Industrial Clusters, Knowledge Integration and...

World Development Vol. 32, No. 2, pp. 305–326, 2004� 2003 Elsevier Ltd. All rights reserved

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doi:10.1016/j.wor

Industrial Clusters, Knowledge Integration

and Performance

PIERO MOROSINI *International Institute for Management Development (IMD), Lausanne, Switzerland

Summary. — Scholars examining the phenomenon of industrial clusters have begun to regard themas social communities specializing in efficient knowledge creation and transfer, in addition to neo-classical arguments focusing on the advantages of localization. We seek to contribute to this bodyof work by developing the argument that both the degree of knowledge integration between anindustrial cluster’s agents and the scope of their economic activities are key dimensions behind theireconomic performance. We present a model that incorporates a hypothesized relationship betweenthese three dimensions and argue that a formal test of this hypothesis constitutes a promising areaof future empirical research in this field.� 2003 Elsevier Ltd. All rights reserved.

Key words — industrial clusters, knowledge integration, social fabric, economic linkages, business

competition, regional policy

*The author would like to express his thanks to IMD

Research Associates Deepak Khandpur and Sophie

Linguri for their assistance with this paper. The author

also thanks Carlos Alberto Schneider and Marcelo Otte

at CERTI Foundation, Florianopolis, Brazil, for their

help and support to our field research work. Final

revision accepted: 31 December 2002.

1. INTRODUCTION

That economic agents gather together inclose geographic proximity and establish rela-tionships with one another in order to betterperform certain economic activities is a factthat can be traced back to the earliest urbandevelopments. 1 Close-knit geographic clustershave remained a relevant economic phenome-non even at the dawn of the 21st century,nurturing some of the most successful playersacross a broad array of global industries,including––somewhat paradoxically––the sameinformation technology industries that give usthe ability to work and communicate virtually.Active membership of an industrial cluster

during the second half of the 20th centuryprovided one of the best opportunities for smalland medium-sized enterprises to survive andstay competitive on a regional, internationaland even global scale. At the same time, largeinternational, multinational and global com-panies risked losing entire parts of the valuechain in the key areas in which they com-peted––such as manufacturing, product designand Research and Development (R&D)––tonimble, formidable companies closely clusteredin specific geographic locations. Some of theselarge companies were also able to leveragethe enormous potential and capabilities that

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industrial clusters offer. Typically they did thisby locating key company operations in care-fully selected industrial clusters around theworld, or by using these industrial clusters ascritical innovators, e.g., from the R&D, sup-plier or customer perspective.In this paper we offer a knowledge-based

framework to facilitate understanding of thekey factors governing the way a cluster func-tions and its international success. Althoughthere are many publications on the subject––orperhaps because of this––both the use of theterm and the existing explanations of theunderlying phenomena of clusters can be ratherconfusing. As a result, regional policy makersand businessmen alike often find it difficult toaddress the potential threats as well as thepromising opportunities that these clustersprovide.In essence, we argue that two fundamen-

tal dimensions will allow both analyst and

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practitioner to gain a real grasp of the clusterphenomenon. On the one hand, by looking atthe nature and quality of a cluster’s underlyingsocial fabric, it is possible to understand itspotential for knowledge creation and innova-tion. On the other hand, by assessing the reachand scope of a cluster’s economic activities,it is possible to understand the forces drivingits competitive and business logic. Altogether,social knowledge, economic factors and theforces of business competition provide a fun-damental––if not exhaustive––understanding ofclusters in a dynamic sense. The implicationsof this understanding for macro- and micro-economic policy design and implementationcannot be overemphasized. In addition, anunderstanding of what makes clusters work inpractice is also critical for all firms competingin the global arena.The paper is organized, as follows. In Section

2, we provide a definition of industrial clustersbased on a comprehensive review of the majorliterature sources, from the early 20th centuryto more recent contributions. Significantly, inour definition of industrial clusters, social andknowledge-based elements are brought toge-ther more explicitly than they have in previousdiscussions.Building on this definition, we discuss the

major characteristics of industrial clusters indetail in Sections 3–8. These include: the socialnature of an industrial cluster’s knowledgeinteractions; the broad diversity of their socialfabric––including much more than purely eco-nomic agents; the key importance of locallyconfined relationships and specialized eco-nomic linkages for efficient knowledge creationand transfer; the ‘‘common glue’’ that bindsindustrial clusters together; and the competitivescope of industrial clusters in today’s increas-ingly interconnected, global milieu.In Section 9, we introduce a knowledge-

based taxonomy of industrial clusters thatbrings together the main elements examined inour previous discussions. This classificationprovides a way to assess the degree of knowl-edge integration of industrial clusters, which isunderstood to be a critical dimension behindtheir economic performance. In Section 10, weargue that the scope of competition of indus-trial clusters constitutes a second criticaldimension in understanding their economicperformance. Based on mainstream strategicmanagement concepts, we develop a frame-work to appraise the scope of an industrialcluster’s competitive dynamics from external

(market), internal (firm) and social (learning)dimensions.Finally, in Section 11, we build on the

foundations laid in the previous sections inorder to postulate the hypothesis that is centralto this paper, making a case for the linkbetween knowledge integration, the scope ofcompetition and the economic performance ofindustrial clusters. We conclude by arguing thata formal test of this hypothesis constitutes apromising, long-overdue area of future empir-ical research in this field.

2. WHAT ARE INDUSTRIAL CLUSTERS?

During the 1990s the explosion of specializedand popular literature on industrial clustersgave them an unprecedented relevance across arange of areas, including business managementand economic, political, public and social pol-icy. There was also a degree of confusion overwhat the various authors mean––and do notmean––by industrial clusters. Our first consid-eration therefore is terminology.It is important to point out from the outset

that we are not concerned here with the kindsof economic agglomerations found in largecities and urban developments of a certain size.As various authors have noted, large urbanrealities of necessity and almost inevitablyprovide opportunities for agglomerations ofsorts to emerge, human first, social and eco-nomic next (Gordon &McCann, 2000). Indeed,it is obvious to those familiar with large citiesand urban realities that economic interactionswithin these kinds of agglomerations are typi-cally governed by the logic of large numbersand random events. But, two basic kinds ofeconomic benefits that are important to ourunderstanding of industrial clusters can alsousually be found here.On the one hand, large cities and similar

agglomerations nurture urbanization econo-mies––in other words, economic advantagesthat stem from factors or conditions that bene-fit all economic entities and agents that are partof the agglomeration. For example, theimpressive air transportation facilities andinfrastructure of a city such as London, thestrategic geographic location of Athens forwest–east logistical links and the multiplicity oflinguistic skills present in Singapore can lead toeconomic advantages that can be enjoyed by allentities located in––or near––these large cities.

