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Knowledge Systems and Technological Dynamism in

Industrial Clusters in Developing Countries

MARTIN BELLUniversity of Sussex, Brighton, UK

and

MICHAEL ALBU *

University of Oxford, Oxford, UK

Summary. Ð Research on industrial clusters in developing countries is increasinglyconcerned with how their competitiveness evolves and changes over time. This articleshows what analytical shifts are needed to unravel the technological underpinnings ofclusters' longer-term competitiveness. Building upon an understanding of technologicallearning in large-scale ®rms, we stress the need to focus on systems of knowledge ac-cumulation, rather than just production systems. With this in mind, future researchshould investigate clusters' active capabilities for generating and di�using knowledge,and their openness to external sources of knowledge. A conceptual framework to guideinvestigation of these aspects of cluster knowledge-systems is presented. Ó 1999 Else-vier Science Ltd. All rights reserved.

1. INTRODUCTION

For those with interests in the technologicaland organizational underpinnings of the long-term process of industrialization, research onindustrial clusters in developing countries hasmoved into an intriguing transition phase. Forseveral years this ®eld of research has concen-trated on various aspects of the comparativemorphology of clusters, but recently it has be-come increasingly concerned with questionsabout the dynamism of clusters and theirlonger-term competitiveness. Rabellotti (1995,p. 229), for instance, has stressed the impor-tance of ``...comparing trajectories and stagesof development instead of snap-shots.''Humphrey (1995) has emphasized the impor-tance of moving ``from models to trajectories''in the analysis of clusters; and indeed Schmitz(1995, p. 9) found that ``the most interestingconclusions emerge from tracing changes overtime''Ða view recently well illustrated byMeyer-Stamer's (1998) exploration of path de-pendency in industrial clusters in Brazil.

Research on clusters has also begun to giveexplicit attention to the ``technological'' aspectsof dynamism. Although they might not use thatterm, authors have turned at least some of theirattention to questions about the rates andsources of change in the designs and qualitylevels of the products produced by clusters andin the characteristics of the materials, processesand organizational arrangements used to pro-duce and market them. Growing interest inthese aspects of technological change does notmean however that there is general agreementabout the importance of the technological di-mension of cluster dynamism. Nor is there anyconsensus around either the key issues to ex-plore or the conceptual basis for any such ex-ploration. A broad aim of this paper istherefore to stimulate and guide interest in this

World Development Vol. 27, No. 9, pp. 1715±1734, 1999Ó 1999 Elsevier Science Ltd. All rights reserved

Printed in Great Britain0305-750X/99/$ - see front matter

PII: S0305-750X(99)00073-Xwww.elsevier.com/locate/worlddev

* We would like to thank Henny Romijn, Evert-Jan

Visser, Khalid Nadvi and Hubert Schmitz for their

helpful comments and advice on earlier versions of this

paper. The usual disclaimers apply.

1715

technological dimension, by highlighting someof the most important questions that are as yetunanswered and by suggesting a conceptualbasis for further research.

The current situation is reminiscent of a verysimilar phase of transition in the closely related®eld of research on technological change anddynamic competitiveness in the large-scale sec-tor of industry in developing countries. Manyof the uncertainties, limitations and di�cultiescurrently confronting research on the techno-logical dynamism of clustered industrial pro-duction were evident about 25 years ago inwhat was then an emerging ®eld of research ontechnological change and learning in individuallarge-scale enterprises in developing countries.

Research since then built up a body of un-derstanding which fundamentally altered thepreviously dominant perspective on technolog-ical change and industrialization. We suggestthat research on clustered small-scale produc-tion in developing countries is part way througha similar conceptual transition. So far howeverit has shown few explicit links with research onthe technological dynamism of the large-scaleindustrial sector in developing countries.1 Inthis paper we speci®cally try to strengthen thoselinks, hoping to contribute to the developmentof understanding about the technological di-mension of the longer-term evolution andcompetitiveness of industrial clusters.

