MAPP working paper no 4October 1992ISSN 0907 2101

The Concept of Key Success Factors:Theory and Method

Klaus G. GrunertCharlotte EllegaardProject no 15

Executive Summary

1. The term key success factors can be used in four different ways: a) as a necessary ingre-dient in a management information system, b) as a unique characteristic of a company, c) as aheuristic tool for managers to sharpen their thinking, d) as a description of the major skillsand resources required to be successful in a given market. We adopt the last view.

2. The actual key success factors on a market, and those key success factors perceived bydecision-makers in companies operating in the market, will be different. A number of psycho-logical mechanisms result in misperceptions of the causes of success on a market. Both theactual key success factors on a market, and the way they are perceived by decision-makers,are amenable to scientific analysis. Such an analysis can improve performance of decision-makers on that market.

3. The major immediate causes of success on any market are the value perceived by customersin a business's products, and the costs (relative to competitors) incurred in producing thisvalue. How good a business is in creating customer value at low costs will depend on skillsand resources of the company. We therefore define a key success factor as a skill or resourcethat a business can invest in, which, on the market the business is operating on, explains amajor part of the observable differences in perceived value and/or relative costs.

4. Key success factors differ from core skills and resources, which are prerequisites for beingon a market, but do not explain differences in value created and costs incurred. They also dif-fer from slack skills and resources, which neither explain differences in value created andcosts incurred, nor do they form prerequisites for being on a market.

5. Key success factors differ in their changeability, i.e., in the degree to which competitors canemulate them. They also differ in whether they are conjunctive or compensatory. Conjunctivekey success factors are necessary conditions for superior performance. Compensatory keysuccess factors open up for choices of areas of excellence and hence for the formation of stra-tegic groups.

6. Perceived key success factors can be measured by semi-structured interviews with businessdecision-makers which follow a laddering procedure. Actual key success factors can be mea-sured by collecting objective or semi-objective company data and relating them statistically tomeasures of relative costs and perceived value.

Table of contents

1. Reinventing the science of business administration?................................................1

2. Four views on key success factors ..............................................................................2

Key success factors in management information systems .......................................2

Key success factors in strategy research ..................................................................5

3. Key success factors and competitive advantage: An extended view .......................7

Meta-theoretical assumptions...................................................................................8

Frame of reference .................................................................................................10

Key success factors and auxiliary skills and resources ..........................................15

Definition and research agenda ..............................................................................17

4. Key success factors and market characteristics......................................................19

5. Methods for measuring key success factors ............................................................22

Finding the actors' hypotheses ...............................................................................22

Establishing causal relationships............................................................................24

6. References...................................................................................................................26

The Concept of Key Success Factors: Theory and Method

The use of the key success factor concept in the MIS and strategy literature is traced, and anew view is presented, which defines key success factors as skills and resources with highleverage on customer perceived value and relative costs of a business. Key success factors aredistinguished from core, slack, and auxiliary skills and resources. Perceived are distinguishedfrom actual key success factors, and it is argued that empirical research on key success fac-tors should address both. A research agenda is presented, and various methods for solving themethodological problems in that agenda are discussed.

1. Reinventing the science of business administration?

In a general sense, almost all scientific research in business administration is concerned withunderstanding what makes some businesses more successful than others. In marketing, orga-nization, finance, accounting - all the academic counterparts of the classical functional areasof business administration are concerned with understanding how that particular part of busi-ness functions, and how this functioning is related to business success. It may, hence, appearstrange that a concept like success factors could be launched as something new and original,something which merits special research attention.

The very fact that business research has departmentalized into the various functional areasmay be one reason. Business practitioners have always emphasized the need for generalmanagement as a topic for both research and education, a topic which could integrate thevarious functional areas in such a way that a basis for the broader policy decisions is formed.Academia has responded to this demand by establishing courses and research in businesspolicy and later strategy formation. This stream of research now forms the background forconcepts like key success factors, which is not supposed to replace a more detailed evaluationof function-specific contributions to success, but rather to direct attention to some of the bro-ader issues around the determinants of business success.

Part of that stream of research has been influenced by research in economics, which alwayshad retained the general view of the firm, and where, in industrial economics, determinants ofperformance has been a research tradition for several decades. However, market characteristi-cs, which are not actionable from the viewpoint of the firm, received more emphasis therethan organizational characteristics, which are actionable, and hence of greater interest fromthe viewpoint of business administration. Indeed, the fact that business performance is basedon perceptions and decisions of business executives is usually neglected in economics.

A theory and/or method contributing to an understanding of the key success factors in anindustry would without doubt be of considerable academic and practical interest. The key suc-cess factor concept has, however, been used in a rather diverse way in the literature, as will beshown shortly. It is the purpose of this paper to sort out the uses of the concept to date, and,on the basis of this, to propose a new view of key success factors. This view will then berelated to existing theories and methods, leading to an agenda for empirical research.

2. Four views on key success factors

We believe that four views on key success factors can be distinguished in the literature.Historically, the concept originated in the field of management information systems. It waslater transferred to the field of business strategy research. There it is used in different ways,corresponding to the different schools of thought that can be found in that area (Grunert, inpress; Jemison, 1981; Mintzberg, 1990a). Mainly, one can distinguish between key successfactors as a business characteristic, as a planning tool, and as a market description. Each viewwill be briefly described and evaluated.

Key success factors in management information systems

The idea that there are a few factors which are decisive for the success of the company, andthat these factors can be ascertained, was first introduced by Daniel (1961) and later mainlyelaborated by Rockart (1979; Bullen & Rockart, 1981) in the context of designing manage-ment information systems. Finding that top management rarely used management informationsystems, they argued that such systems must be structured according to the information needsof the managers. In order to ascertain managers' information needs and link them to themanagement information system, they coined the term critical success factor.

Critical success factors are, according to Bullen and Rockart, "the limited number of areas inwhich satisfactory results will ensure successful competitive performance for the individual,department or organization. Critical success factors are the few key areas where 'things mustgo right' for the business to flourish and for the manager's goals to be attained." (Bullen &Rockart, 1981, p. 7).

Rockart's concept of critical success factors is clearly inspired by the issue of optimum matchbetween environmental conditions and business characteristics, i.e., the core of business stra-tegy. The surrounding environment is assumed to possess certain fundamental requirementsand limitations, threats and opportunities, to which businesses must align their strategy, skillsand resources, in order to achieve success. No organization, according to Rockart, can affordto develop a strategy which fails to provide adequate attention to the principal factors whichunderlie success in the industry. This provides the rationale for making them the basis of amanagement information system.