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On the other hand, urban agglomerationslead to localization economies of scale. Theseare specialized economic advantages stemmingfrom close geographic proximity that benefitspecific industries only. To follow the previousexamples, the City of London is one of theworld’s premier centers of financial talent in theform of tens––perhaps hundreds––of thousandsof highly skilled finance professionals. Thisworld-class talent pool presents obvious bene-fits for all financial services firms that decide tolocate themselves in London. Similarly, Athensand its close surroundings is one of the world’sleading hubs of people, firms, assets andinfrastructure specifically related to the ship-ping industry. The same can be said of Singa-pore, except that its shipping hub is perhapseven larger than that of Athens, with a greaterglobal reach.The idea of localized economies of scale in

geographic agglomerations has a long historyin economics, going back to Adam Smith’searly observations of labor specialization andto Marshall’s (1925) explanations of why firmscontinue to localize in the same areas. Marshallhighlighted three key explanations. First, firmsget close together geographically because thisallows them to develop a pool of specializedlabor that is highly skilled for the specific needsof an industry and relatively easy for the firmsin need of these skills to access. Second, thesefirms can provide nontraded input specific to anindustry, i.e. by localizing themselves in closegeographic proximity, the firms can experienceeconomies of scale in developing and usingcommon technologies or a particular capitalinfrastructure. Third, firms that join togethergeographically can generate a maximum flow ofinformation and ideas. In other words, prod-uct, market and technological knowledge canbe more easily shared and more effectivelyturned into valuable innovations betweenagents that are in close geographic proximitythan between agents that are more geographi-cally dispersed.It is interesting––and to some degree quite

paradoxical––that virtual communication tech-nologies and developments in global trans-portation and logistics during the 20th centuryhave made localization economies more––notless––critical to the competitive performanceof firms. On the one hand, virtual communi-cations and similar technologies have high-lighted tacit knowledge and close personalrelationships between economic agents as keydeterminants for the competitive success of

firms. On the other hand, global logistics meanthat access to basic production factors such ascapital and nonspecialized labor are largelyopen to all, whereas flows of specializedknowledge and rich knowledge interactionsthat lead to valuable innovations remainstronger between agents in the same spatialgroup than among geographically dispersedfirms.Our definition of ‘‘industrial cluster’’ includes

the Marshallian notions of urbanization andespecially localization economies of scale, but itclearly departs from the concept of agglomer-ations in that the knowledge interactions withinthe cluster are not random but rather deliber-ate, socially constructed and determinant for itscompetitive survival:

An industrial cluster is a socioeconomic entity charac-terized by a social community of people and a popula-tion of economic agents localized in close proximity ina specific geographic region. Within an industrial clus-ter, a significant part of both the social communityand the economic agents work together in economi-cally linked activities, sharing and nurturing a commonstock of product, technology and organizationalknowledge in order to generate superior productsand services in the marketplace.

3. INDUSTRIAL CLUSTERS ARE SOCIALENTITIES

The first thing to note is that our definition ofindustrial clusters states that it is the nature,quality and strength of a cluster’s underlyingsocial fabric that determines how it integratesexisting and new knowledge in order to createsuperior products and services. In essence, thisis what more clearly differentiates industrialclusters from simple geographic agglomerationsof economic agents. Gordon and McCann(2000, p. 520) observe:

The strength of [an industrial cluster’s] relationshipsis described as the level of ‘‘embeddedness’’ of the so-cial network. In fact, all economic relations (even the‘‘pure’’ market relations of the agglomeration model)are socially embedded in the sense that thesedepend upon norms, institutions and sets of assump-tions shared among a group of actors and are not, inthemselves, simply the outcome of economic deci-sions. [. . .] Industrial clusters (whether spatial ornot) differ from the agglomeration model in thatthere is a belief that such clusters reflect not sim-ply economic responses to the pattern of avail-able opportunities and complementarities, but alsoan unusual level of embeddedness and social integra-tion.


There is nothing inherently spatial about the social-network model although it has explicit spatial applica-tions. This is because social networks are a form ofdurable social capital, created (and maintained)through a combination of social history and ongoingcollective action.

Some authors in the sociological literature(Granovetter, 1992) have argued that industrialclusters can be considered as distinct from‘‘social networks.’’ Whereas the former arelargely dominated by constellations of eco-nomic agents linked by contracts (Pitelis, 1993;Williamson, 1985), the latter––these authorsargue––are dominated by intensive knowledgeinteractions between firms that are oftenstronger than intra-firm interactions. Althoughthese views can be seen as a sociologicalresponse to neo-classical economic argumentsin this area, conceptual parsimony wouldappear to be desirable for the purposes of thepresent study. Thus, in this research we preferto consider social networks largely as a parti-cular type of industrial cluster, in whichknowledge integration between firms as well asinstitutionalized trust and personal interactionsbetween economic agents are especially strong.Furthermore, differentiating the quality and

complexity of knowledge interactions withinindustrial clusters according to the type ofindustry––i.e. ‘‘high-tech’’ vs. ‘‘basic’’ or‘‘mature’’––seems to be based on rather feeblearguments and little empirical evidence. Indeed,a number of authors suggest that industrialclusters in central Italy formed around seem-ingly basic technologies such as tile manufac-turing demonstrate knowledge interactions thatare as socially complex, pervasive and innova-tive as any found in the biotechnology, tele-communications or computer software clusters(Gordon & McCann, 2000). The crucial differ-ence in this context seems to stem from thedegree to which the economic agents in anindustrial cluster decide to engage in purposefulcollaboration and continuous cooperationacross critical activities that are of commoninterest to all––while keeping the competitivemarket dynamics intact. Simultaneous cooper-ation and competition in a clearly definedgeographic area in turn requires a highlydeveloped social fabric that engages and facil-itates the integration of knowledge and com-munication exchanges as well as the fosteringof a common sense of identity among econo-mic agents. As a result––almost irrespective ofthe technological characteristics of any givenindustry––the degree of knowledge integration

that can be found developing in industrialclusters can be rather complex.There are certain costs associated with

industrial clusters that sometimes serve asobstacles to growth. Although there is certainlya degree of increased competition and conges-tion on both the demand and supply sides,industrial clusters can also experience highrates of employee turnover and noncooperationbetween firms, which can jeopardize the entirecluster. As mentioned, the way in which anindustrial cluster’s agents manage to orches-trate mutual cooperation while at the same timefostering greater competition might becomecrucial to the cluster’s long-term economicsurvival (Swann & Prevezer, 1996).

4. WHO BELONGS TO INDUSTRIALCLUSTERS?

Our definition of industrial clusters includesa close-knit social community of people and abroad set of economic agents, not just firms.Studies that look at Emilia Romagna’s indus-trial districts in Italy offer some of the moststriking characterizations of cohesive socialcommunities actively underpinning the eco-nomic strength of clusters (Becattini, 1990, p.39):

The most important trait of [an industrial district’s]local community is its relatively homogenous systemof values and views, which is an expression of an ethicofworkandactivity, of the family, of reciprocity, andofchange. To some extent all the main aspects of life areaffected by this. The system of values which prevails inthe district develops more or less quickly throughtime, in ways which are still to be explored: it consti-tutes one of the preliminary requirements for thedevelopment of a district, and one of the essential con-ditions of its reproduction. This does not imply thatonly one combination of values is compatible withthe beginning and the growth of the district, but ratherthat some combinations are apparently admissible,while others are not. Under no circumstance, how-ever, can the system of values be such as to discourageenterprise or the introduction of technical change. Ifthat were the case, the district could not be an entitywhich persisted through time, and we would have in-stead an area of social stagnation.