In Section 2 of the paper we compare the twobodies of research, attempting to locate theperspectives which are currently dominant inresearch on clusters on the spectrum of shiftingperspectives about technological change inlarge-scale industry which has developed sincethe 1970s. Then in Section 3 we outline severalconceptual issues about knowledge systems andtechnological capabilities which seem impor-tant in developing a deeper understandingabout the technological dynamism of industrialclusters in developing countries. In doing so, wedraw initially on the previous body of researchabout technological change in large-scale in-dustry, but that is only a starting point. Wethen try to move beyond that to address someof the more speci®c characteristics of techno-logical change in clusters. Finally in Section 4we build on that conceptual discussion to out-line a taxonomy of key features of the organi-zational basis of technological dynamism inclustered industrial production, and we illus-trate how this might be developed as a basis forcomparative analysis of change in cluster``knowledge systems'' and its relationship to the

long-term sustainability of cluster competitive-ness.

2. TECHNOLOGY ANDINDUSTRIALISATION: CHANGING

PERSPECTIVES

(a) Technology, dynamism and large-scaleindustry in developing countries: an earlier

transition

Until the late 1960s there was little interest inunderstanding industrial technological changein developing countries. This was partly be-cause the process of technological change waspresumed to be largely absent, occurring al-most entirely in the industrialized world. It wasalso because, in the tenets of ``mainstream''growth economics, technology was embodied in®xed capital and technological progress wastherefore achieved by the fairly straightforwardprocess of capital accumulation. Consequently,since the industrially advanced countries werethe producers of most of the capital goodsneeded for industrialization, any problemabout achieving long term technological pro-gress in industry was largely a problem aboutgenerating the savings and international capital¯ows needed to acquire externally sourcedcapital goods.

From that perspective, the technological roleof local industry was essentially passive, in-volving merely the adoption and routine oper-ation of externally supplied technologies.Curiously, this view was also held by the ``de-pendency'' school of thought which describedthe reliance of ``peripheral'' developing coun-tries on imported capital goods as ``technolog-ical dependence,'' correspondingly emphasizingthe virtual absence of any signi®cant innovativeactivity in Third World industry. These viewsabout the irrelevance of any signi®cantly cre-ative industrial technological activities in de-veloping countries rested on severalpresumptions about the nature of technologyand technological change.

ÐTechnology was identi®ed almost exclu-sively with machinery (capital goods). Tech-nological change was therefore seen either asdevelopment of new kinds of machinery(technological ``innovation'') or as acquisi-tion and installation of new machinerywhich had already been developed elsewhere(the ``di�usion'' of technology).

1716 WORLD DEVELOPMENT

ÐA sharp distinction was drawn betweentechnological innovation and technology dif-fusion. One was considered technologicallycreative; the other was seen as involvingmerely the passive ``adoption'' of innova-tions created by others.Ð``Modern'' industry in developing coun-tries was seen as typically acquiring almostall its capital goods from suppliers in the in-dustrialized world. If there was any produc-tion of industrial capital goods in developingcountries, that involved merely the techno-logically passive replication of machinery de-signs originating in the industrialized world.

From this stylized ``1960s'' perspective, anylocally generated industrial technologicalchange in developing countries was seen as es-sentially minor and adaptive, involving little orno technological creativity. The main techno-logical tasks were merely to acquire, and learnhow to use, available technologies, and the only``technological capabilities'' needed were thosefor undertaking such routine investment andproduction activities.

For any social scientist interested in creativeaspects of the process of industrial technologi-cal change, this perspective suggested that therewas little signi®cant to observe. Thus, concernsabout policy relating to technological changetended to focus on ®nancial and informational``gaps'' that obstructed or distorted the ¯ows ofcapital-embodied technology. Fortunately, notall social scientists accepted that perspectiveand, starting from the early 1970s, a growingnumber began to explore the realities of tech-nological change in large-scale industry in sev-eral developing countries, especially in thelarger and more industrialized countries such aBrazil, Argentina, Mexico, Korea and India.Twenty-®ve years later this research has accu-mulated a large body of understanding whichsuggests a very di�erent stylized picture fromthat outlined above.

First, analysis of change in a ®rm's produc-tion technology must encompass much morethan just its machinery-embodied technology.Technology is a much more complex bundle ofknowledge, with much of it embodied in a widerange of di�erent artifacts, people, proceduresand organizational arrangements. These em-bodiments of knowledge include at least:product speci®cations and designs; materialsand component speci®cations and properties;machinery and its range of operating charac-teristics; together with the various kinds of

know-how, operating procedure and organiza-tional arrangement needed to integrate theseelements in an enormously variable range ofdi�erent production systems. Moreover, asthese elements of technology are highly inter-connected, improvement in something as``simple'' as product quality may requirechanges to be made across several linked ele-ments of the bundle, e.g., in machine hardwareor operating procedures, the organization ofproduction ¯ows, or the speci®cation andtreatment of materials.

