focuses onindustry on a micro-scale. In the specific context of theprinting industry, but with wider applicability, shedescribes an information value progression. Thisprogression again starts with data and finishes withwisdom, with information and knowledge as inter-

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mediate steps. Cooley (1987) describes data asobjective, calculative and subject to noise. Horton(1997) agrees; data is hard, objective and low order, aswell as being low value, voluminous and contained. Atthe other end, wisdom is subjective, judgmental andless subject to noise (Cooley, 1987). By Horton (1997)it is subjective, soft and high order, as well as highvalue, low volume and contextualized.

Similarities are seen in all three models described,despite their contextual differences. Horton (1997)however adds another intermediate step. This is thenode of understanding, between knowledge and wis-dom. Understanding:

is the result of realising the significance of relation-ships between one set of knowledge and another . . .It is possible to view the ability to benefit fromunderstanding as being wisdom (Horton, 1997,p. 3).

Understanding thus results from knowledge but pre-cedes wisdom. Table 1 compares the three nodemodels. With wisdom a result of understanding and thebasis for positive action, the models are mutuallysupportive. A six-node scheme of knowledge transfer isformed.

Process models are presented by Cohen & Levinthal(1990), Trott et al. (1996) and Horton (1999). Trott etal.’s (1996) four processes of inward technologytransfer, awareness, association, assimilation and appli-cation, are described above. Horton (1999) presents athree-phase process model of successful foresight.Phase one is a collection, collation and summarizationphase. Information is collected from a wide range ofsources, collated to give it structure, and summarizedinto a manageable form. Knowledge generated fromstage one needs translation and interpretation, theelements of phase two. Translation puts the informa-tion in an understandable language. Interpretation isorganization specific. It is about determining themeanings and implications of the translated informa-tion. Translation and interpretation is the most crucialstep in the process, where most of the value is added.But it is also poorly understood, having few theoreticaltechniques. Phase three comprises assimilation andcommitment. Understanding generated in phase twoneeds to be assimilated by decision-makers. If changesare to result commitment to act is needed. It is only atthis point that the value of the whole three-phaseprocess can be realized and judged.

Trott et al. (1996) and Horton (1999) both refer to anassimilation process; the means by which ideas arecommunicated within the organization (Trott et al.,1996), or by which understanding is embedded in theorganization’s decision-makers (Horton, 1999). Assim-ilation then is a process of internal communication.Horton’s assimilation process is in the same phase ascommitment. By separating these out, Horton’s com-mitment becomes correlated with Trott et al.’ssubsequent process of application. Commitment toaction is equivalent to the process of application forcompetitive advantage. By similarly separatingHorton’s phase one, collection becomes analogous toTrott et al.’s first process of awareness. The collectionof data is a central means for an organization tobecome aware of new opportunities. Similarly, Hor-ton’s collation and summarization can together becompared to Trott et al.’s association. Collection andsummarization of information is equivalent to theprocess of associating the value of the information tothe organization. This leaves Horton’s second phase,translation and interpretation, with no direct analogy.The comparison suggests analogy with a process notexplicitly stated by Trott et al., the process ofacquisition. This may be described as the process bywhich an organization draws knowledge into itself,which it can then assimilate. Viewed from within theorganization this is a process of acquisition into itself.Viewed from outside it is a process of translation ofknowledge from one place to another. It should benoted that the term acquisition as used here refers to adiscrete part of the overall process of drawing knowl-edge into the knowledge acquiring organizations. Theterm knowledge similarly has an overall meaning (as inthe phrase knowledge transfer) as well as referring to adiscrete node in the overall knowledge transfer proc-ess.

Trott et al. (1996) and Horton (1999) can now berelated to Cohen & Levinthal (1990). Cohen andLevinthal describe absorptive capacity as:

the ability of a firm to recognize the value of new,external information, assimilate it, and apply it forcommercial ends (Cohen & Levinthal, 1990,p. 128).

These recognition, assimilation and application stepscorrespond to Horton’s (1999) collation, assimilationand commitment. Again there is no explicit provisionfor the translation and interpretation process. Neither is

Table 1. Node schemes of knowledge transfer.

Model node 1 node 2 node 3 node 4 node 5 node 6

Cooley data information knowledge N/A wisdom actionSlaughter data information knowledge N/A wisdom N/AHorton data information knowledge understanding wisdom N/A


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there an initial collection process. However the posi-tions of these can be filled without upsetting the basicmodel. Horton (1999) presents the only model toclearly distinguish the translation and interpretationphase, supporting her contention that this phase ispoorly understood. Table 2 compares the three processmodels. A five-process scheme of knowledge transferis formed.

Knowledge Translation

A Framework for Knowledge Translation

From showing correspondences between modelswithin the two streams, it becomes possible to build anintegrated framework, a framework for knowledgetranslation, combining both nodes and processes. Theterm knowledge translation brings a subtly differentperspective than the term knowledge transfer. The wordtranslation has a dual meaning. It can refer tomovement from one place to another place, much thesame as the word transfer. It can also refer to puttingsomething into an understandable form, as in thesecond phase of Horton’s (1999) process model. Bothmeanings are appropriate to the context here: Knowl-

edge translation is both the movement (or transfer) ofknowledge from one place to another, and the alteringof that knowledge into an understandable form.

Horton’s work (1997, 1999) provides the key tointegrating the knowledge transfer models. Her guideto successful foresight (process model; Horton, 1999)makes use of her information value progression (nodemodel; Horton, 1997):

Each phase (in the process model) creates greatervalue than the previous one as the outputs move upthe information value chain from informationthrough knowledge to understanding (Horton, 1999,p. 5).

Thus Horton’s four nodes are connected by her threephases. Looking simultaneously at the expanded nodeand process schemes in Tables 1 and 2, the fiveprocesses fall neatly between the six nodes. Figure 2illustrates this, combining the contents of Tables 1 and2 into a single combined framework for knowledgetranslation. The initial node is raw data. The firstprocess is to collect this into information. The secondprocess is collation and summarization of this informa-tion into knowledge. Knowledge is the end point of

Table 2. Process schemes of knowledge transfer.

Model process 1 process 2 process 3 process 4 process 5

Horton collection collationsummarization

translationinterpretation

assimilation commitment

Trott et al awareness association N/A assimilation applicationCohen and Levinthal N/A recognition N/A assimilation application

Figure 2. Combined knowledge translation framework.

Source: Expanded from Horton (1997, 1999).

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Horton’s (1999) phase one. The third process exactlyfollows Horton’s (1999) phase two; the translation andinterpretation of knowledge into understanding. Under-standing is then assimilated within the organizationinto wisdom, from which a commitment to positiveaction can follow. The processes in this integratedframework are the ways of reaching the successivenodes.

Figure 2 presents a complete system of knowledgetranslation. This completeness may be misrepresenta-tive of extant knowledge transfer or translationschemes. For example, the U.K. national Foresightprogram has been incomplete in its intent to encouragea forward thinking culture within industry (Major &Cordey-Hayes, 2000a). It is possible that not everyprocess in the scheme will always be fully conducted,thus restricting the overall flow of new ideas that canlead to innovation. The latter processes of assimilationand commitment take place mainly within the destina-tion organization (Cooley, 1987). Early stages might becarried out by an outside body, perhaps the source ofthe data or information. Collection of data andcollation of information can be performed on a macro-scale by government innovation schemes and policyinitiatives (as was done by the U.K. Foresight pro-gram). This leaves the middle process less clear, andpossibly incomplete. This is the notion of the knowl-edge translation gap. For a complete and successfulknowledge translation process this gap must bebridged. This implies a role for parties other than thesource and destination organizations; i.e. for someoutside intermediary organization. Attention is thusturned to the intermediaries, introduced as an externalknowledge source in Section 4, above. The knowledgetranslation gap conceptualizes the barriers to external

learning identified and discussed by Hadjimanolis(2003).

Characteristics of Knowledge and the KnowledgeTranslation FrameworkBefore turning in earnest to the role of the inter-mediaries, some consideration of the characteristics ofknowledge is needed to complete the groundwork.Major & Cordey-Hayes (2000a, 2000b) show howknowledge can be characterized along two perception-dependant dimensions. Whether internally orexternally generated, firms want knowledge in adiscrete and concrete form. To make decisions theywant something tangible that can give them operationalknowledge. Distant external sources such as uni-versities and governments, as well as governmentinnovation schemes and policy initiatives, are per-ceived to be intangible and strategic. Distinctions arebeing made according to the tangibility and thetemporal nature of knowledge. Along one dimension,knowledge can be either concrete (i.e. tangible) orabstract (i.e. intangible, or tacit). A product can becharacterized as exhibiting tangible knowledge, while aprocess exhibits intangible, or tacit, knowledge. Alongthe second dimension, knowledge can be eitherstrategic (i.e. long-term, for overall direction setting) oroperational (i.e. short-term, decision-making knowl-edge, for route finding within the overall direction).

These characteristics of knowledge can be illustratedby a two dimensional framework. Figure 3 shows theabstract/concrete distinction along a vertical axis andthe strategic/operational distinction along a horizontalaxis. Four regions are defined within this framework.The firm’s decision-makers want knowledge in RegionA; concrete information to help them make decisions.

Figure 3. Characteristics of knowledge.


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They want a tangible product giving them operationalknowledge. External knowledge sources such as uni-versities and government innovation schemes areperceived to be in Region C; their distance makes themintangible; their subject matter outside of the firm’soperational needs and therefore strategic. Where spe-cific outputs are produced, such as the reports of theU.K. Foresight program, a scheme or initiative mightbe regarded as being in Region B; it is still long-term,but has a perceived product in the form of reports thatcan be read. Region D represents an area of tacitknowledge, unwritten rules, processes and proceduresthat contribute to a firm’s short-term decision-makingculture.

The two-dimensional framework in Fig. 3 givesanother context in which to view the framework ofknowledge translation developed above. Researchshows that firms view internally generated knowledgein concrete and operational terms (Major & Cordey-Hayes, 2000a, 2000b). They want discrete and tangibleknowledge to enable them to take immediate actions.In the terminology of the knowledge translationframework they are using their internally generatedknowledge as wisdom, from which they take actions.Where they seek externally generated knowledge, mostfirms do so to add to this stock of short-term, tangibleknowledge. Certain firms take a much longer-termview. Forward-looking, future orientated firms have afuller perspective of knowledge acquisition. Their

internal processes (the term processes itself signifyingtheir greater appreciation of the abstract dimension) foracquiring external knowledge show them to be reach-ing much farther back through the nodes and processesof the knowledge translation framework. They seekknowledge that they can translate into understandingwithin the company, which gives an informed base forthe wisdom underlying their actions. Even farther backthey may seek the basic uncollated information thatunderlies knowledge.

Action is concrete, and, almost by definition, short-term. Wisdom, in the sense of firms’ requirements, isalso concrete and operational (though less short-termthan action). Action and wisdom, the final two nodes ofthe knowledge translation framework fall withinRegion A in Fig. 3. It follows that the remaining nodesand processes of the knowledge translation frameworkcan be located on the two-dimensional characterizationof knowledge. The nodes and processes from thecombined knowledge translation framework (Fig. 2)can be superimposed onto the dimensions of knowl-edge (Fig. 3). Figure 4 shows the resulting integratedconceptual framework for knowledge translation.

Action and wisdom are concrete and operational. Atthe other end, data is also concrete. It is a tangibleproduct (hard and objective according to Cooley (1987)and Horton (1997)). However, because it lacks codifi-cation or an operational context it is strategic. As datais collected and collated into information and knowl-

Figure 4. Integrated conceptual framework for knowledge translation.

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edge, its immediate tangibility is reduced. Informationis softer, less tangible, than data. Knowledge is lesstangible still. While information may be talked about inproduct terms, knowledge is regarded more as anabstract process. The translation and interpretationphase brings the system into the operational frame ofan individual firm (as the knowledge receiver), reach-ing a level of internal understanding. Abstractunderstanding, given a context through assimilationbecomes tangible wisdom, the basis for commitment toaction. The sequence of elements in the combinedknowledge translation framework moves through thequadrants defined by the two dimensions of knowledgein the order BCDA. Concrete and strategic data mustbe taken and transformed through abstract elementsbefore they can emerge as concrete and operationalwisdom.

The sequence can be summarized into movementsbetween the quadrants. Moving from quadrant B toquadrant C (strategic-concrete to strategic-abstract) is aprocess of codification. Combining the collection andcollation processes, this extracts abstract meaningsfrom the data. Quadrant C to quadrant D (strategic-abstract to operational-abstract) remains the translationprocess. This process brings externally generatedknowledge into the organization’s sphere, generatinginternal understanding. Finally, a contextualizationprocess, moving from quadrant D to quadrant A(operational-abstract to operational-concrete), extractswisdom and promotes action. This is the result ofputting the understanding into the organization-specificcontext. These three summary phases equate to Hor-

ton’s (1999) three phases of successful foresight. Theyare shown in Fig. 5.

The Role of Intermediaries in KnowledgeTranslation

Bridging the Knowledge Translation GapThe previous section introduced the knowledge transla-tion gap to describe how the overall knowledgetranslation process can be incomplete. In terms of Fig.5, the codification of externally generated knowledgecan be carried out by an outside body, perhaps theknowledge source itself (e.g. government as the sourceof a national innovation scheme). Contextualizationoccurs within the knowledge destination (the smallfirm in the present context). As suggested certain(forward-looking, future orientated) firms reach backthrough the knowledge translation framework to carryout the translation phase (and possibly also thecodification phase) themselves. Most firms though facethe possibility of a gap in the translation phase, andtherefore a barrier in their access to externallygenerated knowledge. Some other body is needed tobridge the gap between the firm and the knowledgesource. An organization fulfilling an intermediary rolecan translate and interpret external knowledge into aform in which receiving organizations can then con-textualize it.

Intermediaries were introduced in Section 3. Inbroad terms, an intermediary organization is any bodycoming between the knowledge source and the destina-tion (fulfilling the role of an agent in principle-agent

Figure 5. Codification, translation and contextualization phases.


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theory). In these terms, organizations within thebusiness support community are intermediaries. Busi-ness support agencies such as Chambers of Commerceand small business support organizations might there-fore be able to fulfil this role. Industry based bodies;trade and research associations and professional insti-tutes, are potentially well positioned to supply anintermediary service to their industries. Even uni-versities, not previously considered as intermediaries,might bridge the gap through their specialist knowl-edge. Intermediaries provide a ready, extant system forconnecting knowledge sources to knowledge receiv-ers.

Categorizing IntermediariesThe roles of intermediaries are poorly understood.Previous studies expressed the somewhat muddled andamorphous perception of the business support commu-nity (Center for Exploitation of Science andTechnology, 1997; Woolgar et al., 1998). Major &Cordey-Hayes (2000a) distinguish three categories ofintermediary, according to the prime function theyprovide for their industrial clients. First are sign-posters. These are first-point-of-contact organizations.They point to sources of advice, guidance andexpertise, rather than answer problems themselves. Inthe U.K. Training and Enterprise Councils (TECs),Business Links and Regional Technology Centers(RTCs) all signpost firms to a body appropriate to thequery. Small Business Development Centers (SBDCs)and the Office of Technology perform a similarfunction in the U.S. Second are facilitators. These giveadvice and guidance to client firms to help them to helpthemselves. Trade associations’ central informationprovision role is a facilitating function. Some industrialresearch associations (RAs) have a membership bodyof industry clients much as trade associations. Thesemembership-based RAs (MRAs) play a similar infor-mation provision, facilitating role. Chambers ofCommerce facilitate regionally, by encouraging net-working between firms in a common locality. Third arecontractors. These are specialist sources of expertiseoffering direct help on specific issues. Universityresearch, non-membership RAs (NMRAs) and pro-fessional institutes (through personal memberships) areprime sources of such expertise. When viewed as acontinuum, patterns emerge within these three interme-diary functions. On moving from signposters, throughfacilitators to contractors there are longer-term inter-mediary-client relationships, increasing involvementwith client firms’ operations, an increasing large firmfocus and a corresponding increasing difficulty ofsmall firm involvement.

Intermediaries then have a central role in bridgingthe knowledge translation gap and thus in the convey-ance to small firms of externally generated knowledge.The next section shows how this bridging role can beexploited to reach the small firms.

Intermediaries and Conveyance to Small Firms ofExternally Generated Knowledge

Categorizing Small FirmsMajor & Cordey-Hayes’s (2000a) research leads to acategorization of small firms. They propose that smallfirms be ordered according to the futures orientation oftheir culture and attitude. Firms with a strong futuresorientation have the ability to reach back to thetranslation and codification processes of the knowledgetranslation framework (Fig. 5) to acquire externallygenerated knowledge, which can then contribute totheir innovative potential. Four themes describing theboundary activities and network orientation of asample of U.K. small firms were studied (Major, Asch& Cordey-Hayes, 2001; Major & Cordey-Hayes,2000a) to identify the attributes that contribute to smallfirms’ futures orientation. The key theme was aware-ness and perception of the U.K. Foresight program andthe concepts it is promoting, and the use of suchconcepts as a regular part of their business. Highawareness, accurate perception and regular use suggesta greater appreciation of long-term issues (i.e. a strongfutures orientation). Such firms are able to reach out toobtain the type of knowledge that Foresight provides.Remaining themes were: (1) firms’ relationships withintermediaries; (2) the importance of external network-ing between firm personnel and people in outsideorganizations; and (3) firms’ reliance on their supplychains as a knowledge source.

Three distinct groups of firms emerged, 10% of thesmall firms sampled had a strong futures orientation.These are strategic firms, with a highly involvedapproach to their future. They are characterized by highand ongoing awareness and accurate perception ofForesight, and regular use of Foresight concepts pre-dating their knowledge of the U.K. Foresight program.Their futures orientation puts them ahead of what theForesight program can provide, and some have beengiving the program the benefit of their own experience.These firms have many contacts with intermediaries,but only a select few, typically with trade and researchassociations and universities, are deep and ongoing.These few intermediaries are important sources ofexternal knowledge. Outwardly focused personal atti-tudes underpin these firms’ foresight knowledge andfutures orientation. Correspondingly, external inter-personal networking was found to be highly impor-tant. However, reliance on supply chains was notimportant. Customers and suppliers (the supply chain)are always a firm’s most extensive contacts (Moore,1996; Woolgar et al., 1998), but for the involved,strategic firms they are not a significant source ofexternally generated knowledge. 70% of the smallfirms sampled had a weak futures orientation. Theirattitude is reactive or uninvolved. These firms havelittle or no awareness of Foresight or its concepts. Theirintermediary contacts are all at a level where they can

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have little impact as external knowledge sources.External networking has a low importance, driven outby concentration on the present. Completing thecontrast with the involved firms, supply chains are theirmost important external knowledge source. But reli-ance on these tangible, short-term operationalmanagement relationships tends to drive out use oflonger-term external knowledge sources. Between thetwo extremes, 20% of the small firms sampled weredistinguished with intermediate characteristics. Thesefirms know about Foresight and are aware of theimportance of their future, but lack prior involvementwith Foresight concepts. Their intermediary contactsare more selective and more useful external knowledgesources than in uninvolved firms, but lack the depth andselectivity of involved firms. The importance ofexternal networking and reliance on the supply chainalso consistently rate between involved and uninvolvedfirms. These are open, or responsive firms. They areopen to the future, but need a stimulus to generateresponse and action. Compared to the involved firmsopen firms know what is needed, but only the involvedfirms can actually do it.

Targeting Small Firms—Small Firm-IntermediaryInteractionsDissemination of the U.K. Foresight program took noaccount of the differences between small firms.Communicating Foresight to involved firms, whoalready have a futures-orientated culture, is likepreaching to the converted. For uninvolved firmsfutures-orientated policy initiatives are so far outside oftheir normal sphere of attention as to be essentiallyunreachable. The present, they perceive, requires theirfull attention. Communicating with these firms wouldrequire great expenditure of effort simply to be heard,let alone be listened to. It is proposed that the mosteffective audience for futures oriented initiatives likeForesight is the open firms. Openness means that theywill listen, lack of prior involvement means thatsignificant impacts may result. Combining manage-ment or organizational changes with a well-targetedinnovation scheme or policy initiative could be thestimulus an open firm needs to become an involvedfirm.

Major & Cordey-Hayes’s (2000a, 2000b) researchshows that only a small portion of an innovationscheme or policy initiative’s potential audience willrespond so as to fulfil its aims. The most importantoutcome of the research in terms of conveyance ofgovernment schemes and initiatives was a modeldeveloped to show how intermediary organizations canprovide the method to target this priority audience.Intermediaries interact with small firms as part of theirregular operation. The research revealed strong pat-terns in how small firms use intermediaries to build uptheir internal knowledge and to enhance their aware-ness and acquisition of externally generated knowledge

(Major & Cordey-Hayes, 2000a). Involved firms werefound to get this support from contractor organiza-tions, open firms from facilitators, and uninvolvedfirms (at a minimal level) from signposters.

Figure 6 illustrates these relationships between smallfirms and intermediaries. The vertical axis ranks smallfirms by their futures orientation. Involved firms, witha strong futures orientation rank higher than open anduninvolved firms with moderate and weak futuresorientation respectively. The horizontal axis ranksthe intermediary organizations by the signposter-facilitator-contractor categorization. In describing thesmall firms-intermediaries relationships, the modelshows that a move up the small firms’ futuresorientation scale is accompanied by a move along theintermediaries functions scale. Involved firms use theirextensive contacts at universities, and to a lesser extentNMRAs and professional institutes, for their specificresearch expertise. Knowing where to look, they do notneed to be signposted. Already having access toimportant external knowledge sources, they do notneed a facilitator. Open firms are open to the desire forfutures involvement but lack the facility. Links withtrade associations and MRAs and links with chambersof commerce give them access to externally generatedknowledge on industry and regional bases respectively.With this access, these firms too are in no need ofsignposting. Uninvolved firms use trade associationsand chambers of commerce for their present-orientatedrepresentational roles, but have little desire to accessexternal knowledge that can lead to innovation.Lacking the intermediation of the facilitators theyrequire the basic guidance of signposters, BusinessLinks and RTCs in the U.K., SBDCs and the SBAOffice of Technology in the U.S., to point them towardsexternal knowledge sources.

Implications and Recommendations

Implications for Innovation Schemes and PolicyInitiativesFigure 6 gives the basis for engaging intermediaries totarget small firms. Though generated primarily fromstudy of the Foresight program, the mechanism that themodel describes is generic; it describes relationships,rather than a single policy initiative. Foresight issimply a package of knowledge, the conveyance, ortranslation of which the model is guiding. It plays nopart in the conveyance, or translation mechanism itself.The mechanism can therefore be applied to translationof other schemes and initiatives of a similar nature tothe Foresight program.

Engaging intermediaries brings schemes and initia-tives closer to the small firms, overcoming some oftheir inherent resource problems. Increasing the dis-tance between policy-makers and firms, reduces theeffects of firms’ mistrust of government and govern-ment’s limited understanding of the firms’ situations.


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Compared with policy-makers, intermediaries holdgreater understanding of, and are more trusted by,small firms, and have more dedicated resources to dealwith them. In principle, all that is required is for thepolicy-maker to select the organizations that dealappropriately with the specified target audience. Theprevious section suggested that for Foresight, thepriority target audience should be open firms. Butwhichever target group a policy initiative deems thepriority, the generic nature of the model indicateswhich intermediaries to engage. The U.K. Foresightprogram has started to recognize the value of inter-mediaries. The Foresight associate program engagingintermediaries to bring the benefits of Foresight to theirspheres of influence, is a welcome development, butthe predominance of professional institutes among theassociates (Office of Science and Technology, 2000)does not fully reflect the recommended targeting ofopen, responsive firms.

Implications for Small Firms

The theory and models developed in this chaptershould encourage small firms to strengthen their linkswith intermediaries. Stronger links could increase thesmall firms’ exposure to externally generated knowl-edge, be it from the intermediary itself or through

greater access to innovation schemes and policyinitiatives, with consequent benefits to the firms’innovative potential.

The previous section linked small firms’ futuresorientation (with its effect on future innovation) to thetype of intermediary they have dealings with. Involvedfirms’ futures-orientation accompanies their deep linkswith contractors. Encouraging open firms to deepentheir contractor links may stimulate them towards aninvolved attitude. Similarly, encouraging greater use offacilitators in uninvolved firms could stimulate a moreopen attitude.