Parallel to this system of values, a system of institu-tions and rules must have developed in such a wayas to spread those values throughout the district, tosupport and transmit them through generations. Themarket, the firm, the family, the church and the schoolare some of these institutions; but they also includethe local authorities, the local structures of political


parties and of unions, and many other public and pri-vate, economic and political, cultural and charitable,religious and artistic bodies.

Industrial clusters also include a populationof economic agents––firms as well as individu-als––with specialized skills or knowledge rele-vant to the linked economic activities that arecarried out. In addition, these economic agentscomprise institutions such as universities,research centers, industry associations andtechnological institutes, which foster mutualeconomic cooperation and the sharing oftechnological knowledge among the membersof an industrial cluster. These kinds of institu-tions have been referred to as comprising an‘‘associational economy’’ (Schmitz, 2000) orconstituting a ‘‘meso-level’’ (Meyer-Stamer,1999) between the macro-level of economicpolicy and the micro-level of a firm’s competi-tion. 2

The available empirical evidence suggeststhat close-knit social communities are a signi-ficant factor behind the economic strengthand sustainability of industrial clusters (Pyke,Becattini, & Sengenberger, 1990). In addition,associational or meso-level agents have beenfound to be effective in promoting cooperationfor ‘‘good purposes’’ which is considered tohave a significant performance-enhancing effectfor firms located in advanced country regions.This is particularly the case for firms seeking tocompete successfully in international andglobal markets (Hudson, 1998).

5. WHY IS GEOGRAPHIC CLOSENESS SOIMPORTANT?

Furthermore, our definition of industrialclusters stresses the notion that the members ofsuch a cluster are localized in close proximitywithin a particular geographic region. We aretherefore concerned here with the types ofeconomic advantages strictly stemming from ahigh degree of geographic concentration amongfirms. These types of economic advantages havebeen well described in the classical and neo-classical economic tradition examining indus-trial complexes (Czamanski & Ablas, 1979;Feser & Bergman, 2000). As in the case ofindustrial clusters, industrial complexes candevelop internal economies of scale in terms ofspecific trading links and customer–supplierrelationships. Conversely, innovative firms canbe heavily dependent on local networking or

linkages to support their novel products andservices. Note that both of these advantages arestrictly dependent upon the close geographicproximity of the firms localized within anindustrial cluster. This is different from thetypical globalization economies, which reducethe importance of traditional localized factorsof production, or from the types of innovationsrelying on inputs that are unlikely to be locallyconfined (Simmie & Sennett, 1999).It must be noted, however, that geographic

proximity could bring as many disadvantagesto the members of industrial clusters as it pro-vides advantages. Disadvantages include thepoaching of specialized labor between firms,greater competition (which can also, however,be an advantage), faster imitation of bothtechnology and product innovations by com-petitors, and shared market intelligence amongfirms. Gordon and McCann’s (2000) study ofthe effects of geographic proximity on relatedactivities by industrial sector in London con-cludes that––out of 17 sectors examined––onlyprinting and publishing and financial servicesshow clear net advantages of proximity. Thistype of empirical evidence raises a fundamentalissue: how do members of industrial clusterswork together to balance the disadvantages andadvantages of geographic proximity?

6. HOW DO MEMBERS OF INDUSTRIALCLUSTERS COMBINE?

Our definition of industrial clusters high-lights that its members work together in relatedor linked business activities. Indeed, the scale-and knowledge-based advantages generatedwithin an industrial cluster stem from both thenumber and the nature of the particular link-ages between its members. In a well-developedindustrial cluster, these linkages can be numer-ous, unique and specialized to the industrialcluster, including:

––common customers (both firms and indi-viduals);––common suppliers and service providers;––common infrastructure such as transpor-tation, communications and utilities;––common pool of human talent such asskilled professionals or specialized labor;––common educational, training and coach-ing facilities and approaches for workers;––common university, research center andtechnology institute specializations;––common risk capital markets.


It has been shown that the number andeconomic value of these kinds of links canprovide an adequate indication of an indus-trial cluster’s strength (Feser & Bergman,2000). For example, empirical research bySwann and Prevezer (1996) suggests thatclusters in industries where multiple linkagescan be created among the member firms (suchas the computer industry) present significantlystronger growth patterns than clusters inindustries with much lower linkages betweenmember firms (such as the biotechnologyindustry).

7. WHAT BINDS INDUSTRIALCLUSTERS TOGETHER?

Next, our definition stresses that membersof an industrial cluster must share and nurturea common stock of product, technology andorganizational knowledge. Indeed, someauthors have described this critical character-istic of industrial clusters as constituting a‘‘social glue’’ (Porter, 1998) that binds thecluster together. Others have referred to a‘‘common glue’’ or an ‘‘organizational glue’’that socially amalgamates diverse structuralagents and integrates key knowledge acrosscultural, organizational and functional bound-aries (Evans, 1993; Morosini, 2002).On the one hand, Porter (1998, p. 88) high-

lights that:

The social glue that binds clusters together alsofacilitates access to important resources and infor-mation. Tapping into the competitively valuableassets within a cluster requires personal relation-ships, face-to-face contact, a sense of common inter-est, and ‘‘insider’’ status. The mere collocation ofcompanies, suppliers, and institutions creates thepotential for economic value; it does not necessarilyensure its realization.

On the other hand, Morosini (2002) identifiesfive key capabilities that need to be in place tobuild this ‘‘common glue’’ that realizes eco-nomic value within––and across––economicorganizations. When applied to industrialclusters, these key capabilities can be charac-terized as follows:

(a) Leadership––Well-functioning industrialclusters are deliberately amalgamated bygroups of key individuals with explicit rolesfostering mutual cooperation, knowledgesharing, leadership coaching and arbitration

of disputes that are seen as benefiting thecommon interests of the members of thecluster. These individuals are identified andtheir roles are explicitly accepted by all theagents that belong to the cluster. Manyauthors have documented these leadershiproles in the context of different industrialclusters. For example, in his role as industryassociation representative during the 1990s,the president of a large textile manufacturingcompany in southern Brazil’s Santa Cata-rina region led a five-year radical transfor-mation that turned the area from a fiercelycompetitive to a closely collaborative indus-trial cluster in this sector (Meyer-Stamer,1999). Other authors have observed thatCEOs and senior executives of establishedtechnology firms in Silicon Valley continu-ously identify promising young entrepre-neurs and spend time coaching andgrowing their leadership talents––often help-ing to appoint them to senior positions incompeting firms (Leonard & Swap, 2000).Moreover, leaders of large and small firmsin Mexico’s Guadalajara footwear clusterwere seen to have worked closely togetherin the 1980s and 1990s to design and imple-ment a comprehensive joint effort to over-come the dramatic effects of a series ofmacro-economic shocks such as large cur-rency devaluations (Rabellotti, 1999).(b) Building blocks––Well-functioning in-dustrial clusters have typically developeda clear, common stock of organizationalknowledge that is shared by all members,across functional, cultural and firm-specificboundaries. These building blocks typicallyinclude strong sociocultural ties among thelocal economic agents, creating a commoncode of behavior that facilitates trust and ac-tive collaboration; a common language, notjust in the literal sense but also encompass-ing common technological, business andorganizational terminology; a common indus-trial culture and atmosphere; a common phi-losophy and approach to developing humantalent and specialized labor; a common busi-ness understanding of the basic competitivedynamics of their industry; and common ap-proaches to competitive performance mea-surement (Meyer-Stamer, 1998; Rabellotti,1995; Simmie & Sennett, 1999). Empiricalevidence suggests that a common system ofsociocultural and economic values, alongwith a well-defined system of institutionsthat supports and spreads those values, is