Second, there is no sharp distinction betweeninnovation and di�usion. Very few componentsof production technology are simply acquired``ready-made'' and then brought into use ac-cording to standard ``recipes'' which are iden-tical to, and replicated from, previousapplications. Even in cases where the intro-duction of some element of new technologyinvolves a fairly close approximation to suchnoncreative technology ``adoption,'' the inter-actions with other elements of technology in theproduction system typically requires creativeproblem-solving and innovative re-con®gura-tion of at least some elements in the overallproduction system. Furthermore, ®rms do notacquire the capabilities to generate these cre-ative changes spontaneously merely from theexperience of doing production, as implied bynotions of learning curves. Indeed, studies ofinfant industries (e.g., Bell, Scott-Kemmis andSatyarakwit, 1982) have demonstrated that theperformance of production systems may notincrease at all over time, and can easily stagnateor decline over the long-run.

Third, external sources of technology are notlimited to machinery suppliers. Customers, forinstance, may be much more important sourcesof technology, providing not just knowledgeabout product speci®cations but also a widerange of other elements (e.g., operating proce-dures and know-how, or knowledge aboutmaterials properties).

More positively, research has identi®ed awide spectrum of technological changes whichdo not ®t the common focus on ``step-jumps''based on new knowledge derived from for-malized Research and Development (R&D),requiring substantial investment in new facili-ties for their implementation. This spectrum ofother kinds of technological change encom-passes, for instance, those which involve (a)improvements to existing production systems,rather than investment in complete units of newproduction capacity; and (b) knowledge inputs

KNOWLEDGE SYSTEMS AND TECHNOLOGICAL DYNAMISM 1717

drawn largely from existing stocks rather thanfrom recent R&D.

These types of change account for a verylarge proportion of all technological changeand, in technologically dynamic ®rms and in-dustries in both developing and developedcountries, continuing streams of such changetypically stretch out over long periods through-out the lives of production systems. Theircumulative economic signi®cance over theseperiods is typically very large. Moreover, theydo not emerge spontaneously as a ``by-prod-uct'' of the routine production operations of®rmsÐas in simpler conceptions of ``learning-by-doing''. They are generated with very sub-stantial inputs from the change-generatingresources of the technology-using ®rms them-selves (Hollander, 1965; Dahlman and Fonseca,1987; Enos, 1997).

To sum up, from this ``1990s'' perspective,technological change is not simply somethingwhich ®rms choose and ``buy-in'' from outside.On the contrary, it is rooted in a speci®c set ofchange-generating resources or capabilitieswhich are located within the structure of tech-nology-using ®rms. Consequently, the learningprocesses which contribute to building andstrengthening those capabilities are seen asplaying an important role in the long-term dy-namism and sustainability of industrial pro-duction.

This also means that for those interested inunderstanding industrial technological dyna-mism in developing countries, there is after allsomething to observeÐapart from the acquisi-tion of externally sourced machinery. More-over, these processes of technological changeand capability accumulation exhibit wide dif-ferences between ®rms, industries and econo-mies, with some of these di�erences apparentlyassociated with di�erent long-term paths ofeconomic performance.

There is, however, an important issue whichuntil recently was given only limited attentionin the literature on technology and large-scaleindustrial development: the inter®rm, network-based nature of the processes of change andtechnological learning. While the importance ofthe individual large ®rm's own change-gener-ating capabilities has been emphasized, it hasalso been recognized that technological changeusually depends on externally sourced inputs.More recent perspectives have therefore cometo emphasize the importance of this dualstructure of internal change-generating re-sources and links to external sources of tech-

nologyÐboth other ®rms and more specializedknowledge-generating organizations like uni-versities or R&D institutes. Such combinationsof internally organized capabilities with exter-nal knowledge resources, and the links betweenthem, have come to be described as industrial``innovation systems,'' ``technology systems'' or``knowledge systems''Ða profusion of conceptsderived largely from research in the industrial-ized world in the last decade or so.2