This chapter has made recommendations for theconveyance of externally generated knowledge to smallfirms. The processes within the firms whereby thatknowledge is manipulated to generate innovation andcompetitive advantage fall beyond the scope of thepresent chapter. This theme is taken up in the literatureon organizational learning (see Gilbert & Cordey-Hayes, 1996; Vickers & Cordey-Hayes, 1999).

Implications for Intermediaries

Intermediaries have been shown to have a central rolein the conveyance to small firms of externally gen-erated knowledge. They should thus be encouraged toperform this role, working with government innovation

Figure 6. Relationships between small firms and intermediaries.

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schemes and other knowledge sources, bridging theknowledge translation gap to increase the innovativepotential of their small firm contacts.

ConclusionsThis chapter has considered the conveyance into smallfirms of externally generated knowledge. It started bypresenting the barriers to innovation in small firms andthe sources of internally and externally generatedknowledge potentially available. From the concept ofknowledge transfer a framework of knowledge transla-tion has been developed. The derived notion of theknowledge translation gap explicitly illustrates why somany small firms fail to reach external knowledgesources. The chapter has proposed that intermediariescan be the bridge that is needed to cover the knowledgetranslation gap. Understanding how intermediaries canbe used to convey externally generated knowledge tosmall firms brings implications and recommendationsfor government innovation schemes and policy initia-tives, for intermediaries and for the small firms, all ofwhich will encourage and enhance successful smallfirm innovation.

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Linking Knowledge, Networking andInnovation Processes: A Conceptual Model

Jacqueline Swan, Harry Scarbrough and Maxine Robertson

IKON (Innovation, Knowledge and Organizational Networks) Research Centre, WarwickBusiness School, University of Warwick, U.K.

Abstract: Innovation is frequently cited as a major reason for the emergence of networkstructures. However, although network structures have been studied extensively, relatively littleresearch has examined the diverse roles played by networking processes in innovation. Thischapter develops a conceptual model that relates specific kinds of networking, to particularepisodes of innovation (invention, diffusion, implementation) and to processes of knowledgetransformation. The operation of the model is illustrated through three case examples, eachfocusing on a different innovation episode. These are used to illustrate interactions amongprocesses of networking, innovation and knowledge transformation.

Keywords: Innovation; Knowledge; Networking; Networks; Invention; Diffusion; Implementa-tion; Knowledge transformation.

IntroductionInnovation may be defined as: “the development andimplementation of new ideas by people who over timeengage in transactions with others in an institutionalcontext” (Van de Ven, 1986, p. 591). Networking—as asocial communication process—is thus recognized asplaying a central role in innovation. Freeman (1991),for example, observes that many studies since the1950s have noted ‘the importance of both formal andinformal networks, even if the expression network wasless frequently used’ (p. 500). Encouraging innovationis frequently cited as the major reason for developingnetwork forms of organization. Despite this observa-tion, relatively little research has focused explicitly onthe links between networks and innovation (Oliver &Ebers, 1998). Moreover, research that does addresslinks between networks and innovation tends to look atthe impact of network structures—social processes ofnetworking receive relatively little attention (Pettigrew& Fenton, 2000). This chapter looks, therefore, at theroles and implications of networks for innovationfocusing, in particular, on processes of networking.

The chapter begins by using existing theory andresearch to develop a conceptual model linking net-working with innovation processes. Innovation ispresented here as an episodic process, encompassing

the design and development (invention), spread (diffu-sion) and implementation of ideas that are new to theadopting unit (Clark, 1987; Van de Ven, 1986).Recognising the limits of stage models of innovation,these episodes are seen as iterative, recursive andultimately conflated, not as linear and sequential (Clark& Staunton, 1989; Ettlie, 1980). For example, pivotalmodifications built into the design of the innovationduring implementation may feed back into its diffusion(Fleck, 1994). Previous studies have tended to separatethese different aspects of innovation and to focus ondiscrete episodes (i.e. either invention or diffusion orimplementation—Wolfe, 1994). However, this chapterattempts to outline a more holistic model of the linksbetween networking and innovation by tracking theroles of networks and social networking activitiesacross the entire innovation process, and by noting howthese roles may vary across different episodes.

This model is developed and illustrated by contrast-ing the role of inter-organizational networking (thecase of a professional association) in diffusing techno-logical innovation with that of intra-organizationalnetworking (the case of a consultancy firm) ininventing scientific innovation for clients. It thenillustrates how these different kinds of networkscoalesce when implementing operations management

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technology (the case of a manufacturing firm). The aimof the chapter is to use these examples to explore theroles played by different kinds of network and socialnetworking activities at different points across thewhole innovation process.

This focus on the links between networking andinnovation processes highlights an area, which iscomparatively under-researched and under-theorized inthe literature (exceptions being the work of Alter &Hage, 1993; Oliver & Leibeskind, 1998; Rogers,1983). Although explanations of the emergence ofnetwork structures frequently cite, as reasons for thesestructures, the industrial change and market turbulenceassociated with product and process innovations,relatively few studies go on to address the performativerole of networks in developing or promoting suchinnovations (Oliver & Ebers, 1998). In contrast, ourstudy, not only highlights that role, but also suggeststhat innovation is closely, reciprocally and system-atically intertwined with the creation and maintenanceof networks (see also Gibson & Conceicao, 2003;Major & Cordey-Hayes, 2003). The argument for thisview is based, firstly, on the development of atheoretical model which draws on research in this area,and, secondly, on the empirical study of networkeffects in widely differing innovation processes. Inconcise terms, the development of the argument isbased on two key propositions. Namely, that aprocessual view of networks is as, or more, relevant toinnovation studies than the conventional structuralview (Pettigrew & Fenton, 2000; Wolfe, 1994) andsecond, that innovation is better characterized not asthe production of physical artefacts but as flows andcombinations of knowledge and information (Major &Cordey-Hayes, 2003; Nonaka et al., 2003; Tidd, 1997).These propositions are elaborated further below.

Network Structures and Networking ProcessesNetworks have been analyzed in a variety of ways andthrough different theoretical lenses (Alter & Hage,1993; Ebers & Jarrillo, 1997; Grandori & Soda, 1995;Oliver & Ebers, 1998). However, in many discussionsa distinction is made between structural characteristicsor forms of networks and the processes involved indeveloping and sustaining networking relations (e.g.Alter & Hage, 1993). Existing work (cf. Ahuja, 2000;Powell et al., 1996) linking networks with innovationtends to focus on the former. Networks are viewedprincipally in functional terms as structured channelsthrough which information is communicated andknowledge is transferred. Hansen (1999), for example,develops a contingency model linking network struc-tures (in terms of the strength of network ties) to formsof knowledge transfer (in terms of relatively complex/tacit or simple/explicit forms). From a detailedempirical study of product innovation in a largeelectronics company he concludes that networks char-acterized by strong ties are most effective for the

transfer of tacit knowledge and weak ties for transfer ofexplicit knowledge.

Structural perspectives tend to see networks as anintermediate organizational form which lies some-where between markets and hierarchies. A distinctionis also made between inter and intra-organizationalnetworks with much of the networking literaturefocusing on the latter (e.g. Alter & Hage, 1993; Ebers,1997; Grandori & Soda, 1995; Jarrilo, 1993). Grandori& Soda (1995, p. 184), for example, define networks as‘modes of organizing economic activities throughinter-firm co-ordination’. Such modes of organizing—i.e. network structures—are, it is argued, important forinnovation because they allow for more open and/orextensive exchange and transfer of knowledge andinformation across firms.

This emphasis on the relatively formal and persistentrelationships between firms usefully highlights thestructural implications of networks in innovation.However, it tends to downplay social processes ofnetworking, including the creative role of actors andagents and the importance of interpersonal and infor-mal relationships in developing, creating andsustaining networks (Ebers, 1997). The emphasis onnetworks as structures for knowledge and informationexchange, then, tends to overshadow the social proc-esses through which these structures emerge anddevelop and the intentions of the actors involved. Italso tends to downplay the performative role ofnetworks in actually creating, defining and shaping theknowledge and information that is exchanged.

In contrast, our chapter builds from this earlier workbut aims to develop a framework that will address therole of networking processes, not just structures. Thisunderscores the importance of both structure andagency in the innovation process. This is not todownplay the importance of structural accounts butmerely to give more serious attention to socialprocesses of networking in innovation. In keeping withrecent work that has highlighted the importance of‘social capital’ (Nahapiet & Ghoshal, 1998), thischapter reveals how the development of the innovationprocess is intertwined with the creation and main-tenance of social networks over time.

Networking involves the active search and develop-ment of knowledge and information through thecreation and articulation of informal relationshipswithin a context of more formal intra- and inter-organizational structural arrangements. Networkingprocesses are critical for innovation precisely becausethey span structural forms rather than being containedwithin them. For example, they occur not just withinnetwork structures but also within hierarchies andmarkets. Networking is also self-sustaining and self-energising—one contact leads to, or precludes, thedevelopment of other contacts. Unlike the searchroutines highlighted in classical organization theory(Cyert & March, 1963), networking is wayward and

Linking Knowledge, Networking and Innovation Processes: A Conceptual ModelChapter 2

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emergent, being driven more by interest and opportu-nity, or by chance ‘accidental encounters’, than by therational needs of a particular decision-making process(Kreiner & Schultz, 1993). It is also important forinnovation because it involves the liberal sharing ofknowledge and information and an open-ended outlookon collaboration (Kreiner & Schultz, 1993).

This processual view of networks has importantimplications for the analysis of innovation. First, ithighlights the need to set aside static, institutionalexplanations of network development in order torecognize their emergent, formative qualities (Ebers,1997). Where a structural view refers to persistence,stability and established relationships, a processualview denotes the need to examine the role, ininnovation, of the sometimes fragile and exploratoryactivities based on embryonic contacts and half-formedrelationships. Second, this approach suggests that theconventional separation between inter- and intra-organizational networks overstates the effect ofmore-or-less settled organizational boundaries andunder-estimates the networking activities that aredirectly subversive of such boundaries. For example, ininnovation projects networking among actors fromdifferent organizations (such as that between projectmanagers and consultants) may be as close or closerthan networking among actors from within the sameorganization. Thus inter and intra-organizationalboundaries may become blurred as those involved maycome to identify more with the innovation process andtheir role in it than with their own organization.

The pervasiveness and importance of social net-working processes in creating and defining the role ofnetworks in innovation is underlined in this chapter bythree case examples. This extends the relevance of thenetwork concept beyond the question of structural formto the wider issue of the ‘organizing methods’ or the‘social practices of organizing’ (Knights et al., 1993)which are applied to socio-economic activity.

Innovation and Networking

Structural perspectives conceptualize networks as dif-fusion channels through which new ideas are spreadfrom innovators to adopters (e.g. Abrahamsson, 1991;Ebers & Jarrillo, 1997). In contrast, the processualview presented here not only addresses diffusion, butalso seeks to analyze ways in which networkingprocesses exert shaping effects on the character anddesign of the knowledge and innovations diffused. Therole of intermediaries (business support agencies, tradeassociations, professional institutes, universities etc.) inthe translation of knowledge relating to innovation isalso highlighted by Major & Cordey-Hayes (2003).During the diffusion of innovation, certain featurestechnologies may be highlighted, and others down-played, depending of the kinds of networking activity

involved and the vested interests of those concerned(Swan et al., 1999a). For example, in the mid-1980sManufacturing Resources Planning (MRP2) was heav-ily pushed by technology suppliers in the USA via theirengagement in a variety of networks, including pro-fessional associations. This became the dominanttechnology design for production management, eventhough other options existed at the time (e.g. Just InTime—Newell & Clark, 1993). This links the diffusionof innovation more closely to its design and imple-mentation and suggests a much closer examination ofthe interactions between networking activities andknowledge flows than is usually the case with morestructural accounts. In helping to understand theseinteractions two existing processual accounts areworthy of closer attention—those of Kreiner & Schultz(1993) and Ring & Van De Ven (1994).

Kreiner & Schultz’s (1993) study of informaluniversity-industry networks in the R&D environmentproposes a multi-stage analysis of network formation.The first stage of this process is ‘discovering opportu-nities’ which is activated by the accidental encountersand exploratory trust-building of a ‘barter economy’.This stage leads on to ‘exploring possibilities’ whereinitial ideas are tested and validated and projectsmaterialize. The final stage—‘consummating collab-oration’—is where projects are enacted through a‘crystallized network of collaboration’. The Kreinerand Schultz model contains some important insightsinto the links between networking and innovation. Forexample, they note that in the initial stages of ideageneration, the concept of knowledge exchange or‘know-how trading’ (von Hippel, 1988) fails to explainthe promiscuous sharing of ideas seen amongst theirR&D actors. This leads them to question the assump-tion that the act of sharing knowledge diminishes itsvalue to the owner. Rather, when knowledge consists of‘loose ideas and inspirations’ (p. 197), the immediatevalue of the knowledge being shared is low, but thepotential gain from combining it with the knowledge ofothers is high.

A further implication of the Kreiner & Schultzmodel is that the process of networking has a jointoutcome. While networking creates knowledge (in thiscase R&D knowledge), it also crystallizes new networkrelationships that then act as a ‘centre of gravity’ forfurther networking and research. In short, networkingcreates path dependencies in the production anddiffusion of knowledge by providing space for newnetwork relationships. It is important to note, however,that such path dependencies do not impose rigid searchbehaviours on network participants. Kreiner & Schultz(1993) suggest that innovation derives from the‘blending of ideas, knowledge, competencies, experi-ence and individuals’ (p. 200), but that this blendingusually happens in unplanned and emergent ways. Theimportance of ‘accidental encounters’ in the Kreiner &Schultz (1993) study underlines the central importance

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Jacqueline Swan, Harry Scarbrough and Maxine Robertson Part IX

to innovation, particularly in the nascent phases, ofinformal, opportunistic inter-personal relations in net-work formation. This need to recognize the role ofpersonal and informal dimensions of networks andnetworking in innovation is further echoed and ampli-fied in the work of Ring & Van De Ven (1994) whoseek to develop a socio-psychological account ofnetworking processes.

The Ring & Van De Ven (1994) model is presentedas a counterpoint to conventional analyzes of networks,contrasting, for instance, the importance of perceivedtrust and equity with the usual emphasis on transac-tional efficiency. This approach leads them to proposea cyclical model of network formation encompassingfour distinctive socio-psychological activities—nego-tiations, commitments, executions, and assessments.This model shows some important similarities with theKreiner & Schultz (1993) analysis. First, it is process-based—network structures are seen as an outcome ofnetworking processes. Second, it is recursive—net-working interactions closely resemble the path-dependencies noted by Kreiner & Schultz (1993).Third, it highlights the importance of exploratory trust-building interactions as the catalyst to networkformation. Fourth, Ring & Van de Ven (1994) alsohighlight the tensions between formal organizationalstructures, networking activities, and innovation,focusing in particular on the tensions created betweenorganizational roles and personal interactions, withnarrowly defined roles limiting the scope for network-ing and innovation.

Innovation and KnowledgeThe important role played by networking highlightedin these studies, underscores the need to revise ourunderstanding of innovation. Traditional views havetended to emphasize the creation and distribution ofphysical artefacts—ideas are invented, distributed asphysical artefacts, and then implemented in firms(Clark, 1995; Rogers, 1983, 1995). This artefact-basedmodel is increasingly challenged, however, by thegrowth of the service sector and the rise of knowledge-based products and processes (reflected in the growthof the consultancy industry). In this context, innovationis better conceptualized not as a materially-constitutedentity but as a particular transformation of knowledgeand information (Macdonald & Williams, 1992;Nonaka et al., 2003). This ‘knowledge-based’ view ofinnovation moves us beyond the linear assumptions ofthe artefact-based model and highlights the complexand recursive interactions that filter and shape theinnovation from inception through to end-use.

One implication of this ‘knowledge-based’ view ofinnovation is the significance attached to networks asthe means through which knowledge is elicited,translated and (re)combined to produce innovation.However, an emphasis on networking suggests thatsuch networks are not passive channels for the transfer

of knowledge but are also implicated in its active(re)production and appropriation (Alter & Hage, 1993;Clark, 2000). This further differentiates the processualview from the so-called ‘entitive approach’ of theartefact-centred model (Hosking & Morley, 1990).Entitive perspectives treat innovation as an object orthing which is invented and diffused, more or lessunchanged, from one adopter to another. In contrast,the knowledge-based view, not only suggests thatinnovation involves complex interactions betweendifferent groups and constituencies, but also that theseinteractions have a shaping and filtering effect upon theinnovation itself. In short, during innovation, knowl-edge and information is both communicated andtransformed by the network of social actors.

Taking this further, the different ‘episodes’ withinthe innovation process (invention, diffusion and imple-mentation) can be characterized as involvingdistinctive shifts in the ways knowledge is constituted(see also Major & Cordey-Hayes, 2003). The episodeof invention, for example, may be characterized interms of the (social) construction of knowledge (Bijkeret al., 1987). Here, loose ideas shared through interper-sonal networks may crystallize into new forms of workpractices or products. In contrast, the diffusion episodeis associated more closely with the commodificationand communication of knowledge (Rogers, 1983). Thisepisode requires that knowledge be made more explicitand codified in order to be translated to a wider socialconstituency. Diffusion involves, then, the progressiveobjectification or ‘black boxing’ of knowledge, suchthat its communication and distribution ceases to bedependent on the particular tacit understandings andsocial context of its creators, but is transformed intomore explicit, generic and therefore, more widelyportable forms (Scarbrough, 1995). In contrast, again,the episode of implementation relies on the appropria-tion of knowledge within firms (Clark, 1987). Heregeneric, objectified ideas about new work practices,technologies or products need to be applied to thespecific context of the adopter by customizing andadapting them to local requirements. This involvesunpacking knowledge from its objectified state andfusing it with local and often tacit knowledge of theorganization (Fleck, 1994).

A Model to Link Innovation, Knowledge andNetworkingThe discussion so far has outlined the importance ofnetworking in innovation by conceptualizing innova-tion as a process that involves flows and combinationsof knowledge and information occurring throughnetworks and networking. This suggests that a con-ceptual model that is able to link networking processesand innovation could be useful. However, the differentknowledge requirements of different episodes of theinnovation process also suggests that any model to linkinnovation and networks needs to be able to relate


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specific types of networking activities to particularepisodes within the innovation process.

A number of different perspectives have beenapplied to this issue. As noted, many writers adopt astructural approach, which sees the creation of net-works as a consequence of the failure of market andhierarchical forms to adequately regulate certain kindsof transactions (Casson & Cox, 1997; Grandori &Soda, 1995; Poire & Sabel, 1984; Powell et al., 1996).However, there are important limitations to thisanalysis. First, it emphasizes the functional character-istics of organizational and institutional structures overthe agency of groups and individuals. Yet, by focusingon agency, it is clear that networking relationships mayoccur, for example, within markets and hierarchies andso these may be complementary, rather than compet-ing, forms (Holland & Lockett, 1997). Second, theemphasis on individual transactions is a poor character-ization of the rich social interactions involved in thetransfer and exchange of knowledge. For example,Kreiner & Schultz (1993) note the inappropriateness ofan exchange perspective to the R&D environment. Thissuggests that the potential creation of new economicbenefits from the innovation—as opposed to thedistribution of existing benefits—may help to lowersome of the transactional barriers to the communica-tion and exchange of knowledge (Lazonick, 1991).Third, the economists’ concerns with the problems ofcosting and exchanging knowledge tend to gloss oversocial concerns to do with the legitimation of knowl-edge (Casson & Cox, 1997; Williamson, 1985). Beforeknowledge can be effectively diffused and imple-mented (for example, as a new form of ‘best practice’),it has to be accepted as legitimate by the relevant socialgroup. Therefore, social and institutional mechanismsof validation are also critical.

The social validation of knowledge involves estab-lishing the credibility, the essential ‘rightness’ of whatis proposed. In a technological context a variety ofinstitutional networks may perform this functionincluding, importantly, professional associations, tradeassociations, policy making bodies and so forth (Major& Cordey-Hayes, 2003; Swan et al., 1999a). Hence, thenetworks which facilitate the communication andexchange of knowledge often, simultaneously, providethe means of its validation. Persuasion and co-optationthrough networking activities, rather than exchangeseem, then, to be key (Callon, 1980). Thus, evenobjectified forms of knowledge—emerging technolo-gies, for example—will depend for their acceptance onthe underpinning validation supplied by industrystandards, relevant professions, and adoption of ‘best’practice by ‘leading’ firms (King et al., 1994). Inter-organizational networks that help to communicate suchstandards will be crucial.

Taken together, these points suggest that networkingactivities are particularly suited to the dynamic andunstructured flows of knowledge associated with

innovation processes. Networking operates within andacross markets and hierarchies. It helps to address thetransactional ‘stickiness’ of knowledge by promotingtrust and stimulating value creation through innovation.At the same time, it also serves as means of validatingknowledge by enrolling network partners. Thus, net-works serve both to disseminate and to transformknowledge. Taking these arguments together suggeststhat a model of the role of networks in innovationshould incorporate the following features:

(1) It should recognize that the roles of networksvary across episodes involved in the innovationprocess.

(2) The importance of knowledge transformation(involving construction, communication andexchange) should be highlighted.

(3) Both structural and processual dimensions ofnetworks should be incorporated.

(4) It should recognize the changing role of personalsense-making and trust-building activities in theevolution of networks through networking activ-ities.

(5) Path-dependency of both the innovation processand network formation should be included.

(6) The implications of different kinds of knowledgefor the roles played by networks should beaddressed.

A model that encompasses the theoretical points aboveand relates different episodes of the innovation processto networking activity is presented in Fig. 1. Thisfocuses on the interplay between the three criticaldimensions identified in the discussion above—networking activity, knowledge attributes, and the epi-sodic innovation process. However, unlike the existingmodels outlined above (e.g. Kreiner & Schultz, 1993),Figure 1 attempts not just to describe the interplaybetween these different elements, but also to comparedifferent innovation episodes in terms of the differentkinds of networking involved.

Mapping these complex interactions in this sche-matic way requires a number of theoretical caveats andqualifications. For example, the relationships betweenthe different episodes of the innovation process must beseen as operating in a non-linear, recursive fashion(Clark & Staunton, 1989; Fleck, 1994). At the sametime, while the character of networking is broadlycorrelated with the relative codification of knowledge(global, inter-organizational networks serving to dis-seminate more codified knowledge, for example), it isalso important to acknowledge the continuous interplaybetween the ways in which knowledge is createdand exchanged. Although Fig. 1 suggests that thedominant forms of knowledge transfer may varyfrom one episode to another, the overall scope anddirection of the innovation process will reflect theseco-dependencies. It follows from this discussion that

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the curve outlined in Fig. 1 defines the interactionbetween networking and innovation, not only in termsof the distinctive characteristics of networking indifferent episodes (e.g. inter- or intra-organizational),but also in terms of its extent or scope. This is denotedin Fig. 1 by the area encompassed within the curveduring the different episodes. The constituent elementsand assumptions of this model are characterized asfollows:

Invention

During this innovation episode, the focus is on thesocial construction and creation of new knowledgethrough an exploratory and highly personalized processof networking among a fairly narrowly defined socialgroup who have relevant tacit knowledge and interests.This is referred to in Fig. 1 as ‘local intra-organiza-tional’ networking in order to reinforce the point thatnetworking activities may cut across organizationalboundaries. However, the advantages of physicalproximity for interpersonal networking, means thatnetworking within the firm would be likely to beespecially crucial.

During this episode informal, interpersonal network-ing seeks to identify potential network participants whopossess information and expertise that could berelevant to the development of new products orservices. This networking will typically be waywardand emergent, with initial lose contacts quicklygenerating stronger ties as individuals come to realizethat they have some common interest. ‘Accidentalencounters’ may be important here (Kreiner & Schultz,1993). Formal or informal coalitions (e.g. projectteams) are assembled on the basis of (uncertain)expectations of reciprocity and trust and a willingnessto share knowledge (Ring & Van de Ven, 1994). Theseexpectations are generated through repeated social

interaction but may also be signalled by contextualcues. For example, membership of a particular organi-zation or profession may imbue incumbents withexpected expertise (Meyerson et al., 1996). A key roleof social coalitions and teams is in the testing andinterpretation of knowledge—what Ring & Van De Ven(1994) term ‘sense-making’. The emphasis here, then,is on the sharing and creation of knowledge, rather thanon the exchange of information or artefacts. Becauseoutcomes are uncertain, economic considerations willbe secondary to interpersonal, trust-based interactions.