associated with economically strong andmore innovative firms in an industrial cluster(Pyke et al., 1990).(c) Communication rituals––Within well-developed industrial clusters, there are regu-lar communication events, interactions andapproaches that continuously foster a com-mon sense of identity among all membersof the cluster. Meyer-Stamer (1999) de-scribes some of these communication eventsin the context of Brazil’s Santa Catarina tex-tile manufacturing industrial cluster, whichare fairly typical of such events in similarclusters elsewhere (Porter, 1998; Schmitz &Nadvi, 1994). In 1996, for example, the pres-ident of one of the largest textile manufac-turing firms in the area (mentioned earlier)organized key events such as visits for localowners and managers of textile firms toItaly’s textile clusters in order to learn bestpractices in interfirm relations and see world-class examples of supportive meso-levelinstitutions. Communication interactionsthat support the development of a commonsense of identity in industrial clusters includeproactive industry associations, commoninterest groups to lobby local or nationalgovernments and building a common imagethrough public relations initiatives and thelike. Finally, typical communication ap-proaches that foster a common sense of iden-tity among the members of an industrialcluster vis-�a-vis the outside world includedeveloping a common product- or quality-brand, as well as common and explicit qual-ity standards. Empirical studies have foundthat industrial clusters with well-developedcommunication events, interactions and ap-proaches have higher levels of interfirmcooperation and are more adaptable toabrupt changes in the macro-economic andcompetitive environment (Pyke et al., 1990).(d) Knowledge interactions––Well-function-ing industrial clusters muster a series ofregular, explicit and highly developed mech-anisms for sharing key technological andbusiness knowledge among all members.Typical examples include continuous bench-marking task forces (both within the clusterand across clusters); research centers, tech-nological institutes, universities, think-tanks,executive education and worker trainingschools that actively promote mutual coop-eration and technology transfers within theindustrial cluster and between firms; jointR&D, product design, manufacturing or

co-marketing efforts between firms; and ex-port and trading organizations both locallyand abroad. In well-developed industrialclusters, meso-level institutions such asindustry associations usually play a key roleas both initiators and managers of thesecoordination mechanisms. It is importantto note, however, that this role is substan-tially different from the conventional collec-tive bargaining, political lobbyist or contactand networking roles that these kinds ofassociations typically play within industriesor inside less developed industrial clusters(Swann & Prevezer, 1996). The availableempirical evidence suggests that industrialclusters with well-developed coordinationmechanisms show a significantly higher levelof cooperation between firms. In turn, co-operating firms within the cluster tend toperform better than noncooperating ones(Schmitz, 2000). Cooperation between firmscan often be facilitated by the economiccomplementarity between a cluster’s agents,which can extend upstream to suppliers,downstream to customers or laterally tomanufacturers (Porter, 1998). This allowsfor increased efficiencies (e.g. technologies,marketing channels), as well as additionalbenefits (e.g., the reputation of certain re-gions for industry excellence benefits all ofits members) and synergies (e.g., consumersof hotel services will value the entire experi-ence according to the quality of each compo-nent). 3

(e) Professional rotations––Within highlycompetitive industrial clusters, there is typi-cally a significant pool of human talent spe-cialized around business and technologicalknowledge that is specific to the cluster’smain economic activities. The degree ofcrossfirm mobility of these professionals lar-gely takes place within the geographicboundaries of the industrial cluster. Perhapsthe most conspicuous example of this phe-nomenon is in Silicon Valley, where the tal-ented and entrepreneurial individuals thatseem to abound in this cluster tend to be ex-tremely mobile, either across firms (after rel-atively short work experience, on average, inany given organization) or in order to startup their own enterprises. But, these movesusually take place within the geographicboundaries of the Valley (Leonard & Swap,2000). These characteristics have certainlyhelped give Silicon Valley some of its legend-ary clout and reputation in most people’s


minds. A flow of talented and skilled profes-sionals continually moving within an indus-trial cluster provides an effective andefficient vehicle for sharing tacit expertise(Bortagaray & Tiffin, 2000), best practicesand experiential knowledge across firms.No less importantly, it contributes to thedevelopment of new knowledge as well astechnology transfers, combinations and rep-lications across the cluster’s economicagents. Indeed, in this area empirical studiessuggest that firms in industrial clusters aremore likely to be innovative when there isa high degree of own-sector employment inthe cluster’s home region (Baptista &Swann, 1998). 4

8. THE GLOBAL SCOPE OF TODAY’SINDUSTRIAL CLUSTERS

Finally, our definition stresses that the ulti-mate goal of industrial clusters is to generatesuperior products and services that are valuableto customers in the marketplace. There are atleast two crucial points to be made about thisnotion. First, although an industrial clustermight benefit from some protective measures atthe outset, in the long-term, selection mecha-nisms that reflect the dynamics of businesscompetition must be in place (Porter, 1998).Otherwise, an industrial cluster will simply notsurvive, or it will do so as a result of economictransfers that are not necessarily market rela-ted, e.g., in the form of state subsidies or fiscalincentives.Second, industrial clusters have not only

proved extremely successful at creating realeconomic value nearly everywhere but havealso––during the second half of the 20th cen-tury––emerged as formidable global players intheir own right across an astonishing variety ofindustries. Therefore, although these industrialclusters are tightly confined geographically,their scope of competition is increasinglyglobal, not local. Whereas the market focus ofsome of these industrial clusters might remainlocal, global competition can nevertheless takeplace in the form of new entrants––which ofteninclude industrial clusters as well. Rabellotti(1999) describes the case of a Mexican shoe-making cluster which during the 1990s waspartly displaced in its main (local) market bycheap competing products from Chinese shoe-making clusters.

The global scope achieved by industrialclusters during the 1990s went far beyond theexport potential and international appeal of aspecific product range that in the past typi-cally made the fortunes of industrial clusterssuch as northern Italy’s tile manufacturers, toolmachinery producers or shoemakers. At thedawn of the 21st century, industrial clusterswere taking over entire areas of many globalindustries, such as manufacturing, R&D andproduct design. As a result, leading multina-tionals in industries ranging from computerhardware, semiconductors and automotivemanufacture to textiles, medical equipment andwatchmaking found themselves increasinglyusing industrial clusters to their benefit or––quite often––simply to enhance their chances ofcompetitive survival. These multinationalswould typically involve industrial clusterseither as leading suppliers or as key customersand innovators in key areas of their valuechain.