Rockart distinguishes between five sources of critical success factors:

• The industry, e.g., demand characteristics, technology employed, product charac-teristics etc. These can also affect all competitors within an industry, but theirinfluence will vary according to the characteristics and sensitivity of individualindustry segments.

• Competitive strategy and industry position of the business in question, which isdetermined by the history and competitive positioning in the industry.

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• Environmental factors are the macroeconomic influences that affect all competi-tors within an industry, and over which the competitors have little or no influence,e.g., demographics, economic and government legislative policies etc.

• Temporal factors, which are areas within a business causing a time-limiteddistress to the implementation of a chosen strategy, e.g., lack of managerialexpertise or skilled workers.

• Managerial position, i.e., the various functional managerial positions in a busi-ness have each their generic set of associated critical success factors.

Critical success factors can thus be characterized by the extent to which they are internal orexternal to the business, or that part of it over which the manager has control, and, con-sequently, whether they refer to something which should be monitored or built. Maintenanceof technological leadership would be a source of critical success factors which the businesscan build, while changing consumer demographics would be a force that can be monitored,but not controlled (for more details on critical success factors in management informationsystems, see Boynton & Zmud, 1984; Ferguson & Dickinson, 1982; Munro & Wheeler,1980).

Rockart has proposed a two-step interview method for ascertaining critical success factors.First there is a round of open interviews, where managers are asked about their views on thecritical success factors relevant for the business. On the basis of these, a preliminary list offactors is compiled, which then, in a second round, are rated on an importance dimension(e.g., Boynton & Zmud, 1984; Rockart, 1979).

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Chief executive of a majoroil company

1. Decentralize organiza-tion.2. Improve liquidity posi-tion.3. Improvegovernment/business rela-tionships.4. Create better societalimage.5. Develop new ventures.

President of a store furnis-hings manufacturer

1. Expand foreign sales forproduct lines B and C.2. Improve market under-standing of product line A.3. Redesign sales compen-sation structure in threeproduct lines.4. Improve productionscheduling.5. Mechanize productionfacilities.6. Strengthen managementteam.

Director of a governmenthospital

1. Devise method for obtai-ning valid data on currentstatus of hospital operations.2. Devise method forresource allocation.3. Manage external relationship.4. Get acceptance of con-cept of regionalization byall hospital directors.5. Strengthen managementsupport, capability, andcapacity.7. Improve relationshipwith governmentdepartment central office.8. Meet budgetary constraints.

Division chief executive ofan electronics company

1. Support field sales force.2. Strengthen customerrelations.3. Improve productivity.4. Obtain governmentR&D support.5. Develop new products.6. Acquire new technologi-cal capability.7. Improve facilities.

Figure 1: Example for results from a critical success factor study (from Rockart, 1979)

Figure 1 shows an example of results from such an investigation (from Rockart, 1979). Theexample shows several things. First, the factors identified are formulated at the level of indivi-dual managers, because this is where critical success factors are conceptualized and assessed.From there, key success factors at the levels of department or business can be inferred, basedon those which are consistently mentioned across respondents and departments (Boynton &Zmud, 1984). The formulation of such factors at the market level, however, has not beenaddressed, and it is not clear how they can be related to the task environment of the market.Also, the factors are not explicitly linked to a success measure, but seem implicitly to be takenas preconditions for business success in some sense.

In evaluating the contribution of Rockart and his co-workers, the aim of that work must be keptin mind. The aim was to develop management information systems which would be both usedby and useful for top management. The concept of critical success factors and the way toascertain them by direct questioning helped managers realize and articulate their informationneeds. Rockart was not concerned with explaining success or with the creation of competitiveadvantage.

Hence Rockart's concept of critical success factors can not immediately be transferred to thearea of business strategy formation. From the viewpoint of business strategy formation, his cri-tical success factor concept has a number of deficiencies. These should not be held as a criti-cism of Rockart's work, but as a criticism of uncritically applying the same concept in the con-text of business strategy.

From the viewpoint of strategy formation, the concept of critical success factors remains vague.The four sources of critical success factors cover a wide range of very different factors. Theyinclude factors which affect all businesses in an industry in the same way, as well as factorswhich can account for the differences in performance within an industry - where the latter haveconsiderably more strategic importance than the former. They include, mirrored in the build vs.monitor distinction, factors which the business can influence, and factors which it cannotinfluence. From a strategic viewpoint, it may be more appropriate to distinguish more sharplybetween environmental forces and business competencies - where building up a business com-petence may be a reaction towards monitoring a development in the environment.

Rockart's interview method may be appropriate in the context of designing managementinformation systems, since basing such a system on the executives' own perceptions willincrease their motivation to use it later. From the viewpoint of strategy formation, however, itmust be noted that executives perceptions of critical success factors may be wrong, leading tocatalogues of critical success factors which lack external validity. Numerous psychologicalmechanisms are known which cause decision-makers to misattribute causes of success(Barnes, 1984; Chakravarti, Mitchell & Staelin, 1981; Hogarth & Makridakis, 1981; Ste-venson, 1976).

The critical success factor concept in the management information systems literature can there-fore only be used as a source of inspiration for theory and method development in strategyformation.

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Key success factors in strategy research

Research in the area of strategy formation varies widely with regard to meta-theoreticalassumptions, including, among other things, the view on causality. Since a key success factoris a statement about a causal relationship, namely between success and some cause of success,the way the key success factor concept is used varies between the various schools of thoughtas well.

For the sake of simplicity, only three schools of thought will be distinguished in the following(Grunert, in press; but cp. Mintzberg, 1990a). These are the design school, the planningschool, and the shared-experiences school.

Key success factors as a business characteristic. The design school maintains that every busi-ness is unique in all aspects, and every business therefore also has to find its own unique mat-ch with its environment. It is therefore not possible to generate general statements about thecauses of success, and the term key success factor, if it is used at all, can have meaning onlywith regard to a single business. Consequently, the only form of research methodology appli-cable for finding key success factors is the case study (see Christensen et al., 1987; and thecritique by Mintzberg, 1990b).

Indirectly, causal statements still have their place, also in the design school. The use of thecase method in business education, as promoted by this school, builds on the idea that, bylooking at many cases, one develops an understanding, an intuition for business ecology,which, in future problem solving situations, will improve the ability to look at the right cuesin the environment and come up with the right strategic decision. In this way, general causalstatements about success are possible, but they cannot be made implicit, because they areburied in the decision-makers intuition, i.e., they have become automatic, perceptual andintellectual skills.

The basic philosophy of the design school is therefore not compatible with the aim of establis-hing explicit general statements about the key success factors in an industry. The key successfactor concept can be used in training business executives, but not in building up a generalbase of knowledge that could be communicated to the business community. In the final ana-lysis, the design school maintains that research in the area of strategy formation is not pos-sible.