Diffusion

During the diffusion episode, the emphasis shifts to theobjectification and communication of knowledgethrough more global, inter-organizational networks.Ideas, now crystallized as new technical artefacts,products or services, become commodities that can beexchanged. Here the primary role of networking is tobroadcast knowledge to legitimize particular inventionsor new ideas (or old ideas repackaged) so that theybecome accepted and adopted by the wider community.For example, particular templates for technologydesign may be promoted to the wider community asnew forms of ‘best practice’.

Diffusion thus involves a social process of formaland informal information exchange among members ofa social system (Rogers, 1983, 1995). This process isunequal and may be conflictual. For example, differentgroups of ‘change agents’ (such as salespeople,consultants, firms) may aggressively and opportunis-tically promote the adoption of particular ideas orartefacts where it is in their interests to do so (Swan &Newell, 1995). New ideas may also be diffusedthrough the ‘weak ties’ linking different socialgroups (Granovetter, 1973; Hansen, 1999). ‘Boundaryspanning’ individuals (e.g. key consultants) play a role

Figure 1. A model linking networking, knowledge and innovation.


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in this diffusion process, involving themselves in awide range of broader inter-organizational networksand translating ideas developed through this network-ing into locally applicable or organizationally usefulsolutions (Tushman & Scanlan, 1981). Thus, boundaryspanning individuals act as ‘knowledge brokers’,helping to overcome social and organizational barriers(Aldrich, 1999).

Particular inter-organizational networks may alsoplay a brokerage role in providing opportunities formembers from different social communities to meetand interact. Professional associations, for example,allow links to develop between practitioners working inindustry, academics, consultants and technical special-ists, thereby promoting knowledge flows within aparticular professional knowledge domain (Aldrich &von Glinow, 1992). At the same time, market structurescreate distinctive incentives towards the commodifica-tion of knowledge. Some networks (e.g. withtechnology suppliers) will act as distribution channelsfor pre-packaged knowledge and ideas (Scarbrough,1995).

The distributed nature of networks in this episodemay enforce a reliance on surrogate indices of thevalidity and legitimacy of the knowledge being dif-fused. One such surrogate may be found in theprofessional ethos and credentials of the networksthrough which knowledge is communicated. For exam-ple, professional groupings such as professionalinstitutes and trade associations may be seen as more‘impartial’ communicators of the ‘state of the art’ thanorganizations with more naked commercial interests.As we will see, this assumption is not alwaysjustified—professional networks may well be colo-nized by actors with commercial interests—but it mayhelp to explain the importance of professionalizednetworks in communicating knowledge.

The usefulness of inter-organizational networks inlegitimizing and diffusing knowledge is not withoutconsequences for innovating firms. Such networks alsoexercise a shaping effect upon the range of techno-logical options available to such firms. For example,DiMaggio & Powell (1983) argue that the greater theinterconnections among firms within a particularcommunity, the greater the tendency for ‘isomorphism’to occur—for example, through mimetic, normative orcoercive processes—that leads firms to resemble oneanother. Hence networks for diffusion may para-doxically allow new ideas to diffuse more widely but,at the same time, place tighter constraints around theparticular ideas that will be considered legitimatewithin the community. Again, social agency is impor-tant here—by engaging in diffusion networks,particular constituents (or agents) can influence furtherprocesses of invention and implementation. For exam-ple, Rogers (1983) describes how the Dvorak designkeyboard—a more efficient alternative to the existingQwerty—was effectively precluded by core con-

stituents (e.g. manufacturers and teachers) with vestedinterests in maintaining the status quo.

ImplementationThis episode refers to the local appropriation of newideas as organizationally-specific solutions. Ofteninnovations cannot be adopted by organizations as ‘offthe shelf’ packages. Rather, they represent multifaceted‘bundles’ of knowledge that require modification andreconfiguration to adapt them to specific technical andorganizational contexts (Clark, 2000). In implementa-tion episodes, the deconstruction and re-construction ofknowledge comes to the fore. Generic, objectified ideasfloated through global, inter-organizational networksneed to be appropriated by blending with ideas aboutspecific organizational problems and context (Clark,1987). The customization, during implementation, ofstandardized software packages diffused by technologysuppliers through global inter-organizational networksso that they meet local user requirements, is a goodexample of the knowledge appropriation involved inimplementation (e.g. Robertson et al., 1996).

Here networking is more purposeful, as those withinterests in implementing specific solutions use net-working to mobilize the information and resources(including political and social resources) that will berequired. With many kinds of implementation, localintra-organizational networking again becomes impor-tant as new generic ideas are interpreted and blendedwith existing local, often tacit, knowledge and asproject owners attempt to generate the commitmentneeded from the relevant social groups (Fulk, 1993).Thus inter- and intra-organizational networks mayconverge during implementation—weak inter-organiz-ational ties for information search combine with strongintra-organizational ties required for the formation ofproject teams.

Figure 1 depicts these episodes of invention, diffu-sion and implementation as unfolding recursively overtime through the medium of networking. Local, intra-organizational networking clusters around the intensiveepisodes of invention and implementation, wheretightly integrated knowledge and information flows arerequired. New relationships are forged on the basis ofreciprocity and the development of trust—the kind of‘barter economy’ described by Kreiner & Schultz(1993). Then, in diffusion, networking is extensivelyrather than intensively oriented and emerges around thedissemination of more explicitly objectified knowledgeflows. Relationships are more structured than emer-gent, and are more explicitly transactional ormarket-based (Scarbrough, 1995). Issues about thevalue of what is being diffused are resolved throughmore institutionalized forms of legitimation and vali-dation.

The remainder of this chapter operationalizes andfurther develops this model using case examples ofinvention, diffusion and implementation. These brief

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case ‘vignettes’ are taken from our earlier empiricalstudies of innovation and networking. The first focuseson the role of local, intra-organizational networkingwithin a scientific consultancy generating inventionsfor clients. The second case examines the ways inwhich global inter-organizational networking madepossible by a professional association can serve as avehicle for the diffusion of innovation in the area ofoperations management. The third case examines howthe knowledge diffused via global networks is appro-priated in situ with that constructed via local networksduring the implementation of operations managementtechnology in a particular manufacturing firm. Thelinks between networks, knowledge and innovationepisodes identified in these cases are summarized inTable 1. The comparison between them also allows

further analyzes of the relative value of different formsof networking in different episodes of the innovationprocess.

Case Example 1: Universal Consultancy—TheRole of Networks in InventionThis case example is based on longitudinal research atthe ‘Universal Consultancy’ organization (see Robert-son & Swan, 1998 for details). Universal was foundedin 1986 by a charismatic, highly successful individualand, over time, had grown from a handful of consult-ants, to an organization employing 150 consultants atits main headquarters and a further 110 on an associatebasis in the U.S., Japan and Europe. The organizationis a laboratory-based, business and technology, con-sulting and investing company specializing in the

Table 1. A summary of networks, networking and knowledge transformation during episodes of the innovation process.

Innovation Episode Dominant networks Features of thenetworking process

Role of networks Knowledgetransformation

Invention:UniversalConsulting CaseExample

Local intra-organizational—strong ties within

consultants’ firmLocal inter-organizational—strong ties between

consultants and clientsMainly interpersonalnetworking

Wayward andemergent: formationof many strong tiesand some weak ties

Coalition buildingaround new ideasand clients projects

Knowledge created,socially constructedfrom looselystructured,ambiguous and novelideas into newproducts/services

Diffusion:ProfessionalAssociation CaseExample

Global inter-organizational—many weak ties across firms

via professional association —relatively strong ties between

MRP2 consultants andprofessional association

Mainly informationalnetworking plus interpersonalfor an active minority

Opportunistic andconflictual: primarilythrough many weak/indirect ties and fewstrong ties

Brokerage,broadcasting andlegitimation of newtechnology as ‘best’practiceSelective promotionof knowledge (e.g. inthe form oftechnology designtemplates)

Knowledgeobjectified,commodified andcommunicated usingmaterial artefacts and‘best practice’methodologies

Implementation:LiveCo CaseExample

Local intra-organizational—strong ties among project

teamLocal inter-organizational—formal relationship between

team and IT consultantGlobal inter-organizational —weak ties with other firms,

professional association, ITvendors.

Intra/inter-organizationalboundaries blurredMainly interpersonalnetworking plus formal, client/consultant relationship

Purposeful,intentional: primarilyusing weak ties forinformation searchcoupled with strongties in project team

Exchange of requiredinformation andresources (includingpolitical and social)

Knowledgeappropriated(unpacked andreconfigured) intoworkableapplications inspecific context bycombiningobjectifiedknowledge with local(often tacit)knowledge


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invention of novel scientific products and services,which are sold to clients in the form of IntellectualProperty Rights (IPR).

All consultants in Universal were educated to Ph.D.level within their respective scientific subjects andmany were considered ‘world’ experts. The emphasiswas on inventing new products and services byconsultants combining their different areas of scientificknowledge with a keen commercial awareness (i.e.characteristic of the ‘symbolic analyst’—Reich, 1991).The overall ethos of the firm (vehemently defended bythe founder) was that sustainable competitive advan-tage was to be achieved through innovation and thatthis depended on the effectiveness of the skill base.Universal’s competitive advantage was based, then, onits ability to rapidly develop inventions in line withclient requirements. This capability was maintainedand supported by the method of working that reliedheavily on the local, intra-organizational networkingactivities of consultants.

Consultants were allocated across seven divisionsreflecting broad specialisms (e.g. applied science, IT,engineering) but these existed purely for administrativepurposes. In general, formal structures were quitedeliberately opposed—the overriding emphasis being,instead, on maintaining a non-hierarchical (with theexception of the founder), egalitarian approach toorganizing. Divisions were created, merged and dis-banded over time in a reactive manner, premised onmarket opportunities and consultants were reallocatedaccordingly. Consultants would come together in self-forming teams for the duration of projects, regroupingas new projects and personal interests demanded. Theapproach to organizing was typical of the ‘adhoc-racy’—an organizational form considered to stimulateinnovation (Mintzberg, 1983)—and work organizationwas described as ‘fluid’. Everyone, including theFounder, was actively involved in project working.Project working was to be largely unconstrained byorganizational (hierarchical or divisional) boundaries.The ‘modus operandum’ was intensive, local, intra-organizational networking, mobilized around theinventions being worked on at any point in time. Inter-disciplinary working across divisions was encouragedand valued—indeed many consultants had chosen towork at Universal because of its emphasis on cross-disciplinary working and informal work practices.

As the organization grew it became necessary toconsider mechanisms that would sustain networkingacross divisions. One mechanism introduced was basedon individual performance targets set within a loosefinancial control system. Personal revenue targets(PRTs) were established by the Board yearly andperformance was monitored monthly. Given theemphasis on egalitarianism, the same monthly PRTapplied to all consultants. This meant that Divisionaltargets were directly related to the number of consult-ants within each division. Personal (and therefore

Divisional) revenue was generated by consultantsgetting themselves involved in a project and receivinga share of the project revenue (decided throughnegotiation with the project leader). To get involved innew projects—and so meet Divisional targets—con-sultants were expected, and encouraged, to activelyengage in intra-organizational networking. This intra-organizational networking allowed them to markettheir own expertise so it could be spotted and exploited.Although this local networking was mainly intra-organizational, it also extended to the client base. Overtime then, as the organization grew, local inter-organizational networking expanded as consultantscreated and built their own client base. Marketopportunities were discovered through a dual processof trust-building and exploring possibilities for inven-tion with clients and consultants in other divisions, in amanner reminiscent of the university-industry R&Dnetworks described by Kriener & Schulz (1993).

Project leaders and project team members were notformally allocated at Universal. Rather, they emergedduring the proposal stage of a project. Typically theproject leader was the consultant who was seen to havegenerated the market opportunity via his or her localnetworking. At the project proposal stage, the potentialgain of combining knowledge with others was high—‘know-how’ trading (von Hippel, 1988) and thepromiscuous sharing of ideas within the intra-organizational network was common (Kreiner &Schulz, 1993). Project leaders were also expected to befair in their assessment of individual consultants’ likelycontribution. This helped to generate trust-based rela-tions and perceptions of equity and so sustained andstrengthened the intra-organizational network (Ring &Van de Ven, 1994).

In short, a micro-economy for knowledge existed atUniversal—the PRT system demanded that individ-uals’ knowledge be made ‘visible’ and traded withinthe organization. This micro-economy facilitated infor-mal and interpersonal intra-organizational networkingnecessary to sustain invention. This networking, inturn, encouraged the social construction and creation ofknowledge in projects and also enhanced the individualconsultants’ own knowledge base. This expandingknowledge base then further advanced the internal‘marketability’ of the individual consultant. Given theabsence of structurally-defined roles and responsibili-ties, there was an overriding emphasis on socialrelations—these being governed by a psychological,rather than a legal or employment-based, contract.These are defining characteristics of co-operativenetworking relationships (Ring & Van de Ven, 1994).

When project work began it was characterized by theexchange of tacit and explicit knowledge (Nonaka &Takeuchi, 1995) among consultants. Some client inputwas necessary, particularly at the early stages, butbecause of the consultants’ high levels of scientificexpertise, knowledge creation relied more heavily on

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intra-organizational networking activity. This intra-organizational networking was based mostly on inter-personal contact, for example, through face-to-faceinformal ‘brainstorming’ sessions. Many consultantsalso spent a considerable amount of time travelling soe-mail was also used intensively. However, the limita-tions of electronic communication, and the advantagesof face-to-face interaction for creating knowledge wererecognized (and referred to, often) by consultants. Thesuccessful conclusion of project work occurred whenthe knowledge that had been created through this intra-organizational networking activity became objectifiedand communicated to the client, for example in theform of a new IPR.

The Universal case highlights the ways in which thedynamics of local and intra-organizational networking,driven by an internal ‘market for knowledge’, couldpromote coalitions around new ideas and projects, andcollaborative efforts to innovate, even amongst a groupof workers that were (as in this case) highly individual-istic. This intra-organizational networking was mostlyinformal and interpersonal and emerged around theinventions themselves. It was supported by the socialcontext, in particular a strong egalitarian culture and anefficient information (e-mail) system. These kind ofintensive informal interpersonal networking activitiesgenerated social mechanisms (e.g. the formation oftrust, establishing reputation, negotiation of responsi-bilities, collaborative working and so forth) that wereimportant for further invention. Given the nature of thework (and workers) involved, these mechanisms wouldhave been much more difficult, if not impossible, toachieve through more formal means.

Case Example 2: A Professional Association—TheRole of Networks in DiffusionThis section examines the relationship between thenetworking engendered through a particular pro-fessional association, and its role in the diffusion oftechnologies for operations management (for details ofthe empirical work see, Newell & Clark, 1993; Swan &Newell, 1995; Swan et al., 1999a). The association isthe Institute of Operations Management in the U.K.The IOM, like its American Counterpart—the Amer-ican Production and Inventory Control Society(APICS)—comprised members from different occupa-tional sectors (e.g. manufacturing, consultancy,software and hardware suppliers, academics) andmarket sectors (e.g. pharmaceuticals, automotive, foodand drink). This association therefore played a ‘broker-age’ role, operating as an extensive (or ‘global’)network that generated a large number of ‘weak ties’across individuals from different organizations(Aldrich & von Glinow, 1992). Weak ties have beenidentified as important for the diffusion of innovationbecause they allow ideas to spread across socialcommunities and allow firms to encounter ideas that

are not bounded by the usual norms of their particularsector (Granovetter, 1973; Rogers, 1983).

The primary aims of the IOM were: first, to keepmembers who work in industry up to date with newdevelopments in operations management; and second,to enhance the professional profile of careers inoperations management (these have traditionallyafforded relatively low status in the U.K.). To achievethese aims, the IOM organized a formalized program ofevents (e.g. journals, conferences, seminars, and com-pany presentations) aimed at broadcasting informationabout new technological solutions to members. It alsoorganized educational qualifications in an attempt toprovide a clearer career path for those working inoperations management.

Members who actually attended formal meetingsalso had opportunities to meet informally and discussnew ideas. The professional association thus created anarena in which knowledge and ideas relating toinnovation could be exchanged both through formaland informal networking. However, although opportu-nities for informal, interpersonal networking existed,only around 20% of members actively exploited theseby attending events on a regular basis. The passivemajority just read, or scanned, the information that wastransmitted through the association’s journals—theirnetworking was informational (contact with informa-tion) rather than interpersonal (contact with othermembers). The role of the IOM, then, was largely oneof broadcasting—it acted as a diffusion networkwhereby knowledge that had already been created andarticulated in explicit forms by the more activemembers (e.g. in the form of written articles) wasbroadcast to the relatively passive community.

Because professional associations rely heavily onvolunteers, the shape of their activities depends on theinterests of those particular social groups who getinvolved. For example, the IOM depended heavily onvolunteers to organize events, write articles for jour-nals, teach on courses and so forth. Technologysuppliers (software vendors and consultants) wereparticularly active in the IOM. They got involvedbecause they saw the professional association as animportant global network for marketing their particulartechnologies. Thus, although they comprised a minor-ity of members (23% as compared to 70% who work aspractitioners in manufacturing), technology supplierswere extremely active in articulating the informationthat the IOM then disseminated to members inindustry. For example, the bulk of the informationdisseminated by two of its key activities—journals andconferences—was written or presented by the muchsmaller group of technology suppliers (Newell et al.,1997).

One of the most widely known technologicaldevelopments for operations management is known as‘Manufacturing Resources Planning’ (MRP2). Theconcept of MRP2 diffused widely during the mid-


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1980s to mid-1990s, mainly from the U.S. to Europe(Clark, 1987). The early diffusion process was drivenby aggressive marketing among suppliers of softwareand hardware (notably IBM) and also by consultantsselling training and education to accompany theintroduction of the MRP2 ‘philosophy’ (notably theOliver Wight Consultancy in the U.S.—Wilson et al.,1994). Market arenas, bolstered by the many problemsthat firms had experienced in implementing technolo-gies to date, developed and provided incentives andopportunities for the commodification of knowledgeassociated with MRP2. Technology suppliers played akey role in this—effectively objectifying knowledgeabout MRP2 in the form of material artefacts (softwarepackages) and tightly prescribed methodologies thatcould, it was claimed, be used in anywhere. Forexample, Oliver Wight developed a step-by-step‘Proven Path’ to successful MRP2 implementation(Wight, 1984). MRP2 was promoted by many as thedefinitive ‘best practice’ for operations management,even though alternative technologies were known atthat time (Swan & Newell, 1995). The diffusionprocess, then, was driven by the commodification andobjectification of knowledge. Whilst this was a usefulmarketing strategy for technology suppliers, it causedproblems for users because the objectified MRP2solutions presented simplified the technology and de-emphasized the need for organizational appropriation(as discussed below—Clark & Staunton, 1989).

In the U.S. and U.K. the professional associationsplayed a critical role in the diffusion of this objectifiedknowledge about MRP2 in two key respects. First, theyacted as global inter-organizational networks for thebroadcasting of information about MRP2. As seen,technology suppliers play an active role in shaping theinformation that these networks disseminate. In thiscase, a few major suppliers of MRP2 systems (e.g.IBM) took the opportunity to form strong ties with theprofessional association organizers (e.g. APICS).These resulted in the APICS network being enlisted tohelp in an ‘MRP2 Campaign’ to disseminate knowl-edge about the new MRP2 technology to its members(Vollman & Berry, 1985). Further, a global networkingarrangement between the IOM and APICS was sig-nificant in providing a channel through which bestpractice ideas originating in the USA could bepackaged and diffused to a practitioner community inthe U.K. (Clark & Newell, 1993).

Second, the professional association networksplayed an important legitimizing role—MRP2 becameaccepted by firms in industry as the latest ‘bestpractice’ in part because communication about it wasbeing broadcast via the professional associations.Whilst information encountered through professionalassociation networks reaches only a subset of therelevant community, it is afforded a very high level oflegitimacy and validity. Where close interpersonal trustamong members of a network is not present or difficult

to develop, then problems surrounding competingclaims to knowledge are solved by trusting your source(Meyerson et al., 1996). If the source is a professionalassociation then credibility is likely to be greater thanif the source is a more direct link with a technologysupplier. Thus, ideas diffused via the professionalassociation network are likely to be seen by potentialadopters as impartial, even though (as seen above) theymay have originated in the supply side.

This case illustrates how professional networks inthe U.K. and U.S. played both a broadcasting andlegitimating role in the promotion of objectifiedknowledge about MRP2 as a new ‘best practice’technology design. This role is particularly salientbecause of the extensive weak ties generated by suchnetwork structures. However, because much of thenetworking activity of members was to do withtransmitting information, rather than with developinginterpersonal relationships, and because much of thisinformation was shaped by an active supply side withinterests in selling technologies, only positive featuresof the technology that would encourage it to be adoptedwere communicated. In contrast, the difficulties andcomplexities associated with the technology and itsimplementation were heavily downplayed. Thus whilstthis inter-organizational networking encouraged rapiddiffusion, it also generated potential problems forimplementation. These are illustrated in the case thatfollows.

Case Example 3: A Manufacturing Firm—TheRole of Networks in ImplementationThis section presents the case of a manufacturing firm,which successfully implemented a new MRP2 system(for details of the empirical work see Robertson et al.,1996). It illustrates how the coalition of global andlocal networks played a key role in the implementationprocess.

The case study firm is referred to herein as LiveCo.LiveCo is a large vehicle manufacturer in the U.K.operating to a make-to-order profile. The implementa-tion process in LiveCo began in the late 1980s when adecision was taken by members of the Board to investin and implement an MRP2 system. At this time thecompany was facing a financial crisis—sales weredeclining in all markets due, it was claimed, toLiveCo’s outdated product range. The firm decided toconsolidate its manufacturing operations from 14geographically distributed different sites into one. Thismajor organizational change clearly had a profounddestabilizing effect on local networking activity. Thisto some extent made it easier to develop new localnetworks specifically in relation to the MRP2 project.

The philosophy behind MRP2 technology is tointegrate information used for different aspects of themanufacturing process with wider capacity planningand sales forecasts, so that materials are available whenneeded without holding unnecessarily high levels of

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inventory. Due to this demand for integration, theimplementation of these technologies often requiresconsiderable change in both organization and technicalpractice (Clark & Staunton, 1989). The implementationof MRP2 technologies, then, depends heavily on thecontext into which they are introduced and require ablending of both technical and organizational knowl-edge. The notion of a single, generic, ‘best’ practicewith regards to MRP2, promoted (as seen) throughdiffusion networks, is actually quite misleading when itcomes to implementation. Rather, MRP2 technologiesneed to be (re)configured according to the uniquecontext in which they operate (Fleck, 1994; Swan et al.,1999b). Some researchers refer to this as a process ofknowledge appropriation (e.g. Clark & Staunton,1989). The need for organizational integration, inparticular, has posed many problems for user firmsattempting to implement MRP2 technologies, withexamples of failure or partial failure littering theresearch on implementation (Waterlow & Monniot,1986; Wilson et al., 1994). LiveCo was perhapsunusual in managing to implement MRP2 technologysuccessfully, achieving both high levels of integrationand appropriation of the technology within a relativelyshort timescale.

LiveCo was structured along traditional hierarchicaland functional lines. Because of this, intra-organiza-tional networking might have been expected to bedifficult. However, because of the uncertainty sur-rounding reorganization, formal routines wereintroduced that demanded that senior managers fromall functions would have an input into all major policydecisions, including those concerning new technology.In line with this policy, LiveCo developed a formalcross-functional senior project team to handle imple-mentation (comprising operations management,manufacturing engineering, manufacturing systems,sales and marketing, and logistics). This team metregularly and over an extended period of time, mostlyon a face-to-face basis. A crucial feature of imple-mentation in this case, then, was the development oflocal intra-organizational network, comprising power-ful individuals who were engaged in interpersonalnetworking that transcended functional boundaries.Because individuals in this team were more or lessequal in terms of their formal status, regular negotia-tion took place over project commitments, directions,roles and responsibilities. Thus, the formation of thisnetwork comprised many of the social processeshighlighted by Ring & van de Ven (1994) as importantfor inter-organizational networking. This local net-working also extended beyond the LiveCo organizationto include information systems support from a special-ist IT consultancy. This had a long history of workingwith LiveCo and provided, among other things, aconsultant to be a permanent member of the projectteam. Thus inter and intra-organizational boundariesbecame blurred during implementation.