9. A KNOWLEDGE-BASEDCLASSIFICATION OF INDUSTRIAL

CLUSTERS

Some of the approaches developed in the pastto understand the phenomenon of industrialclusters include templates for applied regionalcluster analyses (Feser & Bergman, 2000),descriptive frameworks for strategic and com-petitive analyses (Carrie, 2000) and empiricalclassification models (Gordon & McCann,2000; Porter, 1998). What appears to be acommon characteristic of these approaches isthat they rely on the notion of economic link-ages among a cluster’s economic agents tocategorize and analyze both its nature andstrength. In addition, authors such as Carrie(2000) look at the nature and diversity of theinstitutional fabric of the clusters under study,whereas Gordon and McCann (2000) study thenet economic advantages stemming from geo-graphic proximity. But, these types of approa-ches seldom include an industrial cluster’sknowledge-based elements as an explicit part oftheir underlying frameworks, templates orclassificatory models.A relatively different approach looks at the

concept of clusters as a factor in competitiveadvantage (Porter, 1998). With this approach,the strength of a cluster depends on a series ofinteracting factors that can be grouped underthe categories: firm strategy, structure and


rivalry; firm conditions; related and supportingindustries; and factor conditions related to cli-mate, labor supply, government fiscal andincentive policies, etc. Although this approachpays more attention to knowledge-based ele-ments as key determinants of a cluster’sstrength (as in Porter’s, 1998 notion of ‘‘socialglue’’), it still overwhelmingly relies on thenotion of economic linkages when categorizinga cluster’s competitive dynamics and charac-teristics.Knowledge-based elements as key determi-

nants of a cluster’s strength and performancedo receive a considerable amount of attentionwithin qualitative and case-based researchstudies (Meyer-Stamer, 1998; Rabellotti, 1999).Although these studies do not typically provideexplicit frameworks for a structured under-standing of a cluster’s strength and perfor-mance, they do offer both the necessaryconceptual basis and the associated empiricalevidence. Based on the existing approaches, inTable 1 we summarize a series of key variablesthat ought to be included in a more holis-tic, knowledge-based framework for under-standing an industrial cluster’s strength anddynamics.

10. SCOPE OF COMPETITION OFINDUSTRIAL CLUSTERS

As previously mentioned, a second criticaldimension for understanding a cluster’s com-petitive and business logic is the reach andscope of the economic activities carried out byits member firms (Porter, 1998). These activitiescan be grouped according to three broad fac-tors (Morosini, 1998): (a) those that are largelyexternal to the firm, i.e. customers, productmarkets and the macro-level demographic,regulatory and legal frameworks governingthese customers and markets; (b) factors thatshape the internal characteristics of the firm,such as its resources, processes and capabilities;and (c) factors that govern social approaches tolearning, articulating knowledge and creatinga distinct sense of identity and cultural behav-ior.A firm’s external, internal and social factors

are obviously conditioned, driven to changeand influenced by a series of environmental andcompetitive forces (Rumelt, 1984). As previ-ously observed, within an industrial clustersome of these drivers are inherently local––or at

least their scope of influence is predominantlylocal. Thus, external factors such as customersand product markets can be geographicallylocalized either within the cluster or nearby. Inthese cases, the relevant demographic trendsand regulatory frameworks will also tend to belocal ones. By the same token, many of the keyresources and core competencies that areinternal to the firm can be largely driven bylocal forces (Prahalad & Hamel, 1990). Forexample, in many industrial clusters most firmsoverwhelmingly rely on local sources forhuman capital in the form of individuals withspecialized knowledge or knowledge about keycustomers. Similarly, innovation processes byfirms in certain industrial clusters tend to beheavily driven and largely stimulated by whatneighboring competing firms are doing. Finally,a firm’s particular social approaches to learningand knowledge creation, as well as the culturalnorms and behaviors it values and enacts, canbe heavily influenced by the surroundingsocioeconomic system of local cultural valuesand the institutional fabric of an industrialcluster. 5

Conversely, in certain industrial clusters afirm’s external, internal and social factors canbe largely driven by the globalization of theworld economy. This phenomenon has parti-cularly accelerated since the 1970s, involvingan increasing internationalization, greater com-petition between firms, more instability anduncertainty in product markets, as well asshifting patterns of competition where knowl-edge intensity carries a premium (Gordon,1996; Veltz, 1993). Globalization has had agreat impact on industrial clusters, though notin the direction that some might have thoughtat first (Granovetter, 1973). Although it hasreduced the importance of traditionally local-ized factors of production, globalization hasperhaps increased the importance of localizedindustrial clusters across the entire range of afirm’s external, internal and social activities.For example, firms in many industrial clusterstailor externally to a global clientele and toglobal product markets. Similarly, an increas-ing number of industrial clusters competeglobally for key internal resources, and developkey processes and capabilities within a globalcompetitive landscape. Finally it has been thecase that social approaches to learning,knowledge sharing and cultural behavior offirms within an industrial cluster were radicallyinfluenced and changed––i.e. from fiercely

Table 1. Knowledge-based classification of industrial clusters

Key constructs Main references

I––Institutional fabric

Social community

––Relatively homogenous system of values and views Amin and Thrift (1992), Becattini (1990), Gordon and

McCann (2000), Ingley (1999), Porter (1998), Pyke et al.

(1990), Rabellotti (1995), Saxenian (1994)

––System of values and view encourages initiative

and technical change

––System of institutions that spread system of values

within the cluster

Economic agents

––Relative number of individuals with specialized

skills and knowledge

Arni (1999), Brusco (1999), Czamanski and Ablas

(1979), Feser and Bergman (2000), Gordon and

McCann (2000), Hudson (1998), Meyer-Stamer (1999),

Muller-Glodde (1991), Piore and Sabel (1984), Ramos

Campos, Nicolau, and Ferraz C�ario (1999)

––Relative number of firms in geographic proximity

––Relative number of economically linked firms

––Relative number of international and multina-

tional firms

––Relative number of ‘‘meso-level’’ institutions

––Diversity of ‘‘meso-level’’ institutions

––Quality of ‘‘meso-level’’ institutions

II––Geographic closeness

––Net internal economies of scale advantages Berardi and Romagnoli (1984), Camagni (1991),

Cheshire and Gordon (1995), European Commission

(1999), Keeble and Wilkinson (1999), Lazerson (1990),

Marshall (1925), Piore and Sabel (1984), Porter (1998),

Sabel (1982), Simmie and Sennett (1999), Swann and

Prevezer (1996)

––Net specialized labor advantages

––Net interfirm knowledge sharing and networking

advantages

––Net interfirm technology transfer advantages

––Net shared market intelligence advantages

––Net product-, technology- and managerial-inno-

vations advantages

III––Economic linkages

––Common customers (both firms and individuals) Amin and Thrift (1992), Arthur (1994), Becattini

(1990), Becker (2000), Cheshire and Gordon (1995),

Cooper and Folta (2000), Feser and Bergman (2000),

Gordon (1996), Lazerson (1990)

––Common suppliers and service providers

––Common infrastructure such as transportation,

communications and utilities

––Common pool of human talent such as skilled

professionals or specialized labor

––Common educational, training and coaching

facilities for workers

––Common educational, training and coaching

approaches for workers

––Common university, research center and technol-

ogy institute specializations

––Common risk capital markets

IV––‘‘Common Glue’’