Key success factors as a planning tool. The planning school aims at developing planninginstruments which help businesses in finding the right strategy. A main assumption is that, byproviding input which helps decision-makers in structuring their thoughts, the quality of deci-sion-making can be improved. Encouraging decision-makers to reflect their own assumptionsabout the causes of success - key success factors as a planning tool - is one possible example.It is hence assumed that decision-makers have theories of their own, subjective theories lin-king success to its causes (see, e.g., Ferguson & Dickinson, 1982; Hofer & Schendel, 1978;Ohmae, 1982). Neither the theoretical foundation for the concept nor the method for mea-suring it are central to the discussion. With regard to method, an eclectic approach is preferred

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(e.g., Leidecker & Bruno, 1984), with more emphasis on variety than validity. It is the processof building a consideration of key success factors into planning, which improves strategyformation. It is emphasized that key success factors are small in number. By forcing the deci-sion-maker to identify and concentrate on a small number of factors, a process of reasoning isstarted which, by virtue of its systematic nature, actually may lead to improved strategy-forma-tion - even though this has never been demonstrated empirically.

The planning school, just as the design school, puts emphasis on improving the skills of deci-sion-makers. However, since decision-making in the planning school is not primarily related tounconscious intuition, but to conscious problem-solving, the instruments and methods devisedare potentially useful to contribute to a general body of knowledge as well. It becomes possibleto obtain general knowledge on how decision-makers tackle their tasks, and also how theirproblem-solving can be improved. Specifically, it becomes possible to obtain knowledge aboutdecision-makers perceived success factors, about how these perceptions are shaped, and howthey influence strategy formation.

It would be still more exciting, if perceived success factors could be compared to actual suc-cess factors. The latter is what the last view of key success factors is about.

Key success factors as a market description. The shared experiences school maintains that thearea of business strategies is amenable to research aimed at finding nomological statements. Itis believed to be possible to find out how different strategy types are linked to business successunder various conditions. This school can be called the shared experience school, because itbuilds on the expectation that, if experience on business strategies is shared, it becomes pos-sible to build up general, empirically based theoretical knowledge, which then can guide theselection of business strategy. For this school, business success is governed by causal rela-tionships, which exist as an objective truth, and which gradually can be uncovered by research.

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Figure 2: The shared-experiences view on key success factors (from Sousa de Vasconcellos &Hambrick, 1989)

Yes - High Performance

No - Low Performance

Technologyand Market

Context

Key SuccessFactors

Firms'sStrengths

Match ?

Industrial organization theory, and its transfer to the area of strategy formation (e.g., Luchs,1986; Porter, 1980, 1985) therefore provides a source of hypotheses on key success factors,and also a methodological tradition for testing these hypotheses.

As noted, hypotheses can also be inductively established, based on exploratory empirical re-search instead of theory. Studies on factors distinguishing successful from unsuccessful busi-nesses (like the well-known Peters & Waterman, 1982, book) are an example of this: basedon the expectation that there are some general causes for success, possibly such causes areidentified in the comparison process, without establishing that these factors have indeed beencausal.

This fourth view on key success factors is certainly the most ambitious one, and hence alsofaces the most severe obstacles. In empirical studies which have aimed at testing hypothesesabout key success factors, the amount of variance explained in the performance measure hasoften been low. At least two groups of reasons can be given for this. The first is concernedwith the general problem of causal inferences in non-experimental research. Lacking the con-trol mechanisms of an experiment, causality must be established based on covariation, tem-poral sequence, and simultaneous measurement of all possible competing explanations.Especially the latter is often difficult to achieve. In addition, both success and factors leadingto it are often difficult to operationalize, and often little is known about the reliability andvalidity of the measurements.

While these problems may be overcome by using a more sophisticated methodology, asecond group of problems may be still more difficult to solve. They are connected to thedynamic nature of causes of success in a market (Chamberlain, 1968). Success factors maybe transient, and this problem will be more serious, the more concretely the factors areformulated. In addition, recognition of the success factors operating in a market by the actorsin that market, may actually change them: when everybody invests into the same skills andresources, the ability of variation in these skills and resources to explain variation in successwill necessarily decline.

3. Key success factors and competitive advantage: An extended view

In the following, we will attempt to sketch an extended view on key success factors, whichincorporates both the market description and planning tool views mentioned above. Thistakes into account that, on the one hand, a planning tool's usefulness necessarily depends onthe validity of the insights it creates, so that a planning tool aiming at a better understandingof market conditions should preferably rest on a methodology which aims at a valid marketdescription. On the other hand, valid market descriptions, when used by decision-makers,may result in the very object of the investigation, namely the market, changing.

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Meta-theoretical assumptions

We start with a few meta-theoretical assumptions. These are necessary in order to developtheory and method. Like all such assumptions, they cannot be proved empirically. Instead,their validity is dependent on how convincing they are to other researchers.

We start by defining success as a business's performance, with regard to some performancecriterion like return on investment or profit, which is superior to the performance of otherbusinesses it is competing with. The task of explaining success thus becomes the task ofexplaining variations in performance. Four meta-theoretical assumptions will be presentedwith regard to this task. The main ideas are also summarized in figure 3.

• Assumption 1: Determinants of performance in a market are sufficiently stableover a time span of sufficient length to make them amenable to statistical analysis.

This assumption is necessary, if an analysis of differences in performance and its determinantsin a market shall be possible at all. The assumption rules out the possibility that markets areso turbulent that no regularities whatsoever can be observed, or, put in another way, that futuredeterminants of differences in performance have no relationships to past determinants. Thevery fact that markets exist and businesses survive speaks for the assumption and against theextreme view of market turbulence, because assumption 1 is not only a prerequisite for anyscientific analysis of business performance, but also for any experience-based learning in apractical context.

Assumption 1 does not claim that differences in business performance can be completelyrelated to determinants, which have the mentioned stability. It only claims that such determi-

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Figure 3: Meta-theoretical assumptions

nants have an impact on performance which is sufficiently large to warrant their investigation.A certain proportion of the variance in performance may nevertheless be due to factors whichare either random or of such short duration that they are difficult to analyse empirically. Also,aggressive initiatives by major actors may occasionally change key success factors in a market(Ohmae, 1982). It is assumed that such changes are comparatively rare.

The time span in which a determinant of differences in performance is stable may vary consi-derably, and in general one may expect a trade-off between the level of generality vs. concrete-ness of the determinant and the time-span in which it is stable: the more general the determi-nant, the longer the time span. Generality is, of course, linked to information content: the moregeneral the factor, the lower the information content. At the extreme, a factor may be so gene-ral that it is true over a long time period almost by definition. One example may be market ori-entation as a determinant of performance.