Project team members developed an awareness ofMRP2 ‘best practice’ via their involvement in anumber of inter-organizational networks. For example,members of the project team had heard about MRP2from reading trade and professional association jour-nals and from software vendors and their publicitymaterials. As the logistics manager commented ‘it wasdifficult to read or speak to anyone back then withoutMRP2 being mentioned as the answer to all ourproblems’. This manager was also an active member ofthe IOM and had attended IOM courses and seminarswhere MRP2 had been advocated. The IT consultant inthe team also advocated the use of MRP2 and arrangedfor the Oliver Wight consultancy to present the MRP2concept to the board. Thus inter-organizational diffu-sion networks played a significant role in alertingproject managers to generic notions of MRP2 ‘bestpractice’.

However, armed with a good understanding of localmanufacturing operations developed through theirlocal networks, the project team rejected supplierprescriptions regarding MRP2 ‘best practice’ imple-mentation. They were aware that these prescriptionsdid not sit comfortably with their particular manu-facturing profile. Instead, team members explicitly setout to use their own informal interpersonal contacts inother manufacturing firms (e.g. friends and ex-colleagues) to arrange factory visits to other ‘like’companies and to see for themselves what they weredoing. These site visits allowed the team to develop anunderstanding of a broader range of technologicaldesign templates for operations management thanMRP2. During implementation then, some of theseideas were blended with the MRP2 ‘best practice’template, with the result being that MRP2 wasimplemented in a limited capacity for high levelplanning, whilst detailed shopfloor planning and con-trol was achieved with a combination of in-housesoftware and a Just-in-Time ‘Kanban’ system. Initialeducation and training for a broader group of seniormanagers was then provided by a consultancy special-izing in MRP2. However, because project teammembers were aware that this only offered, as they putit a ‘single-point’ solution, they also developed in-house training to show users how knowledge aboutMRP2 concepts was to be appropriated within theparticular operational context of LiveCo.

This case illustrates how knowledge cultivated viaintra- and inter-organizational networks may beblended during implementation. A major factor inLiveCo’s ability to successfully develop and implementtheir MRP2 system was that they recognized thelimitations of the tightly prescribed and commodifiedknowledge regarding MRP2. They were thus able tounpack the knowledge gained via inter-organizationaldiffusion networks and blend this with that gained vialocal networking activity to implement a system thatwas appropriate for their specific context. The fact that


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implementation team members were from differentfunctions extended local networking activity to cover abroader range of expertise.

ConclusionIf the three cases (summarized in Table 1) arecompared in terms of the model outlined in Fig. 1, aninsight can be gained into the differing roles played bynetworks and networking within the innovation proc-ess. Thus, the shallow curve that we see in the typicalproject in Universal reflects a concern with the socialconstruction and creation of knowledge to producecustomized client solutions. Although local inter-organizational networks with clients were importantfor identifying market opportunities, intra-organiza-tional networks among consultants, mobilized by thedevelopment of informal, interpersonal, and trust-based relationships, were more important forknowledge creation. This has certain advantages for theinvention process. Universal is able to mobilize anextensive knowledge community in which knowledgesharing is relatively open and more or less unaffectedby the problems of opportunism seen in marketrelations. This certainly enhances the speed andresponsiveness of the invention process.

Universal Consultancy represents an organizationthat has managed to create a powerful local networkingenvironment that encourages the sharing of knowledgeand information. The ability of organizations topromote such an environment should not be overesti-mated. Despite Williamson’s (1985) claim thathierarchies are generally more conducive to the sharingof knowledge than markets, it is clear that manyorganizations find it difficult to promote open sharingof knowledge amongst their employees. This is partlydue to internal organizational boundaries (e.g. func-tional departments) but it also reflects the instrumentalattitudes that may be fostered by the employmentrelationship. Put simply, even within a hierarchy,groups and individuals may view knowledge as privateproperty to be hoarded and only grudgingly orcalculatedly shared. In this case, the financial perform-ance system developed by the consultancy, as well asthe prevailing egalitarian culture, plays a powerful rolein fostering the kind of organic, loosely structuredsharing of knowledge that is critical in the generationof invention. At the same time, the effective use ofemail reinforces this by making it possible forknowledge sharing to transcend face-to-face contact.

In contrast, the steeper curve of MRP2 diffusionreflects a much greater concern with broadcasting andlegitimation of knowledge and information occurringthrough the objectification of knowledge. Whereas theintra-organizational networking at Universal permitteda rich, highly interactive and collaborative process ofknowledge creation that then generated further innova-tion, the inter-organizational networking that tookplace within the structure of the professional associa-

tion network had a more constraining effect oninnovation. Certainly, both the objectification ofknowledge, as well as the legitimation of selectedtechnologies as ‘best’, encouraged more rapid diffu-sion. However, it is also clear that the validating role ofthe professional association network structure was, toan extent, subverted by the networking activity oftechnology suppliers. The latter created a pro-innova-tion bias and biased communication processes infavour of a particular innovation—MRP2—whichactually proved much harder to implement in manyorganizational contexts than it appeared (Clark &Staunton, 1989). During implementation, for examplein LiveCo, local inter- and intra-organizational net-works needed, then, to be mobilized in order to unpackand reconfigure generic solutions into locally applica-ble ones.

The model in Fig. 1 allows the ways in whichknowledge is organized during the innovation processto be compared across different episodes. This gives itsome analytical value that goes beyond purely descrip-tive accounts of process. The model also attempts—perhaps ambitiously—to weave together innovation,networks and processes of knowledge transformation,and so adding to an understanding of process. How-ever, the model—indeed any model—is schematic. Thereal complexity and inherent ‘messiness’ of network-ing activity during innovation is also captured withinthe case examples. Finally, comparing these cases hassome important implications for further work in thisarea. In particular, the model that is outlined here is astylized representation of particular episodes of inno-vation. It does not address certain important issues,notably the interaction between episodes. If innovationepisodes are indeed iterative and recursive as claimed(Wolfe, 1994), then it is also important to understandthe nature of these iterations and their relation tonetworking.

Further research into these areas is certainly calledfor, especially insofar as it develops a networkperspective on the innovation process. In addition, it isalso worth commenting on the roles of consultancyfirms in the transformation of knowledge and innova-tion that emerges from these cases. It seems clear, then,from contrasting the case of Universal with the case ofthe diffusion process, that consultants may play a widevariety of roles, and that their networking activities canact both to expand and constrain innovation. Whenconsidering this final point, a major theme of thischapter is highlighted, that is, the need to recognize thediverse roles played by networks and networking alikein the processes of innovation systems.

AcknowledgmentsThe authors would like to acknowledge Professor SueNewell, Bentley College, Boston, for her contributionto the empirical work and the Economic and Social

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Research Council for supporting the research thatinforms this analysis.

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Managing Innovation in MultitechnologyFirms

Andrea Prencipe

Complex Product Systems Innovation Centre, SPRU, University of Sussex, U.K. and Faculty ofEconomics, University G. D’Annunzio, Pescara, Italy

Abstract: This chapter identifies two major dimensions of capabilities of firms developingmultitechnology products: synchronic systems integration and diachronic systems integration.Within each of these two dimensions, multitechnology firms maintain absorptive capabilities tomonitor and identify technological opportunity from external sources and generative capabilitiesto introduce innovations at the architectural and component levels. The chapter focuses on afirm’s generative capabilities and illustrates that a firm’s generative capabilities enables it to framea particular problem, enact an innovative vision, and solve the problem by developing newmanufacturing techniques. The triad frame-enact-solve is argued to be the primary feature of afirm’s generative capability.

Keywords: Innovation; Multitechnology products; Systems integration capabilities; Generativecapabilities.

IntroductionEarly research on the management of technologicalinnovation underlined that innovation is a complexmulti-actor phenomenon (Rothwell et al., 1974). Inno-vation is understood as the processes thereby new ideasare commercialized (Freeman, 1972, 1984). Not onlydo successful industrial innovations require theinvolvement and co-ordination of all firm’s businessfunctions, from R&D, through engineering and manu-facturing, to marketing, but also the involvement andco-ordination of external organizations to the firm,such as suppliers (Clark & Fujimoto, 1991; Freeman,1991; Nonaka et al., 2003; Rothwell, 1992; VonHippel, 1988). Organizing and managing the innova-tion process, therefore, span both intra- and inter-firmboundaries (Bessant, 2003; Gassmann & von Zedtwitz,2003; Katz, 2003; Swan et al., 2003).

The literature on technology strategy has highlightedthat several industries are increasingly characterized bymultitechnology multicomponent products (Granstrandet al., 1997). Multitechnology multicomponent prod-ucts have important managerial implications since theyintensify the co-ordination efforts for firms developingthem. The number of technologies and components isin fact too large to be managed within the firm’s

organizational boundaries so that the co-ordination ofexternal sources of components and technologiesbecomes paramount for the successful development ofnew products and processes. In other words, the multi-actor nature of the innovation process is exacerbated infirms that develop multitechnology multicomponentproducts because of the increasing number and rele-vance of external organizations, such as suppliers,customers, and universities.

Building upon the emerging literature on multi-technology corporations (Brusoni et al., 2001;Granstrand, 1998; Granstrand et al., 1997; Patel &Pavitt, 1997), this chapter identifies the different typesof capabilities that firms developing multitechnologyproducts are required to develop and maintain. Itproposes a taxonomy that categorizes these capabilitiesinto synchronic systems integration and diachronicsystems integration. Within each category, firms mon-itor external technological developments (absorptivecapabilities) and introduce innovative solutions at boththe component and architectural levels (generativecapabilities). The chapter focuses on a firm’s gen-erative capabilities and argues that generativecapabilities enables firm to frame a particular problem,

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enact an innovative vision, and solve the problem. Thetriad framing-enacting-solving constitutes the primaryfeature of a firm’s generative capability.

The chapter is organized as follows. Based on thetheoretical and empirical literature on multitechnologyfirms and products, the next section introduces twodimensions of capabilities of firm’s developing multi-technology products. This is followed by a section thatfocuses on the firm’s generative capabilities andattempts to disentangle its primary feature. The finalsection presents the conclusions.

Multitechnology Firms and MultitechnologyProducts: The Multiple Roles of Firms’CapabilitiesEmpirical and theoretical studies on firms’ capabilities,although paradoxically in their infancy (given thatPenrose pioneered the resource-based approach in1959), have provided invaluable insights to understandtheir nature (Dosi et al., 2000) and their role as sourceof a firm’s competitive advantage (Grant, 1998). Thissection relies on the resource-based research traditionand its more recent evolution known as the capability-based approach, to single out and discuss the role of afirm’s capabilities in multitechnology settings. In sodoing, it extends the theoretical and empirical researchon multitechnology corporations (Granstrand & Sjö-lander, 1990). Granstrand & Sjölander (1990) observedthat technological diversification was an increasing andprevailing phenomenon among large firms in Europe,Japan and the U.S. and put forward the concept of themultitechnology corporation. A multitechnology cor-poration is a firm that has expanded its technology baseinto several technologies. Following this line ofresearch, Patel & Pavitt (1997) showed that products,and firms developing them, are becoming increasinglymultitechnology. Firms rely on a growing number ofspecialized bodies of scientific and technologicalknowledge to develop products.

The concept of a multitechnology corporation restson the fundamental distinction between products andtechnologies. A product is a physical artefact made upof components that carry out specific functions and relyon specific yet different technologies. A technology isunderstood here as the body of knowledge underlyingthe design, development, and manufacture of theproduct (Brusoni et al., 2001). In this way, the conceptof a multitechnology firm is different from that ofmultiproduct firm, since ‘the development, production,and use of a product usually involve several technolo-gies and each technology can usually be applied inseveral products. Thus the technology-product connec-tion is not ‘one-to-one’ (Granstrand & Sjölander, 1990,p. 36). Also as discussed in Grant & Baden-Fuller(1995) and Pavitt (1998), the distinction betweenproduct and its underlying technologies is fundamentalfor theoretical interpretations of the firm and inparticular for the definition of its boundaries.

The multitechnology nature of products has sig-nificant managerial implications for the firmsproducing them in terms of the technological capabil-ities that are required to be developed, maintained, andnurtured over time. In particular, ‘make or buy’decisions are critical issues since firms do not andcannot develop in-house all the technologies relevantfor product design and manufacturing. Multitech-nology firms must increasingly make use of externalsources of components and technological knowledge,such as suppliers, through the use of the marketor through collaborative agreements, such as jointventures.

In order to take full advantage of collaborativerelations, firms need to be equipped with an adequateand independent set of in-house technological capabil-ities (Mowery, 1983). Granstrand et al. (1997) foundthat large firms develop capabilities over a widernumber of technological fields than those in which theyactually produce, and this number is increasing overtime. In other words, firms retain technological capa-bilities about components whose production is fullyoutsourced. Specifically, Granstrand et al. (1997) drewsome conclusions on outsourcing decisions in multi-technology firms. They distinguished two sets offactors that affect corporate outsourcing decisions: (a)the degree to which the innovation is autonomous orsystemic; and (b) the number of independent suppliersoutside the firm. On these grounds, they proposed atwo-by-two matrix that identifies four cells. Each cellis associated with a different case calling for aparticular degree of internal technological capabilities.Granstrand et al. identified four intermediate corporatepositions between full integration and full disintegra-tion, where each position is characterized by a differenttype of technological capabilities, namely exploratoryresearch capability, applied research capability, sys-tems integration capability, and full design capability.For instance, when the number of external sources islow and the innovation is systemic, then companiesshould maintain a wide range of in-house capabilities,from exploratory and applied research down to produc-tion engineering.

Besides the factors identified by Granstrand et al.,the type of capabilities that multitechnology firmsshould develop may depend on the role and the ensuingimportance that each component plays within a prod-uct. The importance of components within theeconomics of product, and therefore, of a firm variesgreatly according to a number of dimensions, such astheir technical features, performances, and costs. Firmsconceive components’ hierarchies in order to identifywhich are the peripheral and the key components andconsequently adjust their technological capabilities(Prencipe, 2003b).

An interesting approach to analyzing the hierarchicalrole of components in a product has been put forwardby Maïsseu (1995). Considering three variables,

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namely the impact of the component’s cost on the costof the overall system, its influence on the quality ofthe system, and the technological maturity of thecomponent, he proposed a taxonomy, which identifiesfour categories. According to this approach, it ispossible to determine the relative weight of eachcomponent. Thus, components with low impact interms of quality and cost of the end product and whoseunderlying technologies are mature are to be con-sidered to be trivial. Then, there are basic componentswhose cost is relatively high, while their technologieshave reached the maturity stage. Key components arethose whose characteristics heavily influence thequality of the end product and whose technologies areat the initial stage, but which do not affect the cost ofthe system to a great extent. Finally, there are thecritical components. Their influence in terms of costand quality is relatively high and their underlyingtechnologies are at the initial stage.

It is worth stressing that this approach heavilyunderlines the issue that components may evolveacross the hierarchy over time. Technological changeoccurring at different levels of the systems may shiftthe relative hierarchy of components and system-levelcritical problems. As hierarchies are usually constitutedaccording to a series of ‘rules’ valid at a given point intime, they can provide predictions as long as the ‘rules’remain unchanged. Therefore, a hierarchical taxonomyconcerning products made up of many componentsmay be undermined by changes in the underlyingcomponent technologies. Evolution may be endoge-nous in that changes can occur within the system itself,stemming from existing as well as exogenous techno-logical trajectories, that is to say existing technologiescan be replaced by new ones or new technologies canbe added (Prencipe, 1997).

This may well inform firms’ outsourcing decisions.For instance, Pavitt (1998) argued that a critical issuethat companies take (or should take) into account whenoutsourcing components is the rate of change of theunderlying technologies and the ensuing technologicalimbalances (Rosenberg, 1976). When technologiesadvance at different rates then companies should beable to keep pace with them and incorporate changes intheir product and their components’ hierarchies.

Identifying Capabilities in Multitechnology FirmsDrawing on and extending Granstrand et al. (1997), theaim of this section is to single out the diverse roles offirms’ capabilities in multitechnology settings. Firmsproducing multitechnology products develop capabil-ities to generate new products and processes as well asto integrate externally produced components and co-ordinate the development of new technologies. Inmultitechnology settings, therefore, a firm’s capabil-ities are not monolithic entities, and do not perform‘one role only’, rather they are multifaceted andmultipurpose. This is the reason why multitechnology

corporations are important and interesting empiricalsettings in which to study the roles of a firm’scapabilities. Besides R&D, design, and manufacturingcapabilities, therefore, we argue that firms producingmultitechnology products must develop two main typesof capabilities to compete successfully over time(Prencipe, 2003).

Synchronic systems integration refers to the capabil-ities to define the requirements, specify and sourceequipment, materials, and components, which can bedesigned and manufactured either internally or exter-nally, and integrate them into the architectures ofexisting products. These capabilities are developed andmaintained through a deliberate strategy labelledintelligent customership that enables firms to gain abetter understanding of the underlying technologies ofoutsourced components in order to control and inte-grate changes and improvements (Prencipe, 1997).Therefore, synchronic systems integration relates to thecapabilities to manage evolutionary changes in prod-ucts and their underlying technologies through theintroduction of component-level innovations.

Diachronic systems integration refers to the capabil-ities to co-ordinate the development of new andemerging bodies of technological knowledge acrossorganizational boundaries in order to introduce newproduct architectures. Different bodies of technologicalknowledge relevant to the production of a multi-technology product may be characterized by unevenrates of advance. Firms that develop multitechnologyproducts must keep pace with and, more importantly,co-ordinate uneven technological developments toincorporate them into new products and processes(Prencipe, 2004). Also, firms developing multitechnol-ogy products cannot encompass in-house, all therelevant scientific and technological fields. The man-agement of the relationships with and co-ordination ofexternal sources of technologies, such as universities,research laboratories, and suppliers, becomes, there-fore, a central task for multitechnology firms(Lorenzoni & Lipparini, 1999). Therefore, diachronicsystems integration relates to the capabilities requiredto master revolutionary changes in products andtechnologies.

Multitechnology firms are required to develop bothsynchronic and diachronic systems integration capabil-ities to pursue both incremental and discontinuousinnovations and changes in order to compete success-fully. Synchronic and diachronic systems integrationmay well constitute the capabilities of the ambidex-trous organization as identified by Tushman &O’Reilly (1996). Ambidextrous organizations are thosecapable of competing in mature environments throughincremental innovations and in new environmentsthrough discontinuous innovations.

Besides integrating and co-ordinating, multitechnol-ogy firms monitor external technological developments(absorptive capabilities) and introduce innovative solu-

Managing Innovation in Multitechnology FirmsChapter 3

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tions at both the component and architectural levels(generative capabilities). Absorptive capabilities arethose required to monitor, identify, and evaluate newopportunities emerging from general advances inscience and technology. This is close to the concept ofabsorptive capacity as put forward by Cohen &Levinthal (1990). Generative capabilities are thecapabilities to innovate both at the component level andthe architecture level (i.e. new paths of productconfiguration) also independently of external sources.While component-level innovations mostly relate to thesynchronic dimension of systems integration, archi-tectural-level innovations refer to the diachronicsystems integration. Exploratory research programsplay a fundamental role in the introduction of newcomponent technologies as well as new productarchitectures. Absorptive and generative capabilitiespermeate both the synchronic and the diachronicdimension of systems integration (Prencipe, 2004).

The discussion above should be interpreted as apreliminary attempt to categorize the role of capabil-ities of firms developing multitechnology products.The intention is not to defend the boundaries of aspecific category, particularly because there are otherdimensions according to which firms’ capabilities canbe categorized (see, e.g. Granstrand & Sjölander, 1990;Granstrand et al., 1997). The use of the differentcategories is instead designed to draw attention to themultiple roles that capabilities have in the economicsof the development of multitechnology products. Thedifferent roles of capabilities of multitechnology firmsare discussed at length in previous works. For instance,Brusoni & Prencipe (2001) discussed the impact ofmodular design strategy on firm’s capabilities operat-ing in multitechnology settings. The synchronic anddiachronic dimensions of systems integration and theirrelationships with a firm’s corporate strategy arediscussed in Prencipe (2003). Systems integration isscrutinized in relation to typologies of products (e.g.monotechnology versus multitechnology) and rate andstage of development of the underlying technologies inBrusoni et al. (2001). Dosi et al. (2003) provided anevolutionary economics interpretations of a firm’ssystems integration capabilities. Paoli & Prencipe(1999) discussed the role of a firm’s knowledge base inmultitechnology settings. Building on these previousworks, the following section focuses on generativecapabilities and attempts to identify its primaryfeatures.

Generative Capabilities: Primary FeaturesIn the previous section, generative capabilities havebeen defined as the capabilities to introduce innovativetechnological solutions both at the component leveland the architectural level also independently of exter-nal sources. To detail the primary features of gen-erative capabilities we rely on the contributions ofDierickx & Cool (1989) on the cumulative nature of a

firm’s capabilities and Dosi & Marengo (1993) on therole of a firm’s frame of reference.

Dierickx & Cool (1989) put emphasis on thebuilding process that affects the accumulation of afirm’s capabilities. Although they talked about strategicassets, we argue that their argument holds also for afirm’s capabilities. They argued that the commonfeature of a firm’s capabilities is ‘the cumulative resultof adhering to a set of consistent policies over a periodof time. Put differently, strategic asset stocks areaccumulated by choosing appropriate time paths offlows over a period of time . . . while flows can beadjusted instantly, stocks cannot. It takes a consistentpattern of resource flows to accumulate a desiredchange in strategic asset stocks’ (1989, p. 1506,original emphasis).

Dierickx & Cool argued that the process of accumu-lation of stocks is characterized by the interplay of thefollowing properties:

(a) time compression diseconomies, (‘crash’ R&Dprogrammes are often characterized by low effec-tiveness);

(b) asset mass efficiencies (‘success breeds success’);(c) interconnectedness of assets stocks (assets stock

accumulation is influenced by the stock of otherassets);

(d) asset erosion (all asset stocks decay and need to bemaintained); and

(e) causal ambiguity (the process is not deterministicbut it is characterized by stochastic elements).

This distinction between stocks and flows and thefeatures of the building process of firms’ capabilities asproposed by Dierickx & Cool (1989) underlines that afirm’s capabilities must be painstakingly accumulatedover time. Also, the distinction between stocks andflows underlines the relevance of the accumulatedtechnological capabilities both as a basis for furtherdevelopment and the need for them to be continuouslycultivated over time via dedicated investments inexperimentation and personnel. Based on Dierickx &Cool, we argue that a firm’s capabilities need to bebuilt, cumulated, nurtured, and refined over time.Although the stock-flow dynamics captures the relevantfeatures of each type of capability proposed in thetaxonomy, as discussed below it is particularly usefulto better understand the primary features of a firm’sgenerative capabilities.

Dosi & Marengo (1993) argued that a firm’s learningprocesses could not be reduced to mere informationgathering and processing. Unlike Bayesan learningprocesses, where new information is employed toupdate the probability distribution within a fixed andunchanging frame of reference, in the learning proc-esses Dosi & Marengo referred to, the frame ofreference is continuously updated, constructed, eval-uated, and eventually modified. Dosi & Marengo

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maintained that “There are fundamental elements oflearning and innovation that concern much more therepresentation of the environment in which individualsoperate and problem solving rather than simple infor-mation gathering and processing” (1993, p. 160,original emphasis).

Based on this line of reasoning, we argue that afirm’s frame of reference, and more importantly itscontinuous renewal, constitute the distinctive base ofits learning processes. A firm’s frame of reference isbased on its cumulated knowledge and its updating andmodification are the result of continuous learninginvestments. What is fundamental is the dynamic thatcharacterizes the frame of reference and its continuousrenewal. This dynamic is well captured by the stock-flow binomial á la Dierickx & Cool. We propose that agenerative capability hinges on a firm’s frame ofreference (i.e. its capability to frame and identify aproblem and allocate resources to its solution) andproblem solving capability (i.e. its capability todevelop a solution to a problem). Also, we contend thatthe primary features of a firm’s generative capabilitiesare problem framing, vision enacting, and problemsolving. Firms build, update, and renew their frames ofreference within which they enact an innovative visionto solve problems in turn identified by their frame ofreference (their view of the world). The triad framing-enacting-solving is clearly inspired by the work ofWeick (1969, 1985) on enactment and sense-making.