Leadership

––Explicit leaders of the cluster Buck, Crookston, Gordon, and Hall (1997), Evans

(1993), Leonard and Swap (2000), Meyer-Stamer

(1999), Rabellotti (1999), Rosenberg (2002)

––Explicit leaders are accepted by all of the cluster’s

economic agents

––Explicit leadership roles include:

––Knowledge sharing coordination

––Coaching future leaders of the cluster’s firms

––Dispute arbitration

––Vision and driving change


Table 1—continued

Key constructs Main references

Building blocks

––Strong sociocultural ties across boundaries BRITE (2001), Dominguez-Villalobos and Grossman

(1992), Humphrey and Schmitz (1998), Leon (1998),

Leyshon and Thrift (1994), Lorenz (1996),

Meyer-Stamer (1999), Morris and Lowder (1992),

Piore (1990), Rabellotti (1995), Simmie and Sennett

(1999), Zhang (2001)

––Common code of behavior among the cluster’s

economic agents

––Degree of trust among the cluster’s economic

agents

––Attitude of mutual collaboration among the

cluster’s economic agents

––Common language

––Common industrial culture

––Common industrial atmosphere

––Common approaches to developing human capital

––Common business understanding and mindset

––Common competitive performance approaches

and measurements

Communication rituals

––Regular communication events Pyke et al. (1990), Porter (1998), Schmitz and Nadvi

(1994); Amin and Thrift (1992), Granovetter (1973),

Magplane (2001)

––Regular communication interactions

––Regular communication approaches

Knowledge interactions

––Benchmarking task forces across the cluster’s firms Boston Consulting Group (1998), Saxenian (1994)

––Roles of research centers, technological institutes,

universities include

Executive education of the cluster firms’ employees

Mutual cooperation initiatives across the cluster’s

firms

Bagchi-Sen (2001), Brusco (1999), Christensen (1997),

Keeble, Lawson, Moore, and Wilkinson (1999), Leon

(1998), Lorenz (1996), Pedersen, Sverrisson, and van

Dijk (1994), Porter (1998), Saxenian and Hsu (2001),

Schmitz (2000)

Technology transfers across the cluster’s firms

Joint R&D initiatives across the cluster’s firms

Joint manufacturing initiatives across the cluster’s

firms

Joint product design initiatives across the cluster’s

firms

Joint sales and marketing initiatives across the

cluster’s firms

––‘‘Meso-level’’ institutions’ roles include

Initiating coordination mechanisms inside the

cluster

Amin and Thrift (1995), European Commission (2002),

Keeble et al. (1999), Sanch�ez, del Castillo, Lacave, and

Terras (2000)Managing coordination mechanisms inside the

cluster

Professional rotations

––Degree of own-sector employment within cluster’s

home region

Athreye (2001), Becker (2000), Baptista and Swann

(1998), Bortagaray and Tiffin (2000), Brusco (1999),

Keeble et al. (1999), Leonard and Swap (2000), Lorenz

(1996), Paija (2001)

––Degree of interfirm mobility within cluster

––Degree of spin-offs/start-ups by cluster’s employees


competitive to highly cooperative––once globalcompetition became the norm within theindustry.

In Table 2, we summarize a series of keyparameters that can serve to characterize thecompetitive scope of an industrial cluster’s

Table 2. Scope of competition of industrial clusters

Extent to which the competitive drivers of an industrial cluster’s firms are mostly local or global according to

these factors

Key construct Main references

External factors

––Main customers Brusco (1999), Feloy, Gordon, Lloyd, and Roe (1997),

Lazerson (1990), Mishan (1971), Sanch�ez et al. (2000),

Schmitz (1995)

––Main product and services markets

––Key demographic trends

––Main legal and regulatory frameworks

Internal factors

––Key resources (i.e. human capital, financial capital) Porter (1998), Simmie and Sennett (1999), Rabellotti

(1995), Puri and Hellmann (2000), Saxenian (1994)––Key processes (i.e. innovation, product

development, supply chain management)

––Key competencies and capabilities

(i.e. key technologies, speed of innovation)

Social factors

––Learning (i.e. about customers, products,

technologies, managerial approaches)

Brusco (1999), Keeble et al. (1999), Leonard and Swap

(2000), Rabellotti (1995) Sanch�ez et al. (2000)

––Knowledge creation

––Knowledge sharing

––Cultural behavior and norms


firms according to external, internal and socialfactors.

11. LINK BETWEEN KNOWLEDGEINTEGRATION, SCOPE OFCOMPETITION AND THE

PERFORMANCE OF CLUSTERS

A broad array of existing empirical evidence(some of which is referenced in the previoussections) suggests that both the degree ofknowledge integration and the scope of compe-tition are co-evolving factors that are crucial toexplain the economic performance of industrialclusters. Although the empirical evidenceremains slightly fragmented, it suggests thatfirms in industrial clusters that present a highdegree of knowledge integration and competeglobally innovate more, present stronger growthpatterns, adapt to changing environmentalconditions more rapidly and have a more sus-tainable economic performance than firms inless integrated clusters that tend to competewithin strictly local geographic boundaries(Meyer-Stamer, 1998; Porter, 1998; Pyke et al.,1990; Rabellotti, 1995; Schmitz, 2000; Simmie &Sennett, 1999). These kinds of empirical evi-dence underlie the following hypothesis:

The higher the degree of knowledge integrationbetween member firms, and the higher the globalscope of competition of member firms, the higherthe economic performance of industrial clusters.

Figure 1 provides a graphic illustration of ourhypothesized effects, postulating a comparativetaxonomy of industrial clusters across a diver-sity of industries and geographies, which weassessed within the context of our research. Thishypothesized taxonomy is included here forillustrative purposes only. It is based, however,on an examination of over 2,000 pages ofarchival data, academic and specialized publi-cations as well as expert opinion gatheredthrough a series of field visits and interviewswith industrial cluster agents (e.g., entrepre-neurs, association representatives, practitio-ners) in southern Brazil, Brazil’s Amazon Stateand northern Italy. Both the literature reviewand the expert interviews we carried out weretightly structured around the templates andconstructs outlined in Tables 1 and 2 (seeAppendix B). Our analyses focused on the mid-1990s, for which a relatively large body ofempirical data exists on industrial clusters alongthe following dimensions: degree of knowledgeintegration, scope of competition and economicperformance (e.g., Becattini, 1990; Feser &

Figure 1. Hypothesis: knowledge integration, scope of competition and performance of industrial clusters.