We are concerned only with those determinants of performance which are actionable from theviewpoint of the business. This, however, is no real restriction, because, in the long run, mostdeterminants are actionable. Having a well-established brand name or a convenient downtownlocation may not be actionable in the short, but certainly in the long run. Likewise, we dropthe build/monitor distinction developed by Rockart, because external market forces whichaffect all businesses in a market in the same way do not contribute to the explanation of thevariance in business performance within that market. However, actions taken in response tosuch forces can explain variance, and are hence potential determinants of performance. Thus,demographic developments in the market are no cause of superior performance, but productdevelopment in response to such developments may be.

• Assumption 2: Actual and perceived determinants of differences in performanceare interdependent.

Business executives will usually perceive the causes of differences in performance in amarket, but this perception will be imperfect, distorted by the limitations of human informa-tion processing capacity, various cognitive biases, the impossibility to know the consequencesof actions which have not been taken etc. Thus, the perceived causes of differences in per-formance will be related to, but not be identical with, the actual causes of differences in per-formance.

While the actual determinants of differences in performance thus influence the perceiveddeterminants, this also works the other way round. The actions which business executives takeon the basis of the perceived determinants will influence the actual determinants. If everybodyin a market perceives that broad distribution coverage is a cause of superior performance, andeverybody indeed achieves broad distribution coverage, then there is no more variation in thisfactor, and it will no longer be able to explain differences in performance between the compe-ting firms.

• Assumption 3: Analysing actual and perceived determinants of differences in per-formance will improve the actors' understanding of the market.

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What this assumption expresses is the belief that, here as otherwise, the scientific method issuperior to everyday observation. A scientific investigation of the determinants of perfor-mance can overcome some of the biases and deficiencies which account for the differencesbetween actual and perceived determinants. In this way, it can improve understanding of themarket. But also an investigation of the perceived determinants of performance can help theactors realize and make explicit their own assumptions about how the market works. Con-fronting these assumptions with the results from an analysis of actual determinants is apotentially powerful instrument towards improved understanding of the market.

• Assumption 4: Analysing actual and perceived determinants of differences in per-formance and providing feedback to the actors will change both actual and percei-ved determinants of differences in performance, albeit slowly.

As everywhere in the social sciences, measurement will change the object of measurement.Investigating perceived determinants of differences in performance, e.g., by interviewingbusiness executives, will result in them becoming more aware of their assumptions about the-se determinants, which means that these assumptions will change somewhat (Bullen &Rockart, 1981). Communicating results about actual determinants of differences in perfor-mance to executives will change these assumptions further. Since changed assumptions willlead to changed actions, which then may change the actual determinants as well, the latterwill indirectly also be affected by the investigation.

However, such changes will be slow. Perceptions, which are based on repeated own experien-ce, are not easily changed, especially not by reports about an investigation, the foundations ofwhich may be poorly understood, and especially not if this investigation leads to results whi-ch differ markedly from the conclusions based on own experience. Even when the percep-tions of determinants of differences in performance change markedly as a result of the inves-tigation, strategies and their implementation will change slowly, resulting in only gradualchanges in the actual determinants.

Frame of reference

We will now specify a general theoretical framework relating the concepts of actual and per-ceived determinants of differences in performance to the concept of competitive advantage.

Several writers (Day & Wensley, 1988; Petersen, 1991; Porter, 1980) have argued that causesof difference in performance within a market can be analysed at various levels. The imme-diate cause of differences in performance, these writers argue, can be reduced to two basicfactors: the value which customers perceive in the products/services offered, and the costsincurred in creating this perceived value. The more value customers perceive in a marketoffering relative to competing offerings, and the lower the costs in producing that value rela-tive to competing producers, the higher the performance of the business. Hence, businessesproducing offerings with a higher perceived value and/or lower relative costs than competingbusinesses are also said to have a competitive advantage in that market.

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It should be noted that value and costs can be both positively and negatively related, and thatthey can influence profits or other performance measures in various ways (Luchs, 1988; Phil-lips et al., 1983). Higher perceived value can allow a price premium, thus increasing ROI, butat the same time preventing high market share. In this case, it can also be expected to be asso-ciated with higher costs, even though, in some markets, producing high quality may actuallylower cost because of less waste. But higher perceived value can also lead to an increasedmarket share, which can lead to decreased costs, resulting in a higher ROI as well. Alterna-tively, high market share may be reached by low prices, based on low costs.

There is thus no one-dimensional measure of competitive advantage, and perceived value andrelative costs have to be assessed simultaneously. Given this two-dimensional nature of com-petitive advantage, it will not always be clear which of two businesses will have a competitiveadvantage over the other. In figure 4, business B will clearly have an advantage over businessA in case I, and clearly have a disadvantage in case IV, while cases II and III do not immedia-tely allow such a conclusion. Business A may have an advantage in case II, if customers in themarket are highly quality-conscious, have differentiated needs, and low price elasticity, whilebusiness B may have a similar advantage in case III when customers mainly demand basicfunctionality, have homogeneous needs, and high price elasticity.

Even if A has a clear competitive advantage over B, this may not necessarily result in a higherROI for A, if A has a growth and B a hold policy. Thus, performance would have to be mea-sured by a combination of ROI and capacity expansion, which can be regarded as postponedROI (Petersen, 1991).

While the relationship between perceived value, relative costs, and performance is rather intri-cate, we can retain the basic statement that these two variables are the cornerstone of competi-tive advantage, since both of them, everything else equal, will be monotonically related to per-formance.

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Figure 4: Perceived value, relative costs, and competitive advantage

perceived value

higher for B higher for A

relative

lower for B I II

costs

lower for A III IV

I II

IVIII

relative costs

lower for B

lower for A

perceived value

higher for B higher for A

The perceived value created, and the costs incurred, will depend on the business's skills andresources, which thereby become second order determinants of differences in performance.We may distinguish three kinds of such skills and resources, as shown in figure 5. Core skillsand resources are those which are necessary in order to be able participate in the market. Thusthe competitors in a market will not usually be very different with regard to these skills andresources, and they will not explain differences in created perceived value and in relativecosts, and in the resulting performance. In Varadajaran's (1985) terms, they are failure preven-ters, but not success producers. They may, however, act as barriers to entry for potential newcompetitors, and hence raise the average level of performance in the market. Slack skills andresources are no prerequisite for being in the market, and do not explain large portions of thevariation perceived value and/or relative costs either. Finally, there is a third class of skills andresources, differences which explain the bulk of the variance in perceived value and/or rela-tive costs. These we may call key success factors.