Framing, Enacting, and Solving in Context: AnExample of a Firm’s Generative CapabilitiesThe development of the first- and second-generationwide chord fan blade by Rolls-Royce Aero Enginesdiscussed in Prencipe (2001) constitutes a good case toexplain the deployment and enhancement of a firm’sgenerative capabilities. The in-house technologicalcapabilities accumulated over time by Rolls-Royceconstituted the base of its learning processes and gaveimpetus to virtuous cycles of framing, enacting, andsolving. Notwithstanding the failure of the first attempt(the all-composite wide chord fan blade), Rolls-Royce’s conviction about the enormous advantage ofthe wide chord fan blade supported new investmentsaimed at developing the radically new technology. Dueto the technological knowledge developed over time,Rolls-Royce was able to frame the problem (lowperforming, narrow blade) and enact an innovativetechnological vision (wide chord fan blade).

Borrowing the terms of Dierickx & Cool (1989), theknowledge garnered during the development of thefirst-generation wide chord fan blade representedRolls-Royce’s stock of cumulated technological knowl-edge, which was refined and advanced throughdedicated in-house investments (flows) that led to thedevelopment of the second-generation fan blade. Whilethe stock of in-house technological knowledge cumu-lated during the development of the first generation fan

blade formed the platform for Rolls-Royce’s newtechnological solution, the dedicated investments inexperimentation and personnel enhanced the com-pany’s generative capabilities.

The interrelations between first- and second-genera-tion fan blades also provide empirical support to theinsights of Dierickx & Cool on the features of thebuilding process of a firm’s capabilities. The success ofthe second generation built heavily on the first-generation’s success (asset mass efficiency). AsDierickx & Cool argued “firms who already have animportant stock of R&D know-how are often in a betterposition to make further breakthroughs and add to theirexisting stock of knowledge than firms who have lowinitial levels of know-how” (Dierickx & Cool, 1989,p. 1508).

The second-generation fan blade was not a merepoint extrapolation of the first-generation, however. Itcontained several innovative technological featuresboth at the product level and at the process level. Thefirst-generation wide chord fan blade was innovative atthe time of its introduction. It was, however, bothcomplex and labour intensive in terms of engineeringand manufacturing activities. This called for a funda-mental change in design, analysis, and manufacturingprocesses. Therefore, although the second-generationfan blade development relied heavily on the stock ofpreviously accumulated technological capabilities, thecompany’s generative capabilities from the first gen-eration fan blade were not only deployed, but also werenurtured and enhanced via dedicated investments inexperimentation and personnel. These investmentsgave rise to a new virtuous cycle frame-enact-solve.The cycle started with the reframing of the complexand labour intensive engineering and manufacturingactivities of the first-generation blade which led to newdesign concepts of hollow blades that in turn promptedto laboratory programmes on bonding and formingfabrication processes. Eventually this led to abetter understanding of the manufacturing processes(Prencipe, 2001).

Concluding RemarksThis chapter has identified two main dimensions of thecapabilities firms developing multitechnology prod-ucts. Synchronic systems integration relates to thecapabilities required to specify, buy, and integrateexternally-designed and produced components. Firmsdeveloping multitechnology products are also requiredto develop diachronic systems integration, that is, thecapabilities required for the co-ordination of changeacross different bodies of technological knowledge aswell as across organizational boundaries. Synchronicsystems integration and diachronic systems integrationrefer to the capabilities of the ambidextrous organiza-tion as identified by Tushman & O’Reilly (1996). Thechapter also argued that firms are characterized bywhat Cohen & Levinthal (1990) labelled absorptive


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capabilities related to monitoring, identifying, andevaluating new technologies.

The chapter has then focused on generative capabil-ities needed to innovate both at the component leveland the architectural level, also independently ofexternal sources. It proposed that the primary featuresof a firm’s generative capabilities are problem framing,vision enacting, and problem-solving. Firms build,update, and renew the frames of reference within whichthey enact an innovative vision to solve problems that,in turn, are identified by their frame of reference.Following Dosi & Marengo (1993), we argued thatproblem framing is the distinctive basis for organiza-tional learning processes.

This chapter is an attempt towards a better under-standing of a firm’s capabilities. By considering themultitechnology empirical setting, the chapter pro-posed a taxonomy to categorize a firm’s capabilitiesaccording to their roles. The capabilities needed tostrategically manage the links with a network ofsuppliers are paramount for a firm’s competitiveness ina multitechnology setting (Brusoni et al., 2001;Lorenzoni & Lipparini, 1999). This chapter hasextended this argument and argued that in-housecapabilities play an equally important role in buildingsuch competitive advantage in a high-technologydynamic environment. The firm’s generative capabil-ities, discussed in-depth here, constitute an importantdimension of dynamic capability (Teece & Pisano,1994) that enables a firm to grow (Penrose, 1959).

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Innovation Processes in TransnationalCorporations

Oliver Gassmann1 and Maximilian von Zedtwitz2

1 Institute for Technology Management, University of St. Gallen, Switzerland2 IMD-International Institute for Management Development, Switzerland

Abstract: If innovation is considered as a process, then the differentiation of the innovationprocess into two phases creates several benefits. These two phases are, firstly, a pre-project phasefostering creativity and effectiveness, and a secondly a discipline-focused phase to ensureefficiency of implementation. This differentiation enables transnational companies to replicateand scale innovation efforts more easily in remote locations, exploiting both economies of scaleand scope. Although the characteristics of these phases are quite distinct, few companies haveconsistent and differentiated techniques to manage and lead the overall innovation effort specificto each phase.

Keywords: Innovation; International project management; R&D; Creativity; Phase model.

IntroductionIf innovation is considered as a process, then thedifferentiation of the innovation process into twophases creates several benefits. These two phases are,first, a pre-project ‘cloudy’ phase fostering creativityand effectiveness and, second, a discipline-focused‘component’ phase to ensure efficiency of implementa-tion. This differentiation enables transnationalcompanies to replicate and scale innovation effortsmore easily in remote locations, exploiting botheconomies of scale and scope. Although the character-istics of these phases are quite distinct, few companieshave consistent and differentiated techniques to man-age and lead the overall innovation effort specific toeach phase.

Our overarching goal is to show that dividing theoverall innovation process into the cloudy and compo-nent phases is a simple and easily implementable wayto overcome typical communication and managerialproblems in international innovation. In this chapter wefirst summarize earlier phase models of innovation. Inthis context, we refer to innovation as a company’sefforts in instituting new means of production and/orbringing new products or services to market. Next wedescribe several innovation processes. An innovationprocess is a cumulative sequence of defined stages andactivities leading to an innovation. Recent research,most notably work done in the Minnesota Innovation

Research Program (see Van de Ven, Angle & Poole,1989; Van de Ven, Polley, Garud & Venkataraman,1999), shows that innovation is usually unpredictableand difficult to manage with tight controls. The two-phase model we outline in this chapter allows chaoticand random innovation processes to occur in the earlyinnovation phase, and argues for a narrowing andredirection of the creative energy in the first phaseduring execution and implementation of the initialideas in the second phase. We then describe tools andsystems that help to channel creativity into imple-mentation suited to transnational innovation. Creativityhere is understood to be the ability to produce bothnovel and original ideas appropriate for the task athand. Transnational innovation is innovation thatincludes participants of the innovation process fromgeographically distributed locations, usually in othercountries or time zones. We conclude with someimplications for managing innovation processes intransnational settings.

Linear and Non-Linear Models of InnovationInnovation is an inherently complex and unpredictabletask. Companies that are driven by meeting financialand operational targets as well as strategic objectiveshave invented numerous techniques to capture theuncertainty of innovation into a measurable and hencemanageable framework.

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Pioneering work done by Schumpeter (1911, 1939)and Bush (1945) helped to explain the origin oforganized technology development. Building on thenotion of science push, they described innovation as alinear process from basic research, applied research,and development, through design and manufacturing,to marketing and sales (see Marinova & Phillimore,this volume). Similar models based on a linear logic(e.g. the value-added chain, Porter (1985)) reinforcedthe concept of a sequential innovation process. Scienceand technology programs in many Western countriesare still based on this pipeline model and are often usedto justify the financing of public research and science.This ideology implicitly assumes a causal correlationof research input and innovation output: higher invest-ments in basic research will lead to more innovationand more advanced products.

The linear model has worked well in fields whereimmediate applicability and practicality was not adetermining driver. A well-known example of science-driven innovation is laser technology: the theoreticalfoundation of this technology was built between 1900and 1920 by famous scientists such as Max Planck(quantum effect), Niels Bohr (atomic model) andAlbert Einstein (conventional sources of light emit aspontaneous photon radiation). Scientific research onlaser technology itself took place in the 1950s, andthe first successful laser device was constructed byTheodore Maiman in 1960. Today’s applications arewidespread: cutting, drilling and welding of materials;distance and gas measuring; telecommunications; andmedical technology.

In the 1960s, a new paradigm emerged based on theempirical work of Schmoockler (1966) in patentstatistics. Innovation was found to be determined moreby market pull than by the classical science push. Thismodel also assumes a linear causal innovationsequence, but in this case a market demand is whattriggers innovative activity in the preceding functions.This model enhanced the position of marketing: R&D(research and development) departments and newproduct development teams were assigned reactiveroles to develop products according to given specifica-tions.

The market-driven model has given companies a toolthat aligns internal processes according to measurableoutput, thus greatly increasing the role of R&D as astrategic element in achieving, building and expandingmarket dominance. For instance, AT&T stronglypushed transistor development at Bell Labs becausetelephone companies demanded smaller and moreconvenient switching technologies. Hippel’s (1988)lead user concept, further underscored the importanceof capable users, and customers, as a source ofinnovation. Lead users are technologically savvycustomers with an urgent need for improved productswho could serve as trendsetters in an emerging market.Hilti, a leading construction technology supplier,

applied the lead user concept and easily halved R&Dcosts and time-to-market (Hippel & Herstatt, 1991).

Both the science push and the market pullapproaches are linear sequential models. Only theinitial source of innovation is different. However,several studies since the late 1970s have shown thatinnovation processes are seldom linear processestriggered by a single source—either scientific potentialor market need—but rather random processes that aremore complex and uncertain than the linear modelassumes (e.g. Cohen, March & Olsen, 1972; Tushman& Anderson, 1986).

Van de Ven and colleagues (1989, 1999) haveundertaken several longitudinal studies consisting ofthousands of single observations, and assembled ampleand rich evidence that innovation is inherently achaotic process. The description of 3M’s CochlearHearing Implant innovation journey (detailing morethan one thousand events of the 3M innovation effort,see Van de Ven et al., 1999) is well documented andunderlines how difficult it is to predict consequencesfrom decisions to actions to outcomes.

Managers and organizations, however, assume acertain degree of predictability and cause-and-effectrelationships in innovation and often introduce struc-tured management schemes to increase the stability andcoherence of their efforts. Linear models wereimproved by integrating science push and market pullinto non-sequential feedback models. Regardless of thetrigger of innovation, several complete feedback loopsensure that both science and market inputs arerecognized and implemented. For instance, Roy (1986)described innovation as a cyclic process in whichtechnological opportunities, invention, knowledge pro-duction and market demands were linked together.Later, Kline (1985) and Kline & Rosenberg (1986)introduced the chain-linked model. This modeldescribes five paths of innovations. Some of thesepaths are linear and follow the invention to develop-ment to production to marketing sequence, while otherpaths are based on several feedback loops, i.e.reiteration to early-stage innovative activity. A majorimplication of their model is that the market remains asignificant driver of innovation and that science-driveninnovation is relatively rare, yet should not be totallyneglected.

In the 1980s, several integrative approaches to R&Dmanagement pioneered by Japanese companies (e.g.the ‘rugby’-approach, which advocates a team ratherthan a relay approach to product development) becamepopular under the umbrella of simultaneous or con-current engineering. These approaches focused onoverlapping innovation sub-phases, mainly in productdevelopment and manufacturing (see e.g. Liker,Kamath, Wasti & Nagamachi, 1995; Nishiguchi, 1996).Based on these interlaced models of innovation, i.e.innovation processes with overlapping sub-phases,interaction models were developed that emphasized the

Innovation Processes in Transnational CorporationsChapter 4

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principle of interaction itself as an important source ofinnovation (see e.g. Durand, 1992; Schmoch, Hinze,Jäckel, Kirsch, Meyer-Krahmer & Münt, 1995). Theyexplain innovation as the result of intense, continuousinteraction of both individual and institutional protago-nists, and communication becomes a key factor ininnovation processes. Nonaka & Takeuchi (1995), whointroduced the rugby-approach of R&D managementnoted above, focus on knowledge creation and sharingas the central determinants of corporate success, andconsider innovation almost as a byproduct of knowl-edge management (see also Nonaka & Ahmed, 2003).

Over the past decades, compliance with ISO require-ments has led to highly disintegrated and ineffectiveR&D phase concepts and the illusion that all criticalinnovation factors can be measured and structured. Atthe same time, engineers and developers haverequested more creative freedom and fewer admin-istrative chores, particularly in the early phases ofinnovation. Although it is now accepted that innovationprocesses are non-linear, managers need normativemodels that reflect the need for clear, unified processesthroughout the organization. In the following sectionwe compare process models that attempt to combineboth linear and non-linear approaches to innovation.

Normative Models of Innovation ProcessesIn R&D management practice it is very rare to findclearly distinguishable and predetermined projectphases executed exactly according to a predefinedschedule. Although systems engineering offers somehelp in structuring the R&D process into linearsequential project phases, R&D managers are generallynot successful in implementing these methods in theinnovation process. However, there are some frame-works that guide the design of innovation processes ina company.

Classical phase segmentation and process orienta-tion (as in modern management theory) have beencombined in the stage-gate process (see Cooper, thisvolume; Cooper & Kleinschmidt, 1991; O’Connor,1994; for transnational innovation: Gassmann, 1997).Every step—or ‘stage’—necessary to complete aparticular project task is linked to the next by a ‘gate’at which decisions for the continuation of the projectare made. Unlike milestones, gates are more flexible interms of time, date and content. Gates allow adeliberate parallelization of phases as well as theirrecombination or adaptation to new requirements. Ateach gate the R&D project is analyzed and reviewed inits entirety, often including some competitor intelli-gence (i.e. evaluation of similar R&D projects bycompeting firms), as well as external market andtechnology developments. The number of stages andgates needs to be adapted to industry and projectrequirements. Ex-ante agreements serve as guidelinesfor the collaboration of project participants.

The loose-tight concept also plays a central role inthe design of R&D processes (e.g. Albers & Eggers,1991; Wilson, 1966). According to this concept, thesuccess of the project depends on the degree oforganization during the R&D process. In the earlystage of a project, the organization should be designedloosely; towards the conclusion of the project, it shouldbecome more and more rigid and tight. The varyingdegrees of R&D project organization are imposed byconstraints in time: Although creativity and ideageneration are highly important in the early stage, themanagement concern shifts to efficiency and projectimplementation on schedule in the later stage.

The stage-gate process is successful in areas andindustries dominated by market-pull innovation. Fur-ther indications for applying the stage-gate process areinnovations in existing markets (e.g. transfer of productdevelopment competence); new applications of exist-ing products and services (e.g. relaunch of an adaptedproduct in a new market); high costs of productdevelopment and market introduction (e.g. initialproduct releases); and limited uncertainty in terms ofexpected innovation (e.g. incremental innovation).Most of today’s industries and well-managed R&Dprocesses rely on the stage-gate process to someextent.

In industries or projects where the science ortechnology push is the dominant driver of innovation,stage-gate processes are too rigid and slow. Innovationsthat are triggered by a technological invention withunknown market potential need different processes andtechniques to succeed. Under these circumstances, theprobe-and-learn process is more appropriate. Thisprocess has been described from a number of angles,including marketing and discontinuous innovations(e.g. Lynn, Morone & Paulsen, 1996), product develop-ment and experimentation (e.g. Thomke, 1995;Wheelwright & Clark, 1992) and technology strategy(e.g. Iansiti, 1998).

Traditional market research methods are based onthe ‘law of big numbers’—the more customers whowant a new feature or product, the more valuable it is.These methods often do not work in technology-driveninnovation, as target markets do not yet exist. Moreanticipatory and exploratory market research methodsare needed, such as scenario techniques (pioneered byRoyal Dutch/Shell, see Shoemaker, 1995), Delphistudies (see Best, 1974; Dalkey & Helmer, 1963),Beta-customer test groups (see Kottler, 2000) and leaduser workshops (Hippel, 1986, 1988). Successfulexamples of this kind of innovation are 3M’s Post-it,Corning’s optical fiber technology, Netscape’s Nav-igator and Schindler’s LiftLoc system.

Many innovation projects in New Economy com-panies (i.e. those with mostly Internet-based productsand services) have been characterized by a high degreeof uncertainty in terms of market fit (see e.g. Trott &Veryzer, 2003). An example of an e-innovation that

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Oliver Gassmann and Maximilian von Zedtwitz Part IX

followed the probe-and-learn process is ICQ (pro-nounced ‘I seek you’). The term was coined by fouravid young computer users—who established Mir-abilis, a new Internet company, in July 1996 in TelAviv, Israel—to describe a new way of communicatingover the Internet. Although millions of people wereconnected to one huge worldwide network—the Inter-net—they recognized that these people were not trulyinterconnected. The missing link was a technology thatwould enable Internet users to locate each other onlineon the Internet and create peer-to-peer communicationchannels in an easy and simple manner.

They developed a crude prototype first, and offered itfree of charge on the Internet in November 1996. Stilla very sketchy product, it was full of flaws and lackedimportant functionalities. However, based on onlinefeedback from users and rapid prototyping techniques,they continued to fine-tune the first version. Threemonths after the launch, ICQ customer base reached350,000 users; after an additional three months thisnumber stood at 850,000. Even at this stage the productwas continually refined and adapted to new user needs(e.g. the introduction of a ‘I am busy’ state in order toprevent communication bottlenecks). However, therewas no clear product strategy.

Fourteen months after first introducing the product,Mirabilis had 8 million subscribers and handled 1.3million users a day. Although the company operatedwith heavy losses, the market value increased inexpectation of even higher subscription numbers.Eventually, AOL acquired Mirabilis in mid-1998 for$287 million in cash and renamed it ICQ. ByNovember 2001, ICQ had 120 million users.

ICQ is perhaps a forerunner of product developmentin an increasingly international and online-basedworld. Two observations can be made:

Even established industries develop products withmore and more online content. In addition, simulation,virtual reality and communication tools based onbroadband information and communication technolo-gies (ICT) allow research, development and design tointegrate users, scientists and engineers in virtual teamsaround the world. Greater density of information andgreater geographical dispersion are two importantfactors in the design of modern innovation processes.

Generally, there is only a vague idea about theeventual product design at the start of a developmentproject. Project members differ greatly in their under-standing of project objectives and methodologies. Bycommunicating their ideas during the conception of theproject, project members create shared knowledge andunderstanding. In the early phases of a project, tacit orimplicit knowledge is transformed into explicit knowl-edge. But designing and generating product designdrafts and specification lists must be done and decidedon as a team. Knowledge sharing and know-howtransfer is hampered not only by geographical separa-tion but also by epistemological and cultural barriers.

Stage-gate, loose-tight and probe-and-learn proc-esses were developed when most R&D was carried outin one location by one team. However, the typicaldevelopment team at the beginning of the 21st centuryis becoming transnational in nature (Boutellier, Gass-mann & Zedtwitz, 2000; Gassmann & Zedtwitz, 1997).Probe-and-learn processes also appear to work well inweb-based settings and can thus be transposed todispersed team settings. Nevertheless, distance—andthus problems of different time zones—and cultureimpose barriers and further imperfections on theinnovation process (see e.g. Hadjimanoli, 2003).Would it be possible to combine some of these processmodels and adapt the result to a truly transnationalinnovation process framework?

Cloudy-to-Component Process for ModularInnovations in Multinational Companies

What is the Cloudy-to-Component Process?Companies that undertake more than just applicationengineering and engage in fully fledged R&D will needto split their innovation process into two phases: the‘cloudy’ phase and the ‘component’ phase. Moredifferentiated phase concepts are commonly acceptedand applied in industrial R&D, but they suffer from thestrictly sequential execution of project phases and aretherefore often impractical in transnational innovationprojects. The highly structured stage-gate process caneasily become bogged down in bureaucracy andrigidity; the probe-and-learn process can lead tounplanned trial-and-error development and unpro-ductive chaos.

Our concept of the cloudy-to-component process(C-to-C process, see Gassmann, 1997) is especiallyappropriate for innovation processes in transnationalcompanies. Remember that existing innovation pro-cesses have been fine-tuned for collocated innovation.Due to the increased internationalization of R&D andknowledge creation, it has become more difficult andrewarding at the same time to optimize global productdevelopment and integrate distributed competencies(Gassmann & Zedtwitz, 1999a).

Our C-to-C process does not imply that projects arecarried out without reviews or milestones. Suchprojects tend to be managed both ineffectively andinefficiently. The solution lies in placing the appro-priate focus on what is to be achieved in the twophases. Too many projects are slowed down orcanceled because faulty designs have to be correctedlate in a project, and too many projects have notachieved their full potential because project managershave pushed for cost efficiency and short-term solu-tions too early. In transnational innovation projects,there is less slack to compensate for these managementerrors. At the same time, they offer great potential for‘doing it right the first time’. This separation into twophases must be well planned beforehand and must be


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communicated to, and accepted by, all involved parties.The ‘cloudy’ phase is thus reserved for wild, inventivecreativity, and every idea is given a chance. Only whenthe project proposal is finally approved does the cost-intensive component phase set in with structuredengineering methods (Fig. 1).

Inputs to the Cloudy PhaseThe first phase—the ‘cloudy’ phase—is the domain ofcreative idea generation, research and advanced devel-

opment. Freedom of thought and an open playing fieldfor engineers should be ensured. During the cloudyphase the principal product features are conceived, themain system characteristics defined, and the project isinitiated. This early phase is based on market andtechnology research, as well as on internal problempressures.

Market exploration in this phase is based ontraditional market research tools such as panelresearch, focus group interviews, sales and distribution

Figure 1. The separation of the R&D process into two phases allows a focus on effectiveness and efficiency in the appropriatestages.

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questionnaires, scenario techniques, lead market analy-sis, etc. More recent techniques include cooperativeforms of R&D such as ‘lead users’ and ‘anthropo-logical expeditions’, both of which help to tap intoimplicit user knowledge (see Leonard-Barton, 1995).Technology screening and assessment also take avariety of forms. Technology listening posts, leading-edge innovation centers, technology intelligence andtechnology forecasts, expert interviews, patent data-base research, and reverse engineering of competitorproducts are typical techniques and sources used here.Exploration of markets and technologies has to beconducted on a global scale, as the sources of technicalknowledge are less and less limited to a few regions ofinnovation and markets are becoming increasinglyinternational in nature.

Needs exploration and technology screening are thetwo primary sources of good project ideas. Ideally,project ideas result from a balance between market pulland technology push. Dominance of technology-focused engineers would lead to over-engineeredproducts that would not be accepted by the customer.Conversely, short-term profit considerations of salesand marketing people with no technological visionwould reduce the long-term innovation capacity of thecompany (striking the right balance is one of thefundamental problems of innovation management). Inthe early cloudy phase of a project, it is essential toallow creative input to come from all possible direc-tions.

As well as being influenced by technology andmarket determinants, the generation of project ideascan be highly affected by location-specific problemsand pressures such as low capacity utilization, financialdifficulties and fashion trends. Low capacity utilizationin a particular site (e.g. due to relocation of manu-facturing to another site) will urge local management tosearch for new businesses. Units with negative finan-cial results and low cash flows are under more pressureto change than units with profitable products. We alsofound above average creativity and a propensity toinitiate projects in R&D units that were in danger ofbeing rationalized due to global efficiency enhance-ment measures. If management does not succeed incommunicating a clear framework and a commonvision, the imminent crisis is worsened by the growingparalysis of the work force. For instance, the significantdeparture of qualified personnel at DASA (a Germanaerospace company) in 1995 was related to the longuncertainty about goals as well as changes in theleadership of MTU (a German jet engine and powerplant company and close business partner of DASA).Unlike ABB, which experienced a ‘creative crisis’ inits GT-24/26 radical innovation project (see e.g.Imwinkelried, 1995; Zedtwitz & Gassmann, 2002), andIBM, which experienced such a crisis in its VSE(virtual storage extended) development project (see e.g.Gassmann, 1997), MTU was characterized by paralysis

that resulted in a reduction of idea generation andinnovation.