Bergman, 2000; Gordon & McCann, 2000;Meyer-Stamer, 1998; Rabellotti, 1999).Figure 1 thus illustrates a number of overall

patterns that seem to emerge quite clearly fromthe growing––albeit fragmented––empirical lit-erature on industrial clusters over the last twodecades of the 20th century. These patternsunveil a multitude of characteristics that appearboth to explain and determine the economicperformance of industrial clusters. Some ofthese characteristics have to do with competi-tive factors that are inherent in the industrialsectors in which the clusters operate. Othershave to do with factors concerning an industrialcluster’s institutional fabric, geographic close-ness, economic linkages and ‘‘common glue,’’and here the scope for positive intervention isarguably greater for the macro-economic policymaker and the business planner alike.An empirical test of the hypothesis we have

developed, remains, however, a challengingstep for a better, holistic understanding of themajor factors that both explain and determinethe economic performance of an industrialcluster. We suggest that this empirical test,conducted along the constructs developed inTables 1 and 2, could contribute to thisunderstanding in a way that encompasses the

economic and social aspects that appear to beequally important to the competitive function-ing of industrial clusters.

12. CONCLUDING REMARKS

We have looked at industrial clusters from aneconomic and a social perspective. The com-plexity and richness of this phenomenon seemsto be as great as its potential to contributeeconomic value to both the economic agentsand the social communities involved. We sug-gest that by looking at the crucial dimensionsof knowledge integration and the scope of(global) competition, much of the underlyingfunctioning fabric of industrial clusters can becaptured in ways that are meaningful to boththe economic policy maker and the businessexecutive. No less important, the analyticalframeworks and testable hypothesis developedin this study can also highlight and strengthenthe many social dimensions that are so centralto explaining the unprecedented economicsuccess and unique competitive advantages thatindustrial clusters have come to realize at thedawn of the 21st century.

NOTES

1. A ‘‘cluster’’ of Sumerian cities along the Fertile

Crescent (an elongated valley between the Tigris and

Euphrates rivers, in today’s southern Iraq) is commonly

regarded by archaeologists as the world’s first urban

development, the very cradle of humankind. Early 20th-

century excavations in these ancient cities, which date

back to 4000–3500 bc, unveiled astonishing remnants of

entire districts devoted to well-defined artisan activities,

as well as specialized markets with highly developed rules

and laws governing the production and exchange of


goods and services. Most surprising of all, clear evidence

of the commercial reach of these districts was found as

far away as Anatolia (today’s Turkey), Asia Minor (in

modern Syria) and Egypt.

2. Becattini’s (1990) description of an industrial clus-

ter’s ‘‘institutional system’’ is certainly a more holistic

notion than either ‘‘meso-level’’ or ‘‘associational’’

conceptions. This applies both to the range of institu-

tions that an ‘‘institutional system’’ encompasses and to

the inherent purpose of these institutions, i.e. social and

economic as opposed to strictly economic.

3. The role of different government bodies as initiator,

promoter, coordinator and manager is relevant to a

number of industrial clusters today. A description of the

types of action taken by government is given in

Appendix A.

4. A variable that increasingly seems to affect the

ability of a cluster to attract or retain human talent

is its quality of life. This includes factors such as

housing costs, amenities, commuting time and clean

environment, and has been used to explain the

growth of ‘‘second tier’’ clusters when quality of life

declines in the original cluster (Bortagaray & Tiffin,

2000).

5. See an introduction by Drucker (1997).

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APPENDIX A. THE ROLE OF LOCAL,NATIONAL AND REGIONAL

GOVERNMENTS

The role that local, national and––increas-ingly––regional governments play in both thebirth and management of industrial clusters hasbeen studied quite extensively in the past.Governments are strongly attracted to thisphenomenon for a variety of reasons, most ofwhich must seem obvious to all: there ispotential for economic growth and employ-ment as well as for attracting valuable in-vestments, technological assets and criticaleconomic resources to certain geographic areas,typically those seen as less economically devel-oped or ‘‘strategic.’’The relevant literature has identified a num-

ber of roles that local, national and regionalgovernments typically play vis-�a-vis industrialclusters (Meyer-Stamer, 1999; Rabellotti,1999). These roles can be classified in a fewcategories:

––Initiator: Macro-economic or regionalpolicy decisions often pave the way forindustrial clusters to emerge or they––oftenless deliberately––create critical conditionsthat force these clusters to evolve in com-pletely new ways. Perhaps the most specta-cular recent example of the former is theFinnish government’s liberalization andderegulation of the telecommunicationsindustry during the 1980s. This radical pro-cess contributed to the development of aworld-class industrial cluster in southernFinland over the next decade, nurturing the

growth of global telecommunications equip-ment leaders such as Nokia.––Promoter: Governments can promote anindustrial cluster’s products, services andimage in a variety of ways, including exportpromotion infrastructure, networking abroadand trading negotiations with other govern-ments. National governments of countriessuch as France, Taiwan and Singapore pro-vide good examples of long-term promo-tion efforts on a global scale in support ofindustrial clusters, i.e. in the food, semicon-ductor and computer hardware sectors,respectively.––Coordinator: State governments can beactive in carrying out project tasks such asbenchmarking, technology transfer, best-practice exchanges and expert assistanceon behalf of clusters. In the Brazilian stateof Santa Catarina, for example, the localgovernment has been described as playingan active coordinating role throughout the1990s to support the state’s textile and tilemanufacturing clusters (Meyer-Stamer,1999).––Manager: State, national or regional gov-ernments sometimes provide massive re-sources to start and/or support industrialclusters in particularly intensive ways thateither include or fall just short of takingownership stakes in some of the main firmsand related economic and capital infrastruc-ture. This is often the case with ‘‘industrialcities,’’ ‘‘industrial poles’’ and the like,which are typically started with a significantinjection of public funds and supportedthereafter with heavily subsidized re-sources, tax incentives and favorable macro-economic policies. In addition, in thesesituations, governmental institutions andrepresentatives often play a rather activerole, not only influencing the strategic ori-entations and business activities of the firmswithin the cluster but also taking an openlymanagerial role, i.e. running the economic,capital and financial infrastructure sur-rounding the cluster’s firms––or even someof the firms themselves.

http://www.oycf.org/Perspectives/13_083101/innovator_dilemma.htm

http://www.oycf.org/Perspectives/13_083101/innovator_dilemma.htm

APPENDIX B

RESEARCH STUDIES LOOKING AT INDUSTRIAL CLUSTERS, DEGREE OF KNOWLEDGE INTEGRATION, AND SCOPE OF COMPETITIONa

London

Financial

Piemonte

Automobile

Emilia

Romagna Tiles

Mexican Shoe

Manufacturing

Santa Cata-

rina Textiles

Finland ICT Silicon Valley

IT

I––Institutional fabric

Social community

Relatively homogenous

system of values and views

Amin and Thrift

(1992)

Belussi

(1992)

Barbagli, Brusco,

Pisati, Santono, and

Serravalli (1998),

Capecchi (1990)

Rabellotti

(1995), Arias

(1992)

Meyer-

Stamer

(1999)

Arni (1999) Saxenian (1994),

Porter (1998)

System of values and views

encourages initiative and

technical change

System of institutions that

spread system of values within

the cluster

Economic agents

Relative number of

individuals with specialized

skills and knowledge

Gordon and

McCann (2000)

European

Commission

(1999)

Piore and Sabel

(1984),

Brusco (1982),

Brusco (1999),

Sternberg (1996),

Lazerson (1990)

Rabellotti

(1999),

Schmitz (2000),

Humphrey and

Schmitz (1998)