Key success factors are hence regarded as those skills and resources which have the highestleverage on value and costs. Competitive advantage, and indirectly business performance, cantherefore be related to how the business scores with regard to these skills and resources, which,according to the terminology from the preceding section, can be regarded as the actualdeterminants of differences in performance. How the business scores on these characteristicswill, in turn, depend on whether the business has adopted a strategy which implies investinginto these skills and resources. And this will depend on the perceptions of the decision-makers- of the perceived determinants of differences in performance, but also of the perceived skillsand resources in the business. It is possible, e.g., that decision-makers believe certain skills andresources to be key success factors, because they actually were key success factors earlier, even

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Figure 5: Skills and resources

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M A P P

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Figure 6: Key success factors and competitive advantage

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though they now, because of changed market conditions, have become slack resources. Thiswhole process is depicted in figure 6, which provides a framework for relating actual andperceived determinants of differences in performance to strategy, implementation, and actualperformance.

Key success factors can be distinguished on two dimensions which have implications for theattainment of competitive advantage. These are their changeability and whether they are con-junctive or compensatory.

Conjunctive key success factors refer to skills and resources which are necessary conditions forsuperior performance in a market. The performance of a business will always be related to thedegree to which it has these skills and resources, and a lack of skills and resources here cannotbe compensated for by superior skills and resources in other areas. Compensatory key successfactors refer to a set of skills and resources, where businesses can choose to emphasize one orseveral of these. Lower scores with regard to some of these factors can be compensated for byhigher scores on other factors. The compensatory key success factors can hence be the startingpoint for strategic grouping: Businesses, which have decided to build the same set of compen-satory key success factors compete with each other based on the same set of skills andresources, and can be considered a strategic group.

Changeability refers to how fast it is possible for a business to acquire the skill or resource inquestion. The lower the changeability, the more permanent will be the competitive advantagefor the business having that skill or resource. When the changeability of a compensatory keysuccess factor is low, entry barriers with regard to the corresponding strategic group will behigh. Thus, the changeability concept refers to Porter's (1990) advantages of high and lowerorder.

M A P P

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Figure 7: Example of key success factor profile

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Figure 7 shows a fictitious example of business profiles with regard to a set of key successfactors. Customer orientation is the conjunctive factor, whereas the other factors are compen-satory. Businesses 1 to 3 form a strategic group competing on technology, whereas busi-nesses 4 and 5 form a strategic group competing on communication ability.

The figure is instructive with regard to the analysis of differences in performance. Businesseswithin a strategic group will have very low variation on the key success factor defining thatgroup. By analysing differences in performance within that group, this factor would not evenshow up as a success factor - only by analysing differences in performance across groups willthis factor be identifiable as a success factor. Thus, analyses both within and across groups areof interest.

The analysis across groups will give the complete picture of key success factors, allow todistinguish between conjunctive and compensatory factors, and give information on the rela-tive importance of the various factors. Differences in performance across groups will hencebe explicable with regard to how the businesses fare on the conjunctive factor(s), and by whi-ch strategic group they belong to. Thus, in the example, business 2 would be expected tohave lower performance due to its low customer orientation, regardless to which group it be-longs. Whether businesses 1/3 have higher or lower performance than businesses 4/5 wouldthen be explained by the relative importance of technology versus communication ability.

Differences in performance within strategic groups would primarily be related to the con-junctive factor(s), i.e., customer orientation in the example. Remaining differences would beexplained by how the businesses score on the key success factors which have not receivedstrategic emphasis. Thus, in the group competing on technology, business 2 would be expec-ted to fare worst, while the relative performance of businesses 1 and 3 would depend on therelative importance of sales force and communication ability.

Key success factors and auxiliary skills and resources

Figure 7 depicts success factors as a simple first-order system of causal relationships. This is,of course, a drastic simplification. A resource or skill, which has a major impact on perceivedvalue and/or relative costs, will be related to other resources or skills, resulting in a potential-ly rather complex network of causal relationships.

Figure 8 gives a fictitious example from a food product market. Imagine that having a greenimage is a resource which, on the market in question, contributes considerably to perceivedvalue, and can therefore be called a key success factor on that market. Several other skillsand resources can contribute to a green image: being located in a clean, natural environment,having contractual relationships with primary producers of organic food, whether one wasamong the first to support environmental groups, experience with processing recycled materi-al, or communication ability. Most of these skills/resources will also be related to relativecosts.

M A P P

-15-

Depending on the level of abstraction chosen for the analysis, such networks may be more orless complex. But a key success factor will always be embedded in a network of other rele-vant skills and resources. For analytical and practical purposes, we propose that the term keysuccess factor be reserved for those skills and resources which have a direct and sizableimpact on perceived value and/or relative costs. Hence, in the example, green image is a keysuccess factor, while location is not - only when the remote location in clean, natural sur-roundings is used to contribute to the building or maintaining of a green image will it contri-bute to higher performance (Location may, of course, be a key success factors, if it were amajor cost-determining factor). In this way, the term key success factor is reserved for thoseskills/resources which have the highest strategic leverage, in that a decision to invest in theseskills/resources will have a strong impact on perceived value and/or relative cost, and, at thesame time, point to other skills or resources needed to build the key success factor. Theseother skills and resources can be termed auxiliary skills and resources.

M A P P

-16-

Figure 8: A hypothetical causal structure on a key success factor embedded in auxiliary skillsand resources

one of the first companies to

support environ-mental groups

contractualrelationships to

primary producersof organic food

remote locationin clean,natural

surroundings

greenimage

experiencewith processing

recycledmaterials

communication ability

relativecosts

perceivedvalue

profit market share

performance measure

positional advantage

resources

skills

It should be noted that the distinction between compensatory and conjunctive factors can beapplied to auxiliary skills and resources as well. In order to build up a green image, stablerelationships to primary producers may be a necessary condition, while the first mover advan-tage of having supported environmental groups first may be substituted by a remote location.

Figure 9 shows an extension of figure 5, which takes into account that skills and resourceshave "depth", i.e., are causally related to each other as in figure 8, and visualizes the distinc-tion between key success factors and auxiliary skills and resources.

Definition and research agenda

It is now possible to pull our view of key success factors together to a definition.

A key success factor is a skill or resource that a business can invest in, which, on the marketthe business is operating on, explains a major part of the observable differences in perceivedvalue and/or relative costs.