Fashion trends often seduce managers into enlargingtheir product spectrum with the latest and most refinedproducts in the market. Many R&D project are thusinitiated not because of a clear market need ortechnological potential but rather to improve the imageor reputation of a particular business unit. Besidescompany-external market pull (e.g. request for a newproduct) and technology push (e.g. exploitation of atechnological capability), a major driver of new projectideas is therefore a company-internal problem push(e.g. justification of previous market investments andproduct commitments). The two external drivers pre-vail in a global environment, whereas the internaldriver is local.

Examples of the C-to-C Process in Industry

The innovation process in the chemical and pharma-ceutical industries is two stage and models very muchlike our C-to-C process. BASF underscores the distinc-tion between cloudy and component phases byspeaking of ‘R&D activities’ in the early R&D stagesand of ‘R&D projects’ in later stages. For Bayer,milestones and project review meetings only start oncethe preclinical phase has been reached, when theproject is started formally (Fig. 2).

General Motors calls this early phase of innovationthe ‘bubble-up process’. This process is driven by aninterdisciplinary team, representing advanced develop-ment, strategic purchasing and advanced marketing.Most of the activities focus on strategy developmentand exploration of markets, brands and technologies.At Schindler, the owner of the cloudy phase is a unitcalled the Technology Management Area whose tech-nological experts, representatives of innovationmarketing and lead users jointly develop so-calledconcept elevators. These functional prototypes showtechnical feasibility and market acceptance; they alsodefine the principal product architecture and technol-ogy to be used. The individual components will later bedeveloped and fully documented by the developmentcenter.

The distinction between cloudy and componentphases is therefore not an academic one, but a very realone. Companies succeeding at transnational innovationmanage each phase differently and optimize thedeployment and utilization of specific organizationaland management techniques.

Intralocal Versus Interlocal Execution in the C-to-CPhases

There is substantial research indicating that innovationis spurred by geographical proximity between R&Dand other R&D units, suppliers and customers (e.g.Allen, 1971; Hippel, 1988; Tushman, 1979). AsTushman (1979) noted, the patterns and intensity of


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communication differ remarkably between early crea-tive R&D and later-stage development work.

R&D in the early cloudy phase is a contact sport.Many tools based on modern communication technol-ogy and advances in virtual engineering may allowpeople to collaborate more productively at a distance,but the trip to the coffee corner or across the hallway toa trusted colleague is still the most reliable andeffective way to review and revise a new idea.Moreover, the internal sociopolitical game of findingand convincing an idea champion as well as building acore team is based on frequent and face-to-faceencounters. The early cloudy phase is therefore heavilyintralocal.

However, the stimuli for new ideas and projects—customer needs, technology potentials and perform-ance pressures—can have very global origins. Scien-tists maneuver in an international scientific communityin which the locus of the individual is irrelevant.Gatekeepers are active listeners and transferors ofoutside ideas to the internal R&D organization (Allen& Cohen, 1969). The potential of outside transparencyis of course curtailed by the effects of the not-invented-here syndrome (see e.g. Katz & Allen, 1982), whichstill governs many contemporary R&D organizations.

Once the product or system architecture has been(locally) conceived, and most of the interdependenciesbetween different parts of the final product have beendefined and described, the actual R&D work can beseparated and assigned to specialized and betterprepared R&D units. Some research may still have to

be carried out with respect to the underlying propertiesand improvements of individual system components,but these should not affect the system as a whole.Coordination and communication about the system isnow the task of the overall project management team,which controls and directs the innovation effortthrough interface coordination, travel and regularproject reviews. The integration of local customers inthe innovation process, and the restricted availability ofcritical engineering and testing resources, require thedispersion of project activities, making the componentphase part of interlocal innovation (Gassmann &Zedtwitz, 1999b).

Building Blocks for Improving the ‘Cloudy’ Phasein Transnational Innovation

Intensive Idea Flow and Workflow Systems

Although creativity flourishes over shorter distances,recent advances in collaborative workflow systems (seee.g. Carmel, 1999) allow the idea generation for asingle project to take place on a global scale. This wasdemonstrated by ABB’s workflow system PIPE (Pro-ject Idea, Planning & Execution), a Lotus Notes-basedworkflow system designed to transmit and distributeideas, problems, commentaries and solutions by meansof modern information and communication technolo-gies. The generator of an idea also selected the groupof persons who could access his contributions. Hisinitial idea, along with his evaluation of commercial

Figure 2. Bayer: The first milestones start at preclinical development.

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potential and supplementary comments, was thenrefined and complemented with the ideas and sugges-tions of other PIPE participants.

If an idea received enough support, a formal projectwas proposed, for which detailed information aboutobjectives, risk, possible problems and availableresources was required. Upon approval of the projectproposal, the program manager transferred this infor-mation into the PIPE Planning Application. The projectidea was then integrated into the overall project plan. Aproject manager was assigned and a decision was takenon what the participating sites would contribute. Localgroup managers proposed local project schedulesdefining sub-goals, costs and means of funding. Theprogram manager, local corporate research managersand business unit representatives then evaluated theconsolidated project plan, contributing their prioritiesby e-mail.

PIPE also supported project execution. Simple andformalized project reports concerning costs, schedulesand results served as easy-to-distribute project infor-mation. A report archive logged the project history,thus facilitating exchange of experiences across severalprojects.

Interestingly, after some years of experience withPIPE, ABB decided to restrict the freedom of ideageneration and commenting with this workflow system.This decision was motivated by the frequent uncertain-ties over ownership of shared ideas and inventions. Aslong as reward and compensation systems are tied tothe extent of measurable technological contribution,trust and confidence remain significant determinants ofeffective transnational idea generation (see De Meyer& Mizushima, 1989).

Good Ideas Require Good PromotionAlthough stimulated by global determinants, identify-ing a problem usually starts with a single person or acollocated group of people. Looking for support fortheir ideas, they try to convince influential people intheir organization about the significance of theirinsights (see e.g. Hauschildt, 2003; Roberts, 1968;Witte, 1973). The influence of these idea champions isbased on their hierarchical position (power promoters),their knowledge (functional promoters) or their com-munication abilities (process promoters).

With the omnipresence of e-mail and global commu-nication networks, one may be tempted to look forappropriate promoters regardless of their location.Experience shows, though, that personal relations aretremendously important in winning over decision-makers to new ideas. These personal relations aredifficult to establish just for the purpose of champion-ing a project idea, particularly at internationaldistances. Decision-makers are influenced by projectopponents, who bring in technological and economicalarguments against a new project idea. Internal politicalarguments play an important role, since opponents fear

that new projects may mean a reduction in resourcesfor their own activities.

The better the idea generator is able to communicatehis intentions and visions, the more likely he is tosucceed in finding top-management support. In order tofind a power promoter, a project idea must be fresh andpresented very soon after conception. Commercializa-tion potential and project vision are often moreimportant than technical decision criteria. For as longas it remains difficult to inspire people just by means ofe-mail and shared workflow systems and convincethem to support an idea, the quest for promoters willremain a matter of face-to-face contact.

Bottleneck Product Profitability CalculationIf a project idea finds enough support and passespreliminary evaluations, a formal project proposaldemonstrating technical realization and commerciali-zation potentials is made. Product profitabilitycalculations are widely used as they are robust, but theyare not always appropriate for project evaluation. Forinstance, if a market has not yet developed a dominantdesign of product architecture, project evaluation canlead to unrealistic market forecasts (Fig. 3).

The profitability calculation is based on forecastedmarket returns discounted to net present value. Beforethe emergence of a dominant design (e.g. mobilecommunication), a market is characterized by intensemarket dynamics, making sales forecasts highly unreli-able. Numerical values with many decimal placesprovide an illusion of only hypocritical exactness.Future project proposals should be complemented withqualitative data and evaluated in light of technologicalvision.

More recently, some companies have startedexperimenting with real-option analysis to evaluateR&D projects (see e.g. the special issue of the R&DManagement Journal, 31 (2), 2001, on real-options inR&D). For R&D projects that allow multi-periodinvestment decisions, real-option analysis is better thanthe popular net present value approach. NPV underval-ues potential projects by as much as several hundred%because it ignores the value of flexibility. Real optionsinclude the flexibility to expand, contract, extend ordefer R&D projects in response to unforeseen eventsduring the innovation phase. Managers often overruleNPV results by accepting projects with low or negativeNPV for ‘strategic reasons’. In essence, they are usingtheir intuition to account for the flexibility of aproject’s real options (see Copeland, 2001). Real-options reasoning offers a way to capture the value ofproject portfolios, research programs, technologicaland innovation competence, and technology and prod-uct vision.

Project Approval: Rational Criteria Versus PoliticsAfter what the idea promoters consider sufficientconceptualization and refinement, the project proposal


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is presented to a steering committee. This committee isnot necessarily located in the same R&D unit where theproject will eventually be carried out. For instance, the‘Investment Review Board’ of the IBM S/390 systemarchitecture has been meeting in New York to decideabout major project activities in the IBM Germany siteat Böblingen. Transnational R&D projects requireparticularly large budgets, which must be approved bythe highest authorities of business areas. Projectselection always takes place at the location of thedecision-maker.

Project approval also includes a decision about keyproject members and participating locations. It is at thisstage that it is determined whether an R&D project willbe carried out transnationally or in only one place. Inour experience, this decision is rarely based on astructured top-down evaluation, during which projectrequirements would be systematically combined withcompetencies, know-how bases, and available capaci-ties of potential R&D units. As the IBM VSEdevelopment example demonstrated, project participa-tion was determined in a political agreement finding

process (Fig. 4). Often enough, political considerationsoutweigh rational criteria.

Profit-center thinking usually gains the upper hand,despite the fact that resource and competency-baseddecisions would be economically more reasonablefrom a corporate perspective. Each R&D site strives forfull capacity utilization, and projects funded byheadquarters or central R&D unit are particularlyattractive. Examples of such centrally funded strategicprojects are ‘Top Projects’ at Bosch, ‘Golden BadgeSpecial Projects’ at Sharp, ‘Core R&D’ and ‘StrategicBusiness Projects’ at Hitachi, and ‘Core Projects’ atSiemens and NEC.

System Architecture as a Critical Success FactorConcept finding and definition, which determine thearchitecture of the system to be developed, partlycoincide with the initial goal finding process. Espe-cially during the subsequent interlocal componentdevelopment phase, an accepted system architecture isone of the critical success factors of the entire project.Interfaces between modules and components must be

Figure 3. Product profitability and return-on-investment are inappropriate when the market is dynamic and unpredictable.

Source: Boutellier et al. (2000, p. 176).

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clearly defined. Changes in one module may not affectother modules. The stability achieved through such asystem architecture reduces the number of designchanges in later stages and consequently the intensityof interaction between decentralized teams (see e.g.Morelli, Eppinger & Gulati, 1995). This stability alsomakes standardized reporting possible within the sameproject.

The system architecture defines not only the successof the current product but also the success of futureproducts and perhaps an entire product line. Only ifinterfaces have been designed clearly and with a widerange of applications in sight can this architecture serveas the basis of a product platform in future modularproduct development. A reduction in variant complex-ity (which is an essential part of cost reductionprograms in all industries) is supported by a clearconception and designed stability in system archi-tectures during the development phase.

System Management to Speed Up the ComponentPhase in Transnational Innovation

Goal Conflicts in Teams Jeopardize Project SuccessThe assignment of teams and locations to the R&Dproject is the linking step between the cloudy andcomponent phases of innovation. Although much of theknowledge and goals of the cloudy phase may havebeen tacit and not necessarily well articulated, distrib-uted teams will only be able to work with explicit andeasily transferable information.

Although a system concept may have been definedand approved, these local teams may differ sig-nificantly in their interpretation and realization of theoverall objective. In transnational innovation, theprocess of goal alignment is highly complex, sincediffering ideas about goals are not easily resolvedbecause of geographical distances and cultural differ-ences. Goal conflicts may occur between R&D(technologically advanced products), production (man-ufacturable products), marketing (customer-orientedproducts) and logistics (storable and transportableproducts).

In addition to these classical goal conflicts, regionalperspectives may complicate the situation. Representa-tives of product business areas favor globalstandardization, whereas regional managers requestcountry-specific product variants. Each location tries tojustify the importance of its respective product variantwith forecasts about how much this variant wouldcontribute to overall product turnover.

Furthermore, every location strives for developmentand manufacturing share, partly to utilize as much ofits R&D and production capacity as possible, andpartly to develop new competencies in interestingtechnology areas. The various expectations of partici-pating interest groups lead to minimal consent in thegoal definition, and the participants eventually agreeonly to a rudimentary core concept (also known as‘concept peeling’). The overall project managementteam must possess excellent moderation and negotia-tion skills to align and focus all teams on the optimal

Figure 4. Rational criteria and balancing of interest determine who will eventually participate in a project.

Source: Boutellier et al. (2000, p. 177).


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integrated solution, and to motivate and inspire thevarious participants for a commonly shared goal.

Know-How Redundancy and the Need for GeneralistsThe stability of a system architecture is made possiblethrough the input of all members of the core team inconsultation with invited experts. Much of the under-lying knowledge is tacit or implicit, i.e. it resides in theheads of key project members and is not easilyaccessible or transferable (see e.g. Nonaka et al.,2003). During the project conception, socializationprocesses are therefore highly important to exchangecrucial but otherwise unattainable knowledge. Con-structing a common knowledge base goes in parallelwith establishing redundant knowledge within projectteams to improve project-internal communication.

It is possible to support both a generalist and aspecialist focus. The turbine manufacturer MTUMunich introduced ABC teams and thus separatedindividuals who preferred to avoid working in teamsfrom team-enthusiastic project members. The A-teamis organized in the management level and defines thestrategic framework for project teams (program deci-sions and reviews of critical milestones). B-teams carryout much of the component and parts development,while C-teams consist of highly qualified specialists(e.g. for rotor blade materials). C-team members setfunctional guidelines and are consulted by B-teamswhen specific problems occur.

Defining technical interfaces is also characterized bya socio-psychological effect. Each team manager triesto enforce high tolerance levels in the interfaces of hismodule, since this increases the likelihood of success-ful development of his module. This behavior triggersa cascading effect of tolerance-determined loss ofoperational range for components and products. Hightolerances imply higher costs at lower effectiveness.The overall project manager thus has to ensure thatsafety thinking and risk aversion do not lead toexcessively high tolerances.

Structured Engineering in the Component PhaseThe project manager must be a competent systemarchitect himself to implement the highly modularapproach to the project successfully. He groups thefunctional elements of the system into components,defines clear interface standards and protocols, andassigns development tasks to specialist teams. Whenthe system is being divided into individual work tasks,particularly high-risk tasks should not be distributedamong a large number of distinct modules but arebetter concentrated in one component. This criticalmodule is then tackled by a highly qualified R&D unit,preferably with superior management capacity. Riskconcentration is not easy to achieve, since riskdistribution is an integral part of portfolio thinking andteam managers will attempt to shift risk to otherlocations.

Most of the development, prototyping, testing andeventually commercialization of the product takesplace in the component phase. These activities aremore detailed, structured and less interdependent thanactivities in the cloudy phase. Whereas the emphasis inthe cloudy phase is on goal-adequate concept genera-tion, the focus in the component phase shifts toefficient concept realization. Since the cost-intensivecomponent phase consumes most of the projectresources, it requires resource- and time-consciousproject management. Capacity planning and multi-project management become important.

In order to ensure access to critical resources duringthe component phase, it is necessary for the projectmanager to report to a steering committee at the highestlevel, e.g. the executive board or directly below.Typical members of this committee are directors ofbusiness areas, R&D, marketing, manufacturing andregional areas. Such a heavyweight steering committeeincreases the likelihood of successful commercializa-tion of the project outcome.

At ABB, the strategically important ‘CommonTechnology’ projects were managed directly by thebusiness area director for power transmission. Onlywhen the project had got off to a good start and waswell established would this director hand the projectmanagement over to a lower-ranked manager. Since thesteering committee was not capable of controlling theentire project because it lacked the required specialistknowledge, component-specific sub-committees wereformed to evaluate the project activities. Typically,expert knowledge serves as a foundation for sounddecisions in steering committees as well as in projectmanagement.

Conclusions: Success Factors for ManagingTransnational Innovation ProcessesModern transnational innovation has come a long wayfrom the unmanaged, almost haphazard, exploitation ofresearch in the early decades of the 20th century. Thereare no illusions about the (un-)predictability of sequen-tial logic in linear R&D models.

By dividing the innovation process into two phases,R&D processes can be adequately designed andmanaged in each transnational innovation phase. Thisseparation improves process transparency considerablyand reduces the cost-intensive development phasesignificantly. Although the characteristics of the twophases are different, too few companies approach themwith distinctive management methods. The creativecloudy phase requires soft management methods,ensuring freedom, flexibility and inventiveness ofscientists and engineers. In this phase, tacit knowledgeis transformed into explicit knowledge and communi-cated to other members of the team. In order to keep upintrinsic motivation, it is important to create teamspirit, a common project culture and a shared under-

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standing of project goals and the underlying systemarchitecture.

In the component phase of the project, the focusshifts to efficient implementation of these goals. Costsand milestones are used to determine the progress ofthe project. Compared with the cloudy phase, newcoordination and control mechanisms are used tocomplete the project successfully. The componentphase then leads into another, often globally executed,market introduction.

These R&D project phases allow a different degreeof integration of international contributors to andparticipants in the innovation process. It is important tofind the right form of organization for each phase andproject. Critical success factors must be consideredwell in advance to ensure that upcoming problems aretaken care of as they occur.

The interlocal component phase can be executedmore successfully if enough emphasis has been placedon an effective creative cloudy phase, since it is herethat the basis for the subsequent cost-intensive devel-opment stages is defined. Only in the component phaseshould the focus of innovation shift from effectivenessto measurable efficiency.

Idea generation in the early phase should besupported by modern computer technologies andsoftware products. New tools and software packagesare introduced regularly, and it is the responsibility ofgood innovation managers to back up the idea-findingstages of their engineers with the latest support tools.‘Computers’ and ‘creativity’ are not a contradiction.

Good ideas must be communicated, quickly eval-uated and promoted. Potential promoters must beenlisted for new ideas early in the R&D project.

In order for a project to pass from the cloudy phaseto the component phase, traditional product profitabil-ity calculations must be complemented by alternativeassessment models and qualitative criteria such ascompetence establishment and product visions.

Political power struggles should not be allowed toaffect operative project work. A strong steering com-mittee clears the way for project managers.

The actual innovators in an innovation project arerarely team-eschewing specialists, although their inputis critical for project success. Separating projectmembers into different teams (ABC-teams) can neu-tralize this conflict.

During the component phase, highly structuredengineering is required. Measurement criteria such ason-time-delivery or first-pass-yields used in manu-facturing could be helpful.

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An Analysis of Research and InnovativeActivities of Universities in the United States

Yukio Miyata

Department of Economics, Osaka Prefecture University, Japan

Abstract: University-Industry collaboration in the United States has been revitalized in the lasttwo decades, but research at universities is still primarily federally funded basic research—apattern established after World War II. While universities actively obtaining patents, they cannotfinance their research budget through licensing revenues alone. This chapter argues that highquality of research has to be supported by the federal government, and that the fruits of universityresearch on regional economy takes a long time to be realized.

Keywords: Innovation; Innovative activities; Research; University-industry collaboration; UnitedStates; Technological change.

IntroductionUniversities have been involved in promoting scientificknowledge (seeking the truth) and educating people.This chapter analyzes research and innovative activitiesof universities in the United States, focusing on theuniversity-industry collaboration in research. Accord-ing to Schmpeter (1934, p. 66), innovations are: (1) anintroduction of new products (or products withimproved quality); (2) new method of production; (3)new markets and distributing channels; (4) new sourcesof supply of inputs; and (5) new organizations of anindustry. Although the role of business faculty ofuniversities in helping to create new markets, newdistribution channels, or new organizations should notbe denied, this chapter focuses on how science andengineering knowledge, as well as personnel ofuniversities contributes to creating new products andprocesses.

As innovations do not stop with application oracquisition of patents but include a necessary commer-cialization process, private businesses are the agents ofinnovation. Universities, however, which supplyresearch personnel and scientific/technological knowl-edge, are an important component of the nationalinnovation system. Understanding the way in whichU.S. universities and industry relate is an importantstep in promoting innovation. This collaboration is nowthe envy of the world, and an analysis of its actualpractice has important policy implications.

The collaboration between university and industryoccurs in many forms: contracted research fromindustry to universities, cooperative research betweenuniversity and industry personnel, licensing of uni-versity-owned patents to industry, informal informationexchange between university and industry personnel,consultation by university personnel, and establishingstart-up firms by faculty members or graduates ofuniversities in order to commercialize their researchresults. In this chapter, all of these activities arereferred to as ‘university-industry collaboration’.

The chapter is organized as follows. The next sectionprovides theoretical background of university-industrycollaboration. Then in the next section, after brieflyreviewing the history of U.S. university research, thecurrent status of university-industry collaboration isanalyzed. The section after that discusses potentialproblems in university-industry collaboration, followedby a section of conclusions and policy implications.

Theoretical BackgroundAccording to Kline & Rosenberg (1986), innovationsare traditionally considered to be based on a ‘linearmodel’ in which basic research, applied research,development, production, and marketing occurs insuccession (see Fig. 1). The fruits of basic research arethe fundamental understanding of nature, and areexpressed in the form of academic publication. Peoplewho read these publications develop theories into

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commercial products; therefore, the benefits of basicresearch often cannot be collected by sponsors, nor-mally being utilized by others who do not pay the costof research. Because of this ‘spillover effect’, privatebusinesses are not willing to invest in basic research, sothat, under the market mechanism, basic researchwould have less investment than that at the sociallyoptimal level. Thus, governments provide universitieswith funds to conduct basic research. According to thelinear model, as long as governments support basicresearch at universities, firms that find potentialcommercial benefits in the basic research results willcontinue on with applied research, development, andmanufacturing, thus coming up with the innovations.

However, this simple smooth process of innovation,stated by the liner model, has been criticized as‘unrealistic’ or ‘too optimistic’, and replaced by the‘chain-linked model’. According to the chain-linked

model by Klein & Rosenberg (1986), innovationsresult from the process which consists of the recogni-tion of potential markets, invention and analyticaldesign, detailed design and testing, redesign andproduction, and distribution, sales, and marketing (seeFig. 2). However, more importantly, among thesestages, there are feedback loops. According to thismodel, it is rare that newly generated knowledge fromresearch leads to innovations. Innovations often utilizeand rely on existing knowledge. Scientific and techno-logical knowledge is related to each stage of Fig. 2.Whenever problems occur, one consults with existingscientific and technological knowledge. However, ifthis existing scientific and technological knowledgecannot solve the problem, new research will beinitiated. The universities’ role is to fill the pool ofscientific and technological knowledge so that firmscan utilize it whenever they need.

Figure 1. Linear model.

Source: Drawn by the author from Kline & Rosenberg (1986).

Figure 2. Chain-linked model.

Source: Drawn by the author from Kline & Rosenberg (1986).

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Yukio Miyata Part IX

University-industry collaboration can be understoodas an application of the chain-linked model. Firms haverecognized that it is too optimistic to expect investingin basic research at a central laboratory automaticallyleading to innovations. They must shift their researchemphasis to short-term applied research and rely onuniversities to do basic research. Moreover, connectingscientific and technological knowledge of universitiesto industry needs is now important on the nationallevel. In the 1950s and 1960s, U.S. firms techno-logically were far ahead of foreign competitors. Whenuniversities supplied scientific and technologicalknowledge through academic publication, only U.S.firms could utilize such information. However, today’sforeign firms can utilize academic research results assoon as they are published in academic journals. So, itis important to build a more direct bridge betweenuniversities and industry in the U.S. for exploiting theexcellent research capability of U.S. universities,which is shown by the fact that they dominate in thenumber of Nobel laureates and attract students from allover the world.

However, the university-industry collaboration maynot generate the expected benefits for the followingreasons. First, university research personnel are good atbasic research but may be poor at considering marketa-bility. Thus, firms should not expect universities togenerate research results that are ready for manufactur-ing. Instead of completely contracting out research touniversities, firms should keep in touch with universityresearch personnel so that a feedback loop is built anda synergetic effect is generated.1 In addition, it is notproper for firms to cut in-house research budgets byrelying on universities for basic research. Even thoughbasic research has the spillover problem mentionedabove, firms should invest in basic research foraccumulating scientific and technological knowledgeon their own. Otherwise they will not be able tounderstand recent research trends, making it difficultfor them to discover new research topics and partnersfrom university research personnel. Moreover, patentsare far from innovations. Technological expertise oflicensees is necessary to transform licensed technologyinto commercial products, and technological expertiseis accumulated through in-house research (Rosenberg,1990).