Ramos

Campos et

al. (1999)

FinnFacts

(2001)

Saxenian (1994),

Rosenberg

(2002),

Becker (2000),

Bresnahan,

Gambardella,

and Saxenian

(2001)

Relative number of firms

within geographic proximity

Relative number of

economically linked firms

Relative number of

international and multina-

tional firms

Relative size of international

and multinational firms

Rabellotti

(1995)

Relative number of ‘‘

meso-level’’ institutions

NA Brusco and Righi

(1989)

Diversity of ‘‘meso-level’’

institutions

NA Murray (1991)

Quality of ‘‘meso-level’’

institutions

NA Brusco (1999),

Pyke et al. (1990)

Muller-

Glodde

(1991)

II—Geographic closeness

Net internal economies of scale

advantages

NA European

Commission

(1999)

Berardi and Ro-

magnoli (1984)

Rabellotti

(1995)

Meyer-Sta-

mer (1999)

Koski, Rouvi-

nen, and Yl€a-Anttila (2001)

Porter (1998)

Net specialized labor advantages Cheshire and

Gordon (1995)

Lazerson (1990)

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DEVELOPMENT

322

Net inter-firm knowledge shar-

ing and networking advantages

Amin and Thrift

(1992)

Piore and

Sabel (1984)

Swann and

Prevezer (1996)

Net inter-firm technology trans-

fer advantages

Feloy et al.

(1997)

Sabel (1982) Arni (1999)

Net shared market intelligence

advantages

Sabel (1982)

Net product-, technology- and

managerial-innovation

advantages

Romagnoli and

Lungarella (1989)

III––Economic linkages

Common customers

(both firms and individuals)

Cheshire and

Gordon (1995),

Gordon and

McCann (2000)

European

Commission

(1999)

Lazerson (1990) Humphrey and

Schmitz (1998),

Rabellotti

(1995)

Meyer-Sta-

mer (1999)

Sanch�ez et al.

(2000)

Arthur (1994)

Common suppliers and service

providers

Common infrastructure such as

transport, communications and

utilities

Cooper and

Folta (2000)

Common pool of human talent,

such as skilled professionals, or

specialized labor

Becker (2000)

Common educational, training

and coaching facilities for

workers

Swann and

Prevezer (1996)

Common educational, training

and coaching approaches for

workers

Amin and Thrift

(1992)

Common university, research

center and technology invstitute

specializations

Becker (2000)

Common risk capital markets Humphrey

and Schmitz

(1998)

IV–‘‘Common Glue’’

Leadership

Explicit leaders of the cluster Buck et al.

(1997)

Meyer-Sta-

mer (1999)

Sanch�ez et al.

(2000)

Rosenberg

(2002)

INDUSTRIA

LCLUSTERS

323

APPENDIX B––continued

London

Financial

Piemonte

Automobile

Emilia

Romagna Tiles

Mexican Shoe

Manufacturing

Santa Cata-

rina Textiles

Finland ICT Silicon Valley IT

Explicit leaders are accepted

by all of the cluster’s eco-

nomic agents

Leonard and

Swap (2000)

Building blocks

Strong sociocultural ties

across boundaries

Leyshon and

Thrift (1994)

European

Commission

(1999)

Rabellotti (1995) Rabellotti

(1995)

Sanch�ez et al.

(2000)

Sternberg (1996)

Common code of behavior

amongst the cluster’s eco-

nomic agents

Dominguez-Vill-

alobos and

Grossman

(1992)

Degree of trust between the

cluster’s economic agents

Humphrey and

Schmitz (1998)

Attitude for mutual collabo-

ration amongst the cluster’s

economic agents

BRITE (2001),

Taylor et al.

(2003)

Rabellotti (1995) Humphrey and

Schmitz (1998)

Common language Feloy et al.

(1997)

Zhang (2001)

Common industrial culture Morris and

Lowder (1992)Common industrial

atmosphere

Common approaches to

developing human capital

Cooper and

Folta (2000)

Common business under-

standing and mindset

Rabellotti

(1995)

Porter (1998)

Common competitive

performance approaches and

measurements

Communication mechanisms

Regular communication

events

Feloy et al.

(1997)


interactions

Amin and Thrift

(1992)

Meyer-Sta-

mer (1999)

Sanch�ez et al.

(2000), Ali-

Yrkk€o, Paija,

Reilly, and Yl€a-

Anttila (2000)

WORLD

DEVELOPMENT

324


approaches

Rabellotti

(1995)

Rabellotti (1995)

Coordination mechanisms

Benchmarking taskforces

across the cluster’s firms

Boston

Consulting

Group Study

(1998)

Meyer-Sta-

mer (1999)

Sanch�ez et al.

(2000), Paija

(2001)

Saxenian (1994)

Executive education of cluster

firm’s employees

Mutual cooperation initiatives

across cluster’s firms

Feloy et al.

(1997)

Brusco (1999) Humphrey and

Schmitz (1998)

Christensen

(1997)

Technology transfers across

the cluster’s firms

Pedersen et al.

(1994)

Joint R&D initiatives across

the cluster’s firms

NA

Joint manufacturing

initiatives across the cluster’s

firms

NA Schmitz (1995)

Joint product design

initiatives across the cluster’s

firms

Joint sales and marketing ini-

tiatives across the cluster’s

firms

NA

Professional rotations

Degree of own-sector

employment within cluster’s

home region

Feloy et al.

(1997)


(1995)

Paija (2001) Becker (2000)

Degree of inter-firm mobility

within cluster

Degree of spin-offs/start-ups

by cluster’s employees

Scope of competition of industrial clusters

External factors

Main customers Feloy et al.

(1997)

Schmitz (1995) Woodruff

(1998), Rabe-

llotti (1995)

Main product and services

markets

Brusco (1999)

Key demographic trends Sanch�ez et al.

(2000)

Becker (2000)

INDUSTRIA

LCLUSTERS

325

APPENDIX B––continued

London

Financial

Piemonte

Automobile

Emilia

Romagna Tiles

Mexican Shoe

Manufacturing

Santa Cata-

rina Textiles

Finland ICT Silicon Valley

IT

Main legal and regulatory

frameworks

Internal factors

Key resources (i.e. human

capital, financial capital)

Simmie and Sen-

nett (1999)


(1995), Hum-

phrey and Sch-

mitz (1998)

Puri and Hell-

mann (2000)

Key processes (i.e. innovation,

product development, supply

chain management)

Saxenian (1994)

Key competencies and capa-

bilities (i.e. key technologies,

speed of innovation)

Porter (1998)

Social factors

Learning (i.e. about

customers, products,

technologies, managerial

approaches)

Keeble et al.

(1999)


(1995)

Porter (1998),

Leonard and

Swap (2000)

Knowledge creation Sanch�ez et al.

(2000)Knowledge sharing

Cultural behavior and norms

aFor Veneto Plastics, Manaus Telecom, Manaus Consumer Electronics, Manaus Light Automotive and Stuttgart Autoparts, we carried out expert interviews and field

visits, using the templates and constructs outlined in Tables 1 and 2.

WORLD

DEVELOPMENT

326

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