M A P P

-17-

Figure 9: Skills and resources - extended diagram

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Several characteristics of that definition should be noted:

• A key success factor is a causal relationship. It expresses a relationship betweenthe competitive advantage a business enjoys in a market, in terms of perceivedvalue and relative costs, and the causes of that competitive advantage, in terms ofcertain skills and resources.

• Since a key success factor is a skill or resource of the business, it is always action-able.

• Key success factors are market-specific, but they transcend strategic groups in amarket.

• Key success factors are small in number. By definition, only a small number offactors can explain "a major part" of the variance in perceived value and/or rela-tive cost. This implies that there may be markets where there are no key successfactors, but only many small contributors to success.

• Key success factors imply a causal relationship between a skill/resource andperceived value and/or relative cost. They are hence not directly related to per-formance measures like ROI. This may be an advantage, since the relationshipbetween perceived value/relative cost and ROI may be intricate, and perceivedvalue/relative costs are better indicators of competitive advantage.

The last point does not preclude that it may still be meaningful to measure performance in theempirical research, if the aim is to estimate some more elaborate causal network like in figure8, which involves the link from perceived value/relative costs to performance.

Our view of key success factors leads to the following four main research tasks for an empiri-cal analysis of key success factors in a market:

• Measure the perceived key success factors. This is mainly a qualitative task, aimedat unveiling business executives' explicit or implicit hypotheses about the causesof superior perceived value and lower relative costs. If desired, a measure ofsubjective relative importance can be added, and perceived auxiliary skills andresources could be measured as well.

• Find additional hypotheses about actual causes of superior perceived value andlower relative costs. Since business executives' perceptions can be flawed, it isimportant to look for other sources of hypotheses about key success factors. Suchhypotheses may come from theory, industry experts etc.

• Measure how businesses score on the potential key success factors. This is a quan-titative task, involving finding objective measures for the skills and resourcesappearing in the business executives' perceptions of key success factors, and in the

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additional hypotheses found. If desired, measures of auxiliary factors can beadded.

• Measure perceived value and relative costs. By relating these measures to theobjective measures of skills and resources, it is possible to identify key successfactors and classify them as compensatory or conjunctive. If desired, measures ofperformance can be added.

These four research tasks clearly call for a combination of research methods. A more detaileddiscussion on suitable methods will be taken up in section 5.

4. Key success factors and market characteristics

Key success factors have been defined as business skills and resources with high leverage onperceived value and relative costs, and they have been defined as market-specific. When keysuccess factors are thus defined as business variables, they must be in correspondence to somemarket characteristics, which mandate that some skill is a key success factor in one but not inanother market. If an analysis of key success factors should not only be a market description,but contribute to a more general theory about competitive advantage, then it should be linkedto an analysis of market characteristics. Knowledge about the joint determination of superiorperformance by key success factors and market characteristics would then lead towards a con-tingency theory of competitive advantage.

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Hypotheseson MCs andKSFs

Selectmarkets basedon MCs

Assess actualKSFs

Assess percei-ved KSFs inmarket

Assess actualKSFs inmarket

Develophypotheses onMCs and KSFs

Investigatenext market

deductive mode:

inductive mode:

Figure 10: Inductive and deductive mode of investigating key success factors

The problem of relating key success factors to market characteristics can be tackled in twoways: deductive and inductive (see figure 10). The study by Sousa de Vasconcellos and Ham-brick (1989) is an example of the deductive mode: Two market characteristics and a large listof skills and resources were combined with theoretical reasoning to develop hypotheses onwhich of these skills and resources would be key success factors under which combination ofmarket characteristics. These hypotheses were then tested by finding markets, which corres-ponded to the combination of market characteristics to which the hypotheses referred.

We would like to argue that empirical research on key success factors could also be doneusing the inductive approach, since a contingency theory of competitive advantage is, at thepresent time, a theory development goal, not a solid basis for the deduction of hypothesis.Inductive research would start by analysing key success factors in a market, both perceivedand actual, and on that basis develop hypotheses on the market characteristics which haveresulted in the fact that these skills and resources have become key success factors. Thesehypotheses can then be investigated by looking at additional markets, which differ on thehypothesized market characteristic. While key success factors can be analysed within amarket, an analysis of the relationship between key success factors and market characteristicsalways requires an analysis across markets, in order to be able to review the presence of inter-actions between skills and resources and market characteristics in explaining differences inperceived value and/or relative costs.

The need for more inductive research is also mirrored in the present state of morphologies onskills and resources and market characteristics. Figure 11 shows a sample of these. Such mor-phologies have mainly come from the industrial organization and strategy formation literature.Many, especially of the earlier ones, did not distinguish between performance, first order cau-ses of differences in performance, i.e., perceived value and relative costs, and second ordercauses of differences in performance, i.e., skills and resources in the terminology used here.Thus, market share has been mentioned both as a resource and as a performance indicator. Wehave, in figure 11, chosen examples of morphologies which regard skills and resources in thesame way as we do in this paper (other examples can be found in, among many others,Buzzell & Wiersema, 1981; Hatten, Schendel & Cooper, 1978; Imel & Helmberger, 1971;Miller & Friesen, 1977; Shepherd, 1972).

The skills and resources morphology by Petersen (1991) is a good example of one in the indu-strial organization tradition. Although it is very well-reasoned, and draws on the strategy lite-rature as well, it is mostly geared towards explaining differences in efficiency, and not ineffectiveness. The skills and resources he mentions will mostly lead to cost advantages, andnone of the categories in his list refer explicitly to a skill or resource which allows or mirrorsbetter understanding of customers, leading to higher perceived value.

Porter's (1985) value chain, the perhaps best known example of a typology of skills and re-sources, is more balanced with regard to potential contributions towards both value and costs.It follows a classical functional dissection of the business, however, and is not explicitlymarket-oriented either.