Second, university research personnel may avoidfirms’ requests. While several universities clearlyconsider the contribution of faculty to regional indus-tries through cooperative research, licensing, orconsulting as ‘criteria for promotion’, however, promo-tion of faculty depends on the quantity and quality ofacademic research papers. Accepting money from

industry is beneficial for their research purpose, butuniversity research personnel are neither employeesnor sub-contractors of sponsoring firms. To preventshirking, firms should again keep in touch withresearch progress at universities.

Third, from a social perspective, the university-industry collaboration can be criticized as anexploitation of university research capabilities thatshould be available to the general public. The govern-ment has been supporting university research. When afirm utilizing university research results is able tocommercialize it through new products, consumershave to pay twice (tax to support university researchand the price of the product). Also, research personnelshould conduct research for public interest, such asassessments of pollution damage or risks of newtechnology, which sometimes conflicts with industrialinterests.

Fourth, university-industry collaboration may deteri-orate the research capabilities of universities. Duringuniversity-industry collaboration, firms often ask uni-versity research personnel to withhold announcementof research progress or research results even tocolleagues at the university. University research hasbeen developed with the free exchange of information.If one knows what other research personnel are doing,they can exchange critical opinions with each other inearly stages of their research, making correctionsquickly and avoiding duplication of research efforts(Cohen et al., 1998). The secrecy hinders informationexchange among research personnel in the academiccommunity and is detrimental to the quality of researchconducted by universities. In addition, because gradu-ate education is often based on research experience,deteriorating research quality leads to a decline in itsquality, and is costly to industry in the long-term.

Analysis of University-Industry Collaboration

Universities in the U.S. began as private institutesestablished by churches, followed by state universities.The expansion of state universities was owed to theMorrill Act of 1862, which allowed states to raise fundsfor their state universities by selling federal lands. Thefederal government and Congress played a role in thisact, but besides that, the federal role in universityresearch was minimal until World War II. According toTable 1, federal assistance for university research waslimited to agriculture through agricultural experimentstations. State governments were bigger sponsors ofuniversity research than the federal government.Money from industry accounted for 12%, which isgreater than the current level (mentioned later). More-over, money the universities themselves used to fundresearch came from the gifts or donation from wealthybusinesspeople. Hence, the relationship between indus-try and universities was strong before World War II and

1 Completely contracting out basic research to universities is astrategy based on the linear model rather than the chain-linkedmodel.

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universities often contributed to the development ofregional industry (Rosenberg & Nelson, 1994).

During World War II, many university researchpersonnel worked on military related research projectsincluding ‘The Manhattan Project’. The role of uni-versity research was highly recognized by the federalgovernment: the advancement of scientific knowledgegenerated by university research was expected to solveeconomic, social, and national security problems thenation faced. Furthermore, ‘The Sputnik Shock’ of1957, which lead to the National Defense EducationAct of 1958, increased federal basic research money touniversities. Figure 3 indicates that R&D money thatuniversities used, increased rapidly in the 1960s, so didfederal research money to universities. Also as Fig. 4indicates, the federal government has been the largestsponsor of university research, accounting for morethan 60% of university research spending, while theshare of industry or local governments has been lessthan 10%.

Table 2 indicates which federal agency supportsuniversity research. The National Institutes of Health(NIH) has been the largest federal sponsor of universityresearch. The National Science Foundation (NSF),whose function is to support university research, iscompeting with the Department of Defense (DoD) forsecond place for federal sponsorship. The NSF is farfrom a centralized agency that administrates universityresearch. U.S. policy supporting university researchcan be characterized as decentralized, where eachagency supports ‘directed basic research’ so thatresearch results would contribute to the needs of thatagency.

As shown later in detail, federal research money touniversities is heavily directed toward basic research.Although, compared with industry, universities play aminor role in performing research and development,their share as a performer of basic research increased toabove 50% during the 1960s and has since remained ata high level (see Fig. 5). If we include the government-

owned laboratories, which are contracted out touniversities to operate, the percentage reaches about60%.2

U.S. universities continued to remain in contact withindustry but the relationship between universities andindustry weakened in the 1960s due to an increase infederal funding. Research personnel at universitieswere interested in basic research. However, at the endof 1970s, federal funding stagnated and universitiesturned to industry once again as an important financialsource. Industry also began seeing universities as asource of scientific and technological expertise. Firmsno longer rely on ‘the linear model’ and reduced basicresearch at their central laboratories. As firms startedutilizing outside information sources, they becameinterested in cooperative R&D with other firms as wellas collaboration with universities. Moreover, in thefields of biotechnology and computer software, uni-versity research results were expected to directly resultin commercial products.3 University personnel startedestablishing start-up firms to commercialize their ownresearch results, which was necessary for emerginghigh-tech industries. The interests of industry anduniversity, therefore, coincided. In addition, policy-makers expressed concerns that, although U.S.universities were excellent in research, they did nothelp U.S. firms boost their competitiveness. Congresspassed the Patent and Trademark Amendments of 1980(Bayh-Dole Act), which allowed universities to ownpatents resulting from federal research money and tolicense them to firms. Then, the relationship betweenindustry and universities was strengthened in the 1980sonce again.

However, even after 1980, as Fig. 4 shows, the majorsource of university research money remained thefederal government. Because it is difficult to see thetrend, Fig. 6 focuses on the ratio of research moneyfrom industry to universities, to the amount of research

2 Shapley & Roy (1985) points out that U.S. universityresearch, including second-tier universities, had been heavilyinclined towards basic research and neglected engineering,causing a decline in competitiveness of U.S. manufacturing.However, Rosenberg & Nelson (1994) states that Shapley andRoy overstated the lack of applied research in the U.S.university research. For example, in life science, the fundingfor medical research has been greater than for biologicalresearch. Although, in some cases, university researchpersonnel have conducted research to satisfy their intellectualcuriosity, in many others, they have had only vague ideas ofthe potential application of their research results.3 The emergence of biotechnology does not mean the revivalof the linear model. While biotechnology seems to directlyresult from laboratory research, a great amount of clinicaltesting is necessary. Also, because firms conduct in-houseresearch secretly, if one only looks at commercialized results,they are seemingly breakthrough innovations. However, inactuality, in-house research is incremental, and feedbackloops among marketing, invention, designing, and manu-facturing exist (McKelvy, 2000, p. 273).

Table 1. Estimated university research funding sources in1935.

Sources Shares

State appropriations for agricultural experimentstations

14

Other state appropriations spent for research 14

Federal grants to universities for agriculturalexperiment stations

10

Non-profit foundations 16

Industry 12

University 34

Source: Sommer (1995, p. 7), Mowery & Rosenberg (1989,p. 93).

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Figure 3. University research spending.

Source: USNSF (2000).

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money that universities use. The ratio declined in the1960s and recovered in the 1980s but was still smallerthan 10%. However, research money from industry isoften used in university-industry-government cooper-ative research projects, so industry money is estimatedto affect 20%–25% of university research (Behrens &Gray, 2001). The ratio of research money from industryto universities to the research money that industryspends is shown in Fig. 7. It also recovered in the 1980sbut still only to about 1.3%. The relationship betweenuniversity and industry has been strengthened, but themagnitude is still not so strong. Biotechnology has astronger tie between industry and university. In 1994,research money from industry to universities is 1.5

billion dollars, which accounted for 11.7% of lifescience research funds that universities obtained fromexternal sources such as federal government or non-profit organizations, and for 13.5% of life scienceresearch funded by food-tobacco and pharmaceuticalindustries (Blumenthal et al., 1996; USNSF 2000).However, even in biotechnology, research money fromindustry does not dominate the research budget ateither, the university, or industry level.

The source of research funding is not so differentbetween private and public universities as Table 3shows. Private universities rely more on money fromthe federal government than public universities do, butmoney from university and non-federal government

Figure 4. Sources of university research.


Table 2. Shares of federal agencies in research funding to universities.

Year NIH NSF DoD NASA DoE DoA Others

1970 35.1 15.4 14.7 8.9 6.8 4.4 14.71975 47.8 18.0 8.4 4.5 5.5 4.5 11.31980 47.2 16.1 11.6 3.7 6.7 5.1 9.71985 49.8 15.8 14.8 3.7 5.6 4.6 5.61990 52.3 14.5 13.3 5.2 5.5 3.8 5.51995 52.6 14.5 13.3 5.9 5.0 3.6 5.01998 56.6 14.4 10.5 5.4 4.4 3.4 5.3

NIH: National Institutes of HealthNSF: National Science FoundationDoD: Departrnent of DefenseNASA: National Administration of Space and AeronauticsDoE: Department of EnergyDoA: Department of Agriculture.


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sources account for a higher percentage for publicuniversities than for private ones. However, the relianceon money from industry is the same between publicuniversities and private universities. The percentageincreased from 1977 to 1987, but it did not changebetween 1987 and 1997.

Table 4 shows the ratio of basic research, appliedresearch, and development in university research. Evensince university-industry collaboration was revitalizedin the 1980s, two-thirds of university research fundshave been used for conducting basic research. Devel-opment accounts for less than 10%. Universitypersonnel who are actually involved in collaborationwith industry admit that their research has been moved

away from basic research toward applied research anddevelopment (Cohen et al., 1994). However, this is nottrue of all university research.

A possible reason for the stable ratio of researchdirection at universities is that research money fromindustry to universities is still relatively small com-pared with the total university research budget.Moreover, it is interesting to point out that, as Table 5indicates, basic research accounts for 60% of allresearch money from industry to universities. It wouldseem that industry does not think much of theuniversities’ abilities to conduct development projectsin which industry has expertise. Industry wants accessto university basic research that it cannot adequately

Figure 5. The share of university as performer of basic research.


Table 3. Comparison of research funds source between private and public universities.

Federal Non-Federal Industry University Others*Government Government

1977Private 77.3% 2.3% 3.9% 6.4% 10.0%Public 61.3 13.0 3.1 16.1 6.5

1987Private 74.4 2.3 7.0 8.6 7.8Public 52.9 11.7 6.3 22.8 6.3

1997Private 72.3 2.1 7.0 10.1 8.5Public 53.4 10.4 7.1 22.8 6.3

* ‘Others’ includes non-profit organizations.



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Figure 6. The ratio of industry–university money to university research spending.


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Yukio Miyata

Part IX

Figure 7. The ratio of industry–university money to industry research spending.


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afford because of the spillover effect. Figure 8 indicatesthe ratio of basic research funding from industry touniversities to basic research funding that industryspends on. The ratio is higher than that regarding totalR&D expenditure shown in Fig. 7, but it is still lessthan 20%. As a result, industry does not completelyrely on universities to conduct basic research throughuniversity-industry collaboration.

Another reason for the large share of basic researchat universities even after university-industry collabora-tion was revitalized in the 1980s is that developmentwork is conducted off-campus through start-up firmswhich were established by faculty members or gradu-ates to exploit their research results. So, on-campusresearch remains basic research (Cohen et al., 1998).

Table 6 shows the directions of research money fromthe federal government to universities. Federal researchmoney actually consists of money from several depart-ments and agencies such as the National Institutes ofHealth, National Science Foundation, Department ofDefense, and National Aeronautics and Space Admini-stration. The share of basic research remains thelargest.

Table 7 shows that the composition of non-federal(mainly state) government money had been orientedtoward basic research in the 1960s, but since the early1970s, the percentage of basic research declined. Itshould be noted that, today, research money from stategovernments to universities is similarly proportionedfor basic research, applied research, and developmentas research money from industry is. While both stategovernments and industry like to utilize the basicresearch strengths of universities, they also like toavoid any spillover from their basic research. Stategovernments do not want the fruits of research fundedwith their money to diffuse beyond state borders. Thefederal government does not care much about spillover,

Table 4. Share of directions of university research.

Year Basic Applied Development

1953 45.2% 49.0% 5.9%1955 52.5% 41.4% 6.1%1960 68.8% 26.3% 4.9%1965 76.5% 19.0% 4.4%1970 76.7% 18.6% 4.6%1975 69.5% 26.2% 4.4%1980 66.8% 25.1% 8.0%1985 68.1% 24.5% 7.3%1990 65.7% 26.0% 8.3%1995 67.3% 24.8% 7.9%1998 68.7% 24.1% 7.2%


Table 5. Share of directions of research money from industryto university.


1953 63.4% 31.4% 4.9%1955 63.1% 31.5% 5.4%1960 61.0% 32.6% 6.4%1965 63.9% 31.3% 4.8%1970 65.6% 26.5% 7.4%1975 60.7% 32.5% 6.8%1980 59.0% 33.4% 7.3%1985 61.7% 31.4% 6.9%1990 60.5% 32.4% 7.1%1995 61.4% 31.7% 7.0%1998 63.6% 29.9% 6.5%


Figure 8. The ratio of industry–university basic research money to basic research spending by industry.


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though they recently begin worrying about spillover offederally funded research results to countries who aregood at commercializing research results published bythe research personnel of U.S. universities.

Research funding allocation to each university isbasically decided through a peer review in whichprominent scientists can receive research fundingbased on their ability, excluding political considerationor ‘pork barrel politics’. As a result, top rankeduniversities that have many able researchers, tend toobtain research money from the federal governmentand non-profit organizations. Universities that do notreceive adequate research money from those sourcesare more willing to accept money from industry.

Figure 9 plots the ratio of research money fromindustry to universities to total university researchbudget (INDRAT) against the total university re-search budget, indicating no clear trend. If a regressionanalysis of FY 1997 is done for 61 universities betweenINDRAT and total university research budget, thecoefficient is not statistically significant even at two-sided 10%.

INDRAT = 0.059 + 0.724 Total Research Budget

(t = 1.27, R2 = 0.027)

If the regression analysis is done between INDRAT andresearch money from the federal government touniversity, the relationship becomes weaker.

INDRAT = 0.067 + 0.491 Research Money fromFederal Government

(t = 0.58, R2 = 0.006)

Highly ranked universities receive a large amount ofresearch money from the federal government, so thereliance on money from industry becomes weaker.

Table 6. Share of directions of research money from federalgovernment to university.


1953 54.7% 39.6% 5.7%1955 61.0% 33.0% 6.0%1960 75.2% 20.6% 4.2%1965 80.9% 15.0% 4.1%1970 78.5% 16.6% 5.0%1975 73.7% 22.9% 3.4%1980 70.6% 21.0% 8.4%1985 72.1% 20.3% 7.6%1990 69.3% 21.5% 9.2%1995 71.2% 20.3% 8.6%1998 72.3% 20.1% 7.6%


Table 7. Share of directions of research money from non-federal government to university.


1953 16.4% 76.4% 7.2%1955 27.8% 64.7% 7.5%1960 49.9% 42.9% 7.0%1965 63.1% 31.1% 5.8%1970 75.6% 21.6% 2.7%1975 61.1% 32.5% 6.4%1980 59.3% 33.5% 7.3%1985 61.7% 31.4% 6.9%1990 60.5% 32.4% 7.1%1995 61.4% 31.7% 7.0%1998 63.5% 29.9% 6.6%


Figure 9. Industry–university money ratio (INDRAT) and research spending.



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In addition, Fig. 10 shows that the number of patentsissued to universities has increased since particularlythe mid-1980s, several years after the Bayh-Dole Actof 1980 allowed universities to own patents resultingfrom federal research funding. Although the directionin university research did not move toward develop-ment so much, the surge in patents implies thatuniversity research personnel became more willing toapply for patents.4 Another reason is that patentsresulting from basic research, such as in biotechnology,have been increasing. Since 1981, three life-science/drug-related patent classes have been top-3 patentsgranted to universities. The share of them was 18.81%in 1981 but increased to 25.33% in 1991, then reached40.97% in 1998 (USPTO 2000).

In biotechnology, cooperative research betweenuniversity and industry is productive for generatingpatents. According to Blumenthal et al. (1996),amongst the ‘Fortune 500’ pharmaceutical firms, thenumber of patents issued per $10 million researchspending is 1.7 for cooperative research with uni-versity, and 1.2 for ‘elsewhere’.5 For firms that are notin the ‘Fortune 500’, the number of issued patents per$10 million research spending is 6.7 for cooperativeresearch with university and 3.5 for ‘elsewhere’.

Mansfield (1998) investigates the percentage ofinnovations that could not have been produced withoutacademic research and those which were significantlysupplemented by academic research. The survey con-sidered the contribution of academic researchconducted between 1986–1994, to innovations gen-erated in 1994. Mansfield previously did a similarsurvey investigating the contribution of academicresearch conducted between 1975–1985, to innovationsgenerated in 1985, the results are listed together inTable 8. The survey covered 77 major firms that do notinclude any start-up (spin-off) firms of universities.Drug/Medical products and Instruments (includingmedical devices) have high scores, but the figures are30% at most. In other industries, figures are not sohigh. The contribution of academic based innovationsto total sales or to cost reduction was not so large.Moreover, the last row of Table 8 indicates that it takesa long time (6–7 years) to commercialize academicresearch results, while the time-span became a bitshorter in the 1990s compared with the 1980s. Largefirms tend to keep a proper distance from universityresearch.

While both are research intensive industries, com-pared with pharmaceuticals, in the semiconductorindustry, industrial research has been ahead of uni-versity research. Universities’ role had been limited toresearch personnel and entrepreneurs. The semicon-ductor industry organized Semiconductor ResearchCorporation (SRC) in 1982, which pooled researchmoney from firms and supported research at uni-versities. Since then, SRC has been effective ineducating graduate students for industrial opportunitiesand in providing a desirable scientific knowledge basefor industrial practice, although drastic innovationsremain to be created at industrial research laboratories(See Gutmann, this handbook).

According to AUTM (1998), in FY 1997, there are58 universities, which have at least 10 licenses, andtotal number of license agreements is 2,098. Amongthem, 10.1% went to start-up firms that were estab-lished under license from universities, 49.3% tonon-start-up small firms (less than 500 employees), and40.6% to large firms. When the Bayh-Dole Act wasenacted in 1980, exclusive licenses from universities tolarge firms would be invalid after eight years oflicensing or five years of successful commercialization,whichever comes first, so that large firms would notdominant license acquisitions. Since the dominance oflarge firms did not happen and the restrictions sig-nificantly prevented large pharmaceutical firms fromcommercializing university research results, the restric-tion was lifted in 1984. Even today, licenses actually goto small firms. Because the number of small firms aremany, it may be natural for small firms to account fora greater share of the licensees by universities, thougha large individual firm may be able to purchase manylicenses from universities.

Inter-organizational technology transfer is difficultunless the recipient has technological expertise. Thefact that there are many small firm licensees impliesthat these small firms have expertise to utilize uni-versity research. AUTM (1999) points out that morethan 90% of licenses to start-up firms are exclusive,while about half of all licenses to non-start-up smallfirms or large firms are exclusive. Large firms tend tokeep a proper distance from universities, emphasizinginformal information exchange between university andindustry research personnel. In contrast, start-up firmswant a more direct connection to university research(Etzkowitz, 1999).

Tables 9 and 10 give the results of a survey conducedby Lee (2000) that asked university personnel andindustry personnel respectively to rank the actualbenefits of cooperative research from 1 (lowest) to 5(highest) in scale. The survey shown in Table 9 covers422 faculty members of 40 universities randomlychosen from the top 100 in terms of their total researchbudgets. University personnel were likely to answerthat, through cooperative research with industry, theyobtained money and ideas for their own research. The

4 For universities such as Stanford University or University ofCalifornia-Berkeley that have been active in collaboratingwith industry, the biotechnology revolution of the 1970sincreased their patenting activities prior to the Bayh-Dole Act.However, for many other universities, the enactment of Bayh-Dole Act in 1980 was an important opportunity (Mowery,Nelson, Sampat & Ziedonis, 1999).5 Research carried out ‘elsewhere’ is mainly in-house R&Dbut also includes cooperative R&D with other firms or withnational laboratories.

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Figure 10. Patents issued to universities.


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Table 8. Innovations based on academic research.

Innovations that could not have beendeveloped without recent academic research

results (%)

Innovations that were developed with verysubstantial aid from recent academic

research results (%)

1986–1994 1975–1985 1986–1994 1975–1985

ProductsDrug/Medical Products 31 27 13 17Information Processing 19 11 14 17Chemical 9 41 11 4Electrical 5 6 3 3Instruments 22 16 5 5Machinery 8 N.A. 8 N.A.Metals 8 13 4 9Mean1 15 13 8 9Contribution to Sales* 5.1 3.0 3.8 2.1

ProcessesDrug/Medical Products 11 29 6 8Information Processing 16 11 11 16Chemical 8 2 11 4Electrical 3 3 2 4Instruments 20 2 4 1Machinery 5 N.A. 3 N.A.Metals 15 12 11 9Mean 11 10 7 7Contribution to Cost 2.0 1.0 1.5 1.6

Reduction†Years needed to 6.2 7.0 5.1 6.7

commercialize academicresearch results

* The percentage of new products sales to total sales.† The percentage of cost reduction by new processes.The survey of 1975–1985 did not cover the machinery industry.

Source: Reprinted of Table 1 of Mansfield (1998) with permission from Elsevier Science.

Table 9. Faculty benefits experienced with industry-spon-sored R&D.

Faculty benefits Rating

Acquired funds for research assistant and labequipment

3.87

Gained insights into one's own academic research 3.82Supplemented funds for one's own academic research 3.55Field-tested one's own theory and research 3.50Acquired practical knowledge useful for teaching 3.04Created student internships and job placementopportunities

2.97

Led to patentable inventions 2.55Created business opportunities 2.14

Source: Lee (2000).

Table 10. Industry benefits derived from collaboration withuniversity.

Industry benefits Rating

Gaining access to new research 4.01Developing new product/process 3.74Maintaining relationship with the university 3.61Developing new patents 3.37Solving technical problems 3.15Improving product quality 2.38Reorienting R&D agenda 2.34Recruiting students 1.75

Source: Lee (2000).

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enhancement of business opportunities was not highlyranked. Lee (2000) also points out that universitypersonnel tend to obtain what they wanted. Eventhough lowly ranked, university personnel who soughtto expand their business opportunities through cooper-ative research with industry achieved such goals.

The survey shown in Table 10 covers 140 firms ofAUTM (Association of University Technology Man-gers)6 members including start-up firms as well as largefirms. Industry personnel highly ranked ‘access to newresearch’ and ‘develop new product/process.’ Solvingshort-term problems such as ‘technical problems’ or‘quality improvement’ was not highly ranked. Thesurvey also indicates that, compared with cooperativeresearch between universities and large firms, the onebetween universities and start-up firms tend to focus onbusiness opportunities and does not contribute much tothe enhancement of research and education capabilitiesof university. The distance between universities andlarge firms is greater than that between universities andsmall ones.

Table 11 indicates the correlation among the numberof invention disclosures, the number of patent applica-tions, the number of licensing agreements, and theamount of license revenues based on each university’sdata of FY1999 (AUTM 2000). The relationshipbetween invention disclosures and patent applicationsis strong, but the amount of license revenue is notrelated to any other variable. It is very uncertain whichinvention will result in huge license revenue. USGAO(1998) states that a small number of highly successfulpatents or ‘blockbuster’ patents, which are often in thefield of medical/life science, account for a large portionof the license revenue of a university. And thoseblockbuster patents often resulted from federallyfunded research. Table 12 indicates how much license

revenue of the elite universities come from the resultsof federally funded research results. In some uni-versities, the percentage is above 90. It is unlikely thatcooperative research between industry and universitywill lead to a blockbuster patent from scratch. Auniversity’s research quality has been built on moneyprovided by the federal government for a long time,and nowadays, industry utilizes the research capabil-ities of universities by supporting only 10% of theresearch budget of the universities.

License revenue is not only uncertain but alsoinadequate to support university research. Table 13compares license revenue and research spending byuniversities in FY1997. The ratio of license revenue tototal university research budget is very small. Most ofthe universities have a value of less than 5%. The ratioof license revenue to research money from industry touniversity is also small. Only seven universities havevalues of 100% and the majority of universities havevalues of less than 25%. The license revenue accountsfor a tiny portion of the total research budget. It isdifficult for universities to finance their research budgetwith license revenue.