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Figure 11: Morphologies of skills and resources and market characteristics

Petersen (1991)

(skills and resources)

locational advantages

production factorsphysical infrastructuresocial infrastructuresuppliers and purchasers

scale and scope economies

scale in productionscale in transactionsscope in productionscope in transactions

first-mover advantages

productivity gains through experience

employee motivation

monopsonistic bargainingpower

Sousa de Vasconcellos &Hambrick (1989)

(skills and resources)

information and communication

imagetechnical knowledge of the sales forcemarketing knowledge of the sales forceadvertising and sales promotion

product

product R & Dservice

product cost

process researchfirm sizecustomer financing

product delivery

distributionlocation of manufact-ring facilities

production

technical skills of workforcequality controlproduction managementpurchasinglabor relationstechnical sophistication of the equipment

Hofer (1975)

(market characteristics)

supplier variables

degree of supplier ofconcentrationmajor changes in availability of raw materialsmajor changes in conditions of trade

industry structure variables

type of productdegree of product differentiation# equal productsprice/cost structureeconomies of scaledegree of automationdegree of integrationexperience curvesmarginal plant sizeoptimum plant sizerate of product technological changerate of process technological changetransportation and distribution costsbarriers to entrycritical mass for entry

competitor variables

degree of seller concentrationaggressiveness of competitiondegree of specializationin the industrydegree of capacity utilization

market and consumer behaviorvariables

stage of the life cyclemarket sizeseasonalitycyclicalitymarket segmentationbuyer concentrationbuyer needsbuyer loyaltyelasticity of demandpurchase frequency

Porter (1985)

(skills and resources)

inbound logistics

operations

outbound logistics

marketing & sales

service

firm infrastructure

human resource management

technology development

procurement

The list of skills and resources used by Sousa de Vasconcellos and Hambrick (1989) is moremarket-oriented than the previous two, but, at least according to the authors' intention, limi-ted in its applicability to mature industrial markets.

As for morphologies of market characteristics, much of the strategy literature has concentratedon just one or a few, like, e.g., stage in product life cycle (Fox, 1973; Wasson, 1974), ortechnology and transaction complexity in the Sousa de Vasconcellos and Hambrick (1989)study. A rather comprehensive list of market characteristics with potential strategic importancehas been compiled by Hofer (1975), and this list is reproduced in figure 11.

Such lists as in figure 11 can serve as an inspiration for empirical research, but have certainlynot reached conciseness to such a degree that they can serve as a reliable basis for derivinghypotheses about the interaction of market characteristics and key success factors, underli-ning our call for more inductive research.

5. Methods for measuring key success factors

It is now necessary to relate the agenda for research in section 3 to some more concrete re-search methodologies. An empirical analysis of key success factors along the lines proposedin this paper has to draw on methods from several schools of research on strategy. The plan-ning school, with its emphasis on subjective methods, can provide ideas on how to measureperceived success factors and generate hypotheses on actual success factors. The shared-experience school, with its emphasis on objective measures, can provide ideas on how to testthe hypotheses thus generated, and hence establish the actual success factors. A combinationof methods is thus called for.

The research agenda presented above can be translated into two major methodological prob-lems: Finding the business decision-makers hypotheses about key success factors, i.e., theperceived success factors, and confronting the hypotheses found in this and other ways withreality, i.e., establishing causal relationships about actual success factors.

Finding the actors' hypotheses

Unveiling business executives' subjective theories about the causes of success can, in prin-ciple, be done through interviewing and/or through observation. A systematic way to tacklethis is the theory-in-use approach (Heffring, 1985; Skytte, 1990; Zaltman et al., 1982). Thisapproach actually has a still broader aim, in that it attempts not only to unveil subjectivetheories, but also their objective validity. If we reduce the sequence of steps advocated by thetheory-in-use approach to what is necessary to unveil subjective theories, then the followingsequence can be established:

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• identifying theory holders• find one or more success indicators• observe behaviour or practice aimed at success (this includes interviewing)• develop principles that describe the observed behaviour or practice, and specify

them as concepts and their linkages• try to find interrelationships between the various principles • try to map the subjective theory of both the theory holder and the researcher.

These are very general guide lines, which can help structure qualitative techniques like depthinterviews or in-depth case studies involving both interviews and observation. With regard tothe intricate problem of generating concepts from observational data, additional methodologi-cal advice is available from the discussions about grounded theory (Glaser & Strauss, 1967;Turner, 1981).

Some more specific instruments are potentially helpful in solving part of the task. Three suchinstruments will be mentioned.

Laddering is a methodology developed in consumer research, where it is used to measureconsumers' cognitive structures about consumer products. The main idea is that product prefe-rences are linked to concrete product attributes, which then may be linked to abstract productattributes, functional consequences, psycho-social consequences, instrumental values, andterminal values (Peter & Olson, 1990; Olson & Reynolds, 1983). Such a sequence is called ameans-end chain. Such chains are measured by having respondents generate concrete productattributes which differentiate products, ask for their preferences with regard to that attribute,and then keep asking "why do you prefer...", thus pushing up the respondent a ladder ofabstraction.

Laddering has many problems (Grunert & Grunert, 1991), but may be potentially useful inmeasuring decision-makers' subjective causal chains. The respondent could start by specifyingsome success criteria and how the main competitors differ with regard to these criteria. Then,the interview could keep asking "what is the reason for..." until the subjective causal chainseems to be exhausted.

Laddering has the potential advantage that it solves the qualitative and the quantitative tasks inmeasuring subjective success factors at the same time. It provides both lists of potential cau-ses of success and an estimate of the subjective strength of a causal relationship, at least in theaggregate over a group of respondents.

Sequence analysis, proposed by Grunert (1990) also in the context of consumer research, inprinciple leads to the same kinds of results as laddering: an estimate of a subjective networkof relationships. The main difference is that the method works on the basis of natural speechdata, like those that would result from a depth interview.

Rating prespecified causal networks is a simple method to estimate the strength of subjectivecausal relationships once the possible causes and effects are known. It has been used, e.g., to

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estimate the subjective causal maps about economic phenomena of people with different partyaffiliation (Lunt, 1989; Williamson & Wearing, 1991). The basic idea is to give people amatrix, where possible causes and effects represent rows and columns, and fill in the cellswith estimates of the strength of causal relationships.

All three methods have the advantage that they take into account that perceived successfactors may not be simple bivariate relationships, but more intricate causal networks. All threemethods generate matrices with cell entries measuring the perceived strength of a causal rela-tionship. This matrix can then be processed using network algorithms, leading to causal maps.

Establishing causal relationships

Estimating the actual success factors involves two basic problems:

• finding operationalizations of perceived value and relative costs, their potentialcauses, and other relevant variables,

• establishing causal relationships in a non-experimental setting.

With regard to the first problem, the difficulty is not so much that there are several potentialindicators for perceived value, or relative costs, or possible causes of it. On the contrary, mul-tiple measurements are desirable, since they tend to increase reliability. The problem is that itwill often be difficult to obtain "objective" measurements. We may distinguish three levels of"objectivity" (measurement of relative costs is used as an example):

• Relative costs could, in principle, be derived from the documented costs of thebusiness operations relative to the documented costs of the main competitors. Thiswould have a high degree of objectivity in the sense that the result would only to asmall degree depend on how the variable in question is perceived by an observer.However, such data are usually very difficult, if not impossible, to obtain.

• "Self-reported objective measures" (Dess & Robinson, 1984) imply that one relieson a business executive in giving relative costs figures, e.g., as a percentage whichexpresses the cost (dis-)advantage with regard to the three main competitors. Thishas a lower degree of objectivity, in that the original data is not inspected by theresearcher, who trusts in the validity of the figures given by the executive (whomay have estimated the figure without data base).

• Finally, subjective ratings of the variable in question, e.g. on a five-point ratingscale, can be used. The business executive would rate his business on a scale withpoles, e.g., "very high cost advantage" to "very high cost disadvantage". This kindof measurement can be regarded as having the lowest degree of objectivity.

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The second and third possibilities are widely used, e.g., in the PIMS data base. Such measuresopen many possibilities for measurement error (Grunert, 1990; Phillips, 1981; Venkatraman &Grant, 1986). However, by putting more emphasis on the psycho-metric properties of suchmeasures, such errors may be kept at an acceptable level. Actually, Parasuraman and Varadar-ajan (1988) have shown that PIMS data are astonishingly robust, and Dess and Robinson(1984) have documented reasonably high correlations between self-reported objective mea-sures and subjective ratings. Validity checks can always be obtained by having several respon-dents rate the same business, e.g., have competitors rate each other, etc.

Causal inferences in non-experimental research are an old problem in the social sciences (see,e.g., Blalock, 1964). It is now generally agreed that the following conditions should be metbefore one can maintain to have found a causal relationship in a field setting (Hildebrandt,1983):

• the two variables in question have to be correlated• there may be no demand characteristics because of the measurement device or the

study design• the two variables have to be temporally asymmetric, i.e., the effect has to be mea-

sured after the cause• alternative causes have to be measured as well and ruled out.

The first condition is usually taken for granted, since it normally starts the investigation of acausal relationship.

The second condition may be violated when, e.g., both perceived customer value and the busi-ness's market orientation are measured by ratings from the same expert. It is fulfilled whenperceived customer value is measured by customer ratings, and market orientation by a panelof experts.

The third condition can be met only by a longitudinal design leading to time series data. Inmany studies, this is impossible for practical reasons. If a cross-sectional design is necessary,the choice of the right length of time period, which the measures are meant to reflect, becomesimportant. As mentioned above, a very long time period is problematic because success factorsmay not be stable. A short time period may be problematic because of lagged effects; i.e., theeffect of a success factors occurs first in the next period.

The fourth condition can never be fully met, because one can obviously never make sure thatall relevant alternative explanations have been taken into account. However, by having a care-fully designed qualitative phase before going into the quantitative measurements, the problemcan be reduced. Based on a careful study of business executives' perceived success factorsplus a perusal of theory, the emerging list of possible candidates for success factors will mostlikely be rather complete.

Structural equation models (e.g., Hildebrandt, 1983) like LISREL are the most appropriatestatistical framework for an investigation of actual success factors. Structural equation models

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allow the simultaneous testing of complex causal relationships, and they allow multiple mea-surements of the variables in the model. They have previously been used in strategy research,for example in a study on generic strategies (Phillips et al., 1983), and in a study on successfactors in retailing (Hildebrandt, 1989). Their applicability may, however, be limited in induc-tive research, when the number of competitors on a market is small relative to the potentialnumber of key success factors.

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MAPP publications

MAPP working papers

No. 1: Grunert, K. G. & Baadsgaard, A. Market-based Proces and Product Innovation in the Food sector: A

Danish Research Programme, January 1992

No. 2: Thøgersen, J. Fødevareinnovation og Emballage - Miljøkonsekvenser og Forbrugerreaktioner, Marts

1992

No. 3: Bonke, J. Choice of Foods - allocation of time and money, household production andmarket services, Sep-

tember 1992

No. 4: Grunert, K. G & Ellegaard, C. The Concept of Key Success Factors: Theory and Method, October 1992

MAPP conference papers

Baadsgaard, A., Gede, M. P., Grunert, K. G. & Jensen, N. J. Lagged life cycle structures for food products: Their

role in global marketing, their determinants, and some problems in their estimation, July 1992

Grunert, K. G. Towards a concept of food-related life style, July 1992

Jelsøe, E., Land, B. & Lassen, J. Understanding consumer perceptions and priorities with relation to food quali-

ty, July 1992

Furthermore there are a number of project papers, which are not available to the public.

1. Strategic Planning and Innovation Capability in the Danish Food Sector Morten Kvistgaard & Kirsten Plichta, Copenhagen Business School;Lone Rossen, Biotechnological Institute

2. Innovation Capability as a Key Success Factor Klaus G. Grunert & Hanne Harmsen, The Aarhus School of Business

3. Flexible Manufacturing in the Danish Food-Processing Industry - How to Inte-grate Management, Marketing and Technology John Kjeldsen, The Aarhus School of Business

4. Definition of the Sales Potential for a New Food Product to be Launched on Home or Foreign Markets Anne Martensen & Lorentz Andersen, Copenhagen Business School

5. Primary Producers and Product Innovation in the Food Industry Villy Søgaard, University Centre of South Jutland

6. Controlling Processes of Production to Guarantee Process Characteristics Demanded by Consumers of Food Products: Paradigms and Danish Experiences Esben Sloth-Andersen, Aalborg University Centre

7. The Role of the Distribution System in Product Innovation Hanne Hartvig Larsen & Lasse Henningsen, Copenhagen Business School

8. Prototyping in the Danish Food Industry Preben Sander Kristensen, Aalborg University Centre

9. Product Quality and Consumer Preferences: Assessing the Optimum Design of Food ProductsKai Kristensen, Hans Jørn Juhl, Anne Bech & Erling Engelund, The Aarhus School of Business; Carsten Stig Poulsen, Aalborg University Centre

10. Product Innovation and Packaging in the Food Industry - Environmental Con-sequences and Consumer Reactions John Thøgersen & Tino Bech-Larsen, The Aarhus School of Business

11. The Consumer as Agent in Relation to Research and Development in Food Technology Erling Jelsøe, Birgit Land & Jesper Lassen, Roskilde University Centre

12. Households’ Choice of Foodstuffs with Different Kinds of Preparation Jens Bonke, University of Copenhagen

13. The Cultural Dimensions of Food Consumption and the Implications for Strategy Formation and Implementation in Small and Medium-sized Danish CompaniesDominique Bouchet, Josette Andersen, Søren Askegaard, Tage Koed Madsen &Per Østergaard, Odense University

14. Market Surveillance Systems for the Food SectorKlaus G. Grunert & Karen Brunsø, The Aarhus School of Business

15. Identification of Key Success FactorsKlaus G. Grunert & Charlotte Ellegaard, The Aarhus School of Business

The Mapp programme consists of the following 15 projects