However, license revenue represents only smallportion (usually 2%–5%) of sales of new productsresulted from licensed technology. According toATUM (2000) which assumes that 83% of the licenseincome is associated with product sales, the royaltyrate is 2% of product sales, and $151,000 is necessaryto support an employee, in FY 1999, licensed technol-ogy generated $35.8 billion of sales and 237,100 jobs.Moreover when a firm receives a license, it increasesinvestment to commercialize that technology. Licensesfrom universities induced $5.1 billion investment,supporting 33,800 jobs. As a result, the total impact ofuniversities on North American economy is $40.9billion and 270,900 jobs. This is a tiny compared withGross Domestic Product of the U.S. ($9000 billion in1999), but greater than the amount of researchspending by universities ($26 billion in 1998). While

6 AUTM is an organization whose purpose is to promotetechnology transfer from universities to industry. Firms thathave membership in AUTM are strongly interested incollaborating with universities.

Table 11. Correlation among outputs of university research(FY 1999).

Invention Patent License LicenseDisclosure Application Agreement Revenue

Invention 1Disclosure

Patent 0.87 1Application

License 0.76 0.61 1Agreement

License 0.33 0.29 0.39 1Revenue

Source: Calculated by the author from AUTM (2000).

Table 12. Share of license revenue resulted from federallyfunded research (FY 1996).

University Share

Johns Hopkins Univ. (Medical) 60.9%Johns Hopkins Univ. (Applied Phys Lab) 25.7%Univ. of Washington 16.9%Stanford Univ. 92.7%Univ. of Michigan 21.5%Univ. of Wisconsin-Madison 98.9%Harvard Univ. 70.0%Columbia Univ. 95.6%Michigan State Univ. 97.7%Univ. of California* 51.8%

* The entire University of California campuses.

Source: USGAO (1998).


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universities cannot earn license revenues to financetheir research spending, the impact of their innovationsis greater than the research spending, implying thejustification of government support for universityresearch.

Table 14 shows the inputs and outputs of research ofthe elite universities with a high quality of research. Asmentioned before, the elite universities receive a largeamount of research money from industry, but moneyfrom the federal government is much larger, so moneyfrom industry is proportionally smaller in total of aresearch budget. The University of Michigan has notgenerated a blockbuster patent yet, so its licenserevenue is small compared with the other threeuniversities. Table 14 also indicates that even eliteresearch universities that generate many patents andlicenses and earn license revenue cannot finance theirresearch budget from it alone. Their high qualityresearch has to be federally financed.

In the same way as university-industry collaborationwas revitalized in the late 1970s, the university’s role in

promoting regional development has been recently re-emphasized. Stanford University and the adjacentStanford Research Park have successfully attractedmany high-tech firms, and have become a nucleus ofdevelopment for the greater Silicon Valley area. Also,the state of North Carolina has created a research parkcalled Research Triangle Park, through three prominentuniversities in the state; University of North Carolina,Duke University, and North Carolina State University.These two cases are the envy of the world as well asother state governments.

What is the relationship between university researchand innovation in a region? Despite being somewhatdated, the database created in 1982 by the U.S. SmallBusiness Administration (SBA) is excellent because itidentifies the actual location of where those innova-tions occurred, rather than the location of headquartersof innovators. As well as Feldman (1994) andAudretcsh & Feldman (1996) who used this database tosupport spillover effects of university research onregional innovations, Varga (1998) has conducted a

Table 13. Relative magnitude of license revenue of universities (FY 1997).

The Ratio to Total Research Budget The Ratio to Research Money from Industry

Range No. of Universities Range No. of Universities

5% � X 10 100% � X 71% � X < 5% 25 50% � X < 100% 90.5% � X < 1% 15 25% � X < 50% 130.2% � X < 0.5% 15 10% � X < 25% 180.1% � X < 0.2% 4 5% � X < 10% 9X < 0.1% 4 X < 5% 17

Total 73 Total 73

Source: Calculated from AUTM (1998).

Table 14. Inputs and outputs of major university research (FY 1997).

Stanford MIT Harvard Michigan

Research Money from Industry(thousand dollar)

24,000 59,000 12,000 31,000

Research Money from Federal Govermnent(thousand dollar)

332,000 311,000 223,000 296,000

Total Research Budget(thousand dollar)

395,000 411,000 300,000 483,000

No. of Invention Disclosures 248 360 119 168

No. of New Patent Application 128 200 61 80

No. of Issued Patents 64 134 39 52

No. of License Agreements 122 75 67 47

License Revenue(thousand dollar)

51,760 21,210 16,490 1,780

No. of Start-up Firms 15 17 1 6

Source: AUTM (1998), USNSF (2000).

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regression analysis on state and metropolitan arealevels. In both cases, the number of innovationsdepends on the university research budget, the researchbudget of other firms, and the distance between thefirm and the university. As the distance increases, theimpact of research spending from other organizationsdeclines. The rate of decline is faster in the case ofspillover from other firms’ research than that fromuniversity research. University research is more charac-teristic of the public good (spillover effect) thancorporate research does. However, the distancebetween innovator and university is still important.

Furthermore, Varga has calculated how much auniversity’s research budget has to increase in order togenerate one more innovation of a region, which hecalled Marginal University Research Expenditure,MURE. And he categorizes regions into four groupsaccording to calculated MURE values. In the lowestMURE group, which is the most innovative, in order toincrease the number of innovations by one, a uni-versity’s research budget must increase by 5%. In thesecond group, the university’s research budget mustincrease by 33%. In the third group, the university’sresearch budget must be three-fold. In the forth group,the university’s research budget must be 50 times thecurrent level, which is impossible to implement.Regions where university research has already led toinnovations can increase the number of innovationseasily by increasing university research budget. How-ever, for regions in which university research has notsuccessfully resulted in innovations, it is very difficult,if not impossible, to increase the number of innovationsby increasing university research budget. Thus, a newlybuilt research park is not always successful.

Although the SBA database, on which the aboveresearch works were based, is excellent in identifyingthe actual location of innovations, it is old and wascreated before the onset of the Internet. The Internetmakes complex information exchange possible. Theextent to which the Internet will be able to substitutefor face-to-face interaction in the future will affect theimportance of concentrating research facilities adjacentto universities and thus the policy in building researchparks. Further empirical research is necessary in thisfield.

AUTM (1998) states that while, in FY1997, 333firms were established under licenses from universities,83% of them were located in the same state as thelicenser universities. It is true that research universitiesgenerate and diffuse the scientific/technological knowl-edge necessary for innovations, and supply and attractentrepreneurs. However, it seems these research parksare only successful in the long term. Stanford ResearchPark and North Carolina Research Triangle took morethan twenty years to successfully develop. Patientsupport is necessary. Moreover, when these two weredeveloped in the 1960s, competitors were rare. Sincethe 1980s, many local governments have been inter-

ested in developing research parks as part of theirhigh-tech industrial policy, so competition is rigorousand offers no guarantee that every research park will besuccessful.

Another important point is that, even if a researchpark is successful in attracting research facilities, itdoes not necessarily lead to an increase in employmentin the manufacturing industry. State governments donot build research parks near universities for theprestige—they do so to increase employment. Researchresults of a research park are, however, not necessarilymanufactured in the region. Lugar & Goldstein (1991)compares research park regions against adjacentregions that have a similar size population withoutthem. Of 45 research parks, 19 have generated lessemployment than the adjacent regions. Moreover, theyhave found that the success factors of research parksfor regional economic development are: having time-honored tradition, having good research universities,and being located in regions of 0.5 to 1 million peoplewhich offers both a pool of labor forces and a marketfor manufactured products.

Potential Problems of University-IndustryCollaborationAs mentioned previously, university-industry collab-oration has potential negative aspects. Money fromindustry may induce university research personnel todistort research results, called a ‘financial conflicts-of-interest’ problem. The problem is that the public doesnot trust research results of university personnel whohave financial ties with industry, even though they donot actually forge research results. When universityresearch personnel spend too much time in cooperationwith industry, other tasks of the university such aseducation and research for public interest areneglected, called a ‘conflicts-of-commitment’ problem.A sponsoring firm might ask university researchpersonnel to postpone the announcement or publicationof research results until the firm obtains patents orbecomes ready for commercialization, called a‘secrecy’ problem. These problems existed before andwere discussed when the Bayh-Dole Act was enactedin 1980.7 The atmosphere in 1980 was, however, that‘you cannot make an omelet without breaking eggs’

7 It is often difficult to distinguish these three problems. Forexample, the secrecy problem is a neglect of duty tocontribute to public interest on the part of university researchpersonnel, so it is also the conflicts-of-commitment problem.Also, it can be viewed as a distortion of behavior by financialinterests with the sponsoring firm, making it somewhat likethe financial conflicts-of-interest problem. Furthermore, howmuch university professors should be allowed to use theirstudents for collaboration with firms has both aspects offinancial conflicts-of-interest and conflicts-of-commitmentproblems. Therefore, sometimes all these problems are calledsimply as ‘conflicts-of-interest’ without being categorizedfurther.


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(one should tolerant some negative aspects in order toexploit the benefit of university-industry collaboration)(White, 2000, p. 96).

An example of a negative aspect of university-industry collaboration was cited by Blumenstyk(1998), referring to that, in 1996, an article pointed outthe danger of appetite suppressants. In the same editionof the journal, two other academic physicians made acommentary that minimized the study’s conclusions.What was not announced to the public was that thesetwo physicians were paid consultants to companies thatmade or distributed similar drugs. This is a typicalfinancial conflicts of interest problem. Among 789 lifescience articles published in 1992, 33.8% of them hadat least one chief author who had financial interestswith firms whose activities were related to the field ofthe published research. Financial interests include theresearch personnel owning stock in a firm, working asa consultant or a director for it, or receiving researchmoney form it (Krimsky et al., 1996). It is difficult toidentify whether or not these financial interests actuallycause university research personnel to alter researchresults in favor of the sponsor, but it is important not tomake the public suspicious of any such activity.

In 1995, NIH and NSF requested universities thatwere receiving funds from them to establish a (finan-cial) conflict-of-interest policy. Today, manyuniversities have their own such policy. According toCho et al. (2000) who surveyed 89 universities of thetop 100 NIH fund recipients, it is uncommon (only19%) for a university to specify prohibited activities.Many universities simply require research personnel toreport financial interests as the first step to mitigate afinancial conflicts-of-interest problem. Selection oflicensees and cooperative research partners should beconducted fairly so that firms which do not have anyfinancial relationship with research personnel wouldnot be disadvantaged. Hence, research personnel whohave financial interests with firms should not be amember of any committee that decides licensees orresearch partners.

In the conflicts-of-commitment problem, universitypersonnel have been allowed to work as a consultantonce a week. Although consulting is often a paid job, itis regarded as a community service or diffusion ofknowledge, which is the university faculty’s duty inaddition to research and education. Because inter-organizational technology transfer is difficult, auniversity researcher often works as a consultant of alicensee firm to provide technical advice for thecommercialization of licensed technology. The eliteuniversities tend to set a maximum number of days foroff-campus work, which is usually equivalent level tothe traditional ‘once a week’ rate.

About the secrecy problem, Cohen et al. (1994) hasconducted a survey regarding restrictions on commu-nication which are placed on the faculty members whoparticipate in industry–university cooperative research.

The survey covered 479 UIRCs (University IndustryResearch Centers) in which several firms participatedin cooperative research programs with several depart-ments of one university and which received financialsupport from both the federal and local governments.The state government is particularly interested insupporting UIRC so that it would be developed into anucleus of research park. UIRCs have an aspect ofpublic policy and the UIRC staff state that the purposeis to gain scientific/technological knowledge ratherthan to improve existing products or to create new jobs/business, but restrictions are found to be rather tight.The survey indicates that 56.6% of UIRCs have somerestrictions. 21.3% of UIRCs set restrictions oncommunications with faculty members of the sameuniversity if they do not participate in the samecooperative research project. 28.6% of them setrestrictions against the faculty members at otheruniversities, 39.9% of them set restrictions againstcompanies that are members of UIRCs but do notparticipate in the same project, and 41.5% of them setrestrictions against general public.

Blumenthal et al. (1997) has surveyed 3,394 lifescience faculty members of the top 50 universities ofNIH recipients. Among 2,167 responses, 19.8% ofthem answered that they had postponed publicationmore than six months. As NIH states that a delay of 60days is reasonable for sponsoring firms to preparepatent application, six months is rather long. However,the delay of publication is due not only to thesponsoring firms’ request but also to the intentions ofthe research personnel. When a researcher publishes apaper, other researchers may follow the research andpublish more and better papers. A researcher wants towithhold publication until many papers are ready to bepublished. However, in the survey, 27.2% of research-ers who participate in cooperative research withindustry have experienced publication delays of morethan six months, but the percentage is 11.1% forresearchers who do not participate in cooperativeresearch with industry. Hence, collaboration withindustry may cause publication delays. On the otherhand, in the aforementioned survey about conflicts-of-interest policy by Cho et al. (2000), only 12% of theuniversities set a time limit for how long a publicationcan be delayed.

Problems of conflicts of interest, conflicts of com-mitment, and secrecy have been occasionally reported.There is no time-serious data to check whetherproblems have recently become worse or not. More-over, it is inconclusive whether or not the negativeaspects of university-industry collaboration outweighthe benefits. However, as Table 12 indicates, uni-versities earn a significant portion of license revenuefrom federally funded research. There are severalpolicymakers who express concerns that federallyfunded research is being utilized for specific firmswhich only pay for the last stage in the completion of

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research, rather than for the general public interest(Campbell, 1998). The elite research universities ofhigh research quality have a strong negotiating positionwith sponsoring firms because these universities obtainadequate federal research money and need not rely onmoney from firms. Moreover, firms definitely wantaccess to the research capabilities of these universities.However, second-tier universities may easily acceptfirms’ demands to attract research money from indus-try. Firms can threaten even the elite universities bycontracting with others that are more responsive totheir needs. It may be time for the entire universitycommunity to set up rules on the freedom of publica-tion (Nelsen, 1999).

Another problem, which is often called ‘institutionalconflicts of interest’ or ‘conflicts of mission,’ is thatuniversities have become very interested in earninglicense revenue. Recently, universities are increasinglytaking a too restrictive approach to licensing andputting too high a value on their intellectual propertycontributions. Firms seek second-tier universities andforeign universities for collaboration when they thinkthat elite universities are tough to deal with (Govern-ment-University-Industry Research Roundtable, 1998).Firms complain that university TLO (TechnologyLicensing Organization) is too oriented toward legalstaff, and should have more staff with engineering andmarketing backgrounds (Siegel, Waldman & Link,1999). Moreover, in 1999, NIH issued the guidelinesregarding research tools such as cell lines, animalmodels, reagents, clones, or database, which resultedfrom NIH funding. NIH thinks that these research toolsshould not be treated as intellectual property right butshared by the entire research community.

Finally, if government promotes university-industrycollaboration, the existence of foreign firms andforeign students would be a problem. If foreign firmscan participate in university-industry collaboration,federally funded research results would help themcompete against U.S. firms. The Bayh-Dole Actrequires that when firms commercialize a product madewith the exclusively licensed technology from uni-versities, it should be ‘substantially’ manufactured inthe United States.

USGAO (1992) has found that in 1991, among 197patents resulting from funding of NIH or NSF, only 18patents are licensed to foreign firms and 11 patents arelicensed to foreign subsidiaries in the U.S. The Bayh-Dole Act does not prohibit licensing to foreign firms,but it does requires that manufacturing occurs in theU.S. However, universities do not follow wheredomestic licensees actually manufacture the productmade with the licensed technology (USGAO, 1998).

While a similar concern was discussed to a lesserextent when the Bayh-Dole Act was enacted, access byforeign firms to U.S. universities research was hotlydebated by Congress in the late 1980s when economicnationalism peaked. In particular, MIT was criticized.

About half of the members of the MIT LiaisonProgram, which will be discussed in detail later, wereforeign firms, and MIT had a Liaison Office in Tokyo.However, in 1990, 15% of MIT research funds weresupported by industry, with 20% of those funds comingfrom foreign firms; therefore, only 3% of all researchmoney was from foreign firms. 86% of the licenseswent to U.S. firms, and 77% of the firms for whichfaculty worked as consultants were U.S. firms. Amongthe 215 chairs endowed by firms, only 30 weresupported by foreign firms. While some media andpoliticians expressed concern that elite universitiessuch as MIT helped foreign competitors, the actualinvolvement of foreign firms was minor.8 It was anappropriate policy decision not to impose any restric-tion on university-industry collaboration in terms oftheir nationality (U.S. Congressional Hearing, 1993).

Another concern is the existence of foreign students.As university research was increasingly orientedtoward industry needs, foreign students may under-stand what kind of frontier research U.S. firms areinterested in. And, when foreign students go home afterobtaining a Ph.D. and work for firms in their homecountry, they compete against U.S. firms.

Figure 11 shows that the percentage of U.S. citizenrecipients of science and engineering doctoral degrees9

was declining between 1986 and 1994, while the a littleincrease was observed in the late 1990s. The percent-age would be increasing if it included foreign-bornpermanent residents; however, a significant portion ofdoctoral degrees are offered to foreign students. Thescientific/engineering manpower has to rely on foreignnationals or foreign-born residents. Foreign studentscould not participate in several research projectsfunded by the Department of Defense due to nationalsecurity concerns. There was an opinion that a similarrestriction might be necessary for economic com-petitiveness reasons. In fact, as Korea and Taiwandeveloped their own high-tech industries, studentsfrom those countries increasingly tended to return totheir home countries. But students from India andChina supplemented this decline in the 1990s.10

Throughout the 1990s, the percentage of foreign(including permanent residents) doctoral degree recipi-ents in science and engineering fields who plan to stayin the U.S. has been increasing, to above 70% in 1999.The percentage of those who could actually find jobs

8 U.S. firms can have an informal relationship with universityresearch personnel, so they do not have to be members of theLiaison Program. As a result, foreign membership must behigh.9 Science includes social and behavioral psychology sciences,in which the shares of U.S. citizens and permanent residentsare relatively high.10 However, Chinese government recently tries to bring backChinese research personnel by offering financial incentives.


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Figure 11. The ratio of U.S. citizens to S&E Ph.D.

Source:USNSF (1998b).

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Yukio Miyata

Part IX

Figure 12. Foreign S&E Ph.D. recipients staying in the USA.

Source: USNSF (1998b).

An A



nited StatesC

hapter 5

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and stay in the U.S. has also been increasing, as high as50% in 1999 (USNSF 2002, Chap. 2). Foreignscientists and engineers have already become impor-tant labor force in the high-tech industries anduniversities Between 1995 and 1998, 29% of the start-up firms in Silicon Valley were established by Indian orChinese people. As they keep a relationship with theirhome country, foreign start-up firms tend to be good atexporting (U.S. Congress Committee Report, 1999).Moreover, even if foreign students from the eliteuniversities such as MIT and Stanford return homeafter graduation, they are likely to become business/political/academic leaders there, and are expected tocontribute to enhanced relations with the U.S. andfavor of long-term U.S. interest (U.S. CongressionalHearing, 1993). Because foreign graduate studentsactually conduct a significant portion of research,excluding them may deteriorate the quality of researchcarried out at U.S. universities (Burtless & Noll, 1998).It was the correct decision for policymakers not toimpose any restrictions on enrollment of foreignstudents at U.S. universities.

Conclusions and Policy ImplicationsU.S. universities contributed to regional economies inthe second half of 19th century and the early 20thcentury. However, since World War II, particularly afterthe Sputnik Shock of 1957, the federal government hasbeen strongly supporting basic research conducted byuniversities. In the late 1970s, university-industrycollaboration was revitalized, thanks to the Bayh-DoleAct, the biotechnology revolution, the financial needsof universities, and a revised of R&D strategy withinindustry based the linear model.

However, even since the 1980s, university researchhas still emphasized basic research funded by thefederal government. The elite universities obtain alarge amount of research money from the federalgovernment as well as industry, so their reliance onindustry is small. The elite universities generate bothacademic research results and research results close tocommercialization. However, license revenue resultsfrom a few ‘blockbuster’ patents, often in the field ofmedical/life science. There is a major mechanism inuniversity-industry collaboration in the U.S.: federalgovernment generously supports medical/life researchat universities and its research results are easilyprotected by patents, resulting in licenses to firms someof which generate huge license revenue for universities.However, license revenue is small compared to theresearch budget of universities. It is difficult foruniversities to maintain a high quality of research byearning money themselves. Universities cannotbecome ‘for-profit research organizations’, and tax-payer support is still critical for maintaining universityresearch. Because federal government money is stillimportant for university research, how to draw the linebetween two of the following opinions should be

further discussed: ‘university research results shouldbe openly available to the public’ and ‘industryshould be allowed to promote the commercialization ofresearch results that eventually benefit consumers’.

Research universities actually generate innovationby diffusing knowledge and supplying entrepreneurs.Governments of states try to build research parks thatconcentrate research facilities around universities,hoping to create their own ‘Stanford Research Park’ or‘North Carolina Research Triangle Park.’ However,these two research parks took more than twenty yearsto become successful, and for research parks tosucceed there must exist a high quality research uni-versity nurtured by long-term federal research funds. Itis not easy for local governments to quickly improvethe quality of research done by universities. Moreover,even if a research park successfully attracts researchfacilities, it is uncertain if they will significantlycontribute to an increase in high wage manufacturingemployment of the region.

The contribution of universities to industry shouldnot be limited to visible results such as inventions,patents, or licenses. As Cohen et al. (1998) points out,informal linkage such as communication betweenresearch personnel is important for industry to transferuniversity expertise to firms. In this aspect, the role ofILP (Industrial Liaison Program) is important. Firmsthat become members of the ILP are introduced toproper research personnel at the university to obtainadvice. TLO (Technology Licensing Organization) isan organization that licenses patented technologyresulted from university research. By promoting theflow of informal information, ILP covers the primitivestage of university-industry collaboration and TLOcovers the mature stage of collaboration. In the regionsor nations in which university-industry collaboration isnot yet so active and where universities do not havemany patents to license, ILP rather than TLO should beestablished first.

An important point is to provide research personnelat universities with incentives for informal cooperationwith industry. Visible results, such as the number ofpublications or patents, can be easily used to evaluateuniversity research personnel, but assistance toregional industry through informal communication isnot. The effort a university faculty member spendsanswering questions from industrial personnel shouldalso be evaluated since it is important for industrialinnovations, even if it does not necessarily lead topatents.

Besides contributing to industrial innovations informal (patents, inventions, licensing, or activity ofTLO) and informal ways (advising, communication, oractivity of ILP), education remains an important rolefor universities. Basic research educates researchpersonnel. Middle class engineers are also generatedby university education. In addition, universities gen-erate mathematics and science teachers for elementary

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and secondary educational institutions. Of course,graduate education has been effectively developedthrough a mixture of education and research, so goodresearch universities are often strong in generatingresearch personnel, academic research results, andinventions. However, universities can contribute togenerating engineers and schoolteachers even if theirresearch is not top-ranked. Local governments shouldnot insist on universities generating innovations, butprovide them with financial resources for research andscholarships. Good engineers and scientists have tacitknowledge by which they learn new things not writtenin books. Therefore, if a region has this kind of humanresources, firms can realize a high rate of return ontheir R&D or manufacturing investment.

In conclusion, there are several ways in whichuniversities contribute to innovations and regionaldevelopment: they generate inventions, patents,licenses, informal communication with regional firms,spin-off firms, and human resources including scien-tists, engineers and schoolteachers. One universitydoes not have to attempt all of these functions. Theentire university system of a nation or a region shouldcover them so that the division of labor among theiruniversities is desirable. Needless to say, all of thesetasks take a long time to achieve. It is not proper forpolicymakers to expect quick results with an increasein funding to university research. Funding universityresearch is not the way to solve cyclical recessions. Inaddition, firms and universities should not expect easyreturns from collaboration, remembering the followingstatement by Doctor Lewis Branscomb, whose brilliantcareer spans government (National Bureau of Stan-dards, now National Institute of Standards andTechnology), private sector (IBM), and academia(Harvard University):

If the universities value the partnership as a means ofexposing faculty and students to leading-edge tech-nical issues that are driving innovations of benefits tosociety, and are not basing their expectations pri-marily on revenues from patents, a stable, productiverelationship may endure. If the firms see universitiesas sources of new ideas and as windows on the worldof science, informing their own technical strategies,rather than viewing students as a low-cost, pro-ductive source of near term problem-solving for thefirm, they too will be rewarded (U.S. CongressCommittee Report 1998, p. 22).

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