While these systemic perspectives have re-cently been seen as important for understand-ing the technological dynamics of lateindustrialization, there has only been limitedempirical research about how these networkedknowledge systems emerge and evolve in thespeci®c context of large-scale industry in de-veloping countries. On the other hand, ofcourse, understanding the development of net-worked structures has been the ``bread andbutter'' of research on clustered small-scale in-dustry in developing countries. That is a majorreason why the transition of interest in this ®eldof research toward issues about technologicaldynamism is potentially so fruitful. If futureresearch on industrial clusters can combine afocus on the inter®rm networked structures andrelationships, with an exploration of thechange-generating resources and learning pro-cesses occurring within technology-using ®rmsand cluster institutions, then we may movecloser to understanding the basis of techno-logical dynamism and the sustainability ofcompetitiveness in clusters.3

(b) Technology, dynamism and industrialclusters in developing countries

It is di�cult to re¯ect in a short commentarythe inevitable heterogeneity of views and per-spectives within a ``®eld'' of research. Never-theless, even a sketchy attempt to do so heremay be useful in highlighting some of the keyissues which we will develop further inSection 3 of the paper. To provide a clearframework for this, we have focused on therecent doctoral research of six authors. Whilefour of them are contributors to this SpecialIssue, this article refers to their doctoral theses.Key features of these studies are summarized inTable 1.

All these studies are concerned with issuesabout change and dynamism over relativelylong periods. There are, however, wide di�er-ences in the key aspects of change given mostattention and, at a broader level, there is little


Table 1. The investigation of technological dynamism in selected cluster case studies a

Case study Main focus of inquiry Investigation of technological change and its determinants

Nadvi (1996)SurgicalInstrumentManufactureSialkot, Pakistan

Relations betweeneconomics ofclustering, local socialidentities andinternationalcompetitiveness.

Used questionnaire-based survey of 57 ®rms. A few technology-relatedquestions, but not the main focus (~5% of questionnaire): What newtechnologies and equipment adopted in past ®ve years? General sourcesof technical innovations? Changes in the way production is organized andcontrolled? Sources of information for process innovation and productdesigns?There is however, limited discussion of the results of these questions.Qualitative evidence from detailed case-studies is ambiguous abouttechnological advantages of clustering relationships. Relates clustering toimproving organization and quality standards.

Tewari (1996)Light-engineeringIndustry Ludhi-ana, India

Patterns of small-®rmgrowth, includingtheir dynamism andincorporation intonational and exportmarkets.

Explored the social and historical origins of the vibrancy of Ludhiana'sindustry. Interviewed 117 ®rms, but details of methodology andquestionnaire not available. Little quantitative analysis of results. Amplequalitative evidence provided for mechanisms which aid rapid di�usionof technical changes through the cluster, and lower entry barriers.Extols ``innovative adaptability of Ludhiana's small ®rms.'' Does notaddress the key question of how new knowledge and skills for managingtechnical change (innovative adaptability) are acquired.

Sandee (1995)Rural Roof-tileManufactureKaranggeneng,Indonesia

Longitudinal study,exploring processes oftechnical changeÐinparticular, howinnovations di�usethrough a villagecluster.

Six-year study of innovation di�usion among 34 producers, distinguish-ing between early, late and non-adopters. Highlights disadvantages tothose excluded from networks of information and credit. Demonstratesadvantages of clustering where collaboration is necessary due to scale-economies (indivisibility of capital goods).Comprises a detailed investigation of the closing of a particular``technological gap,'' but not if and how this is part of a process ofdeepening technological capabilities (to close future/wider gaps). Sourcesof technological change (innovation) are treated as exogenous. Con-cerned with how the use of innovations di�uses, rather than with thecapabilities of adopters.

Rabellotti (1995)FootwearIndustry Leon/Guadalajara,Mexico

Analysis of theeconomic e�ectsarising from thelinkages existingamong economicagents within clusters.

Structured questionnaire-based survey of 50 ®rms in Mexico and asimilar number in Italy. Supplemented by nine detailed case-studies.Technology-related questions few, along same lines as Nadvi (above), butthere is minimal discussion of the results.Technological performance is seen as very poor, but this is associatedsimply with the lack of a domestic capital goods sector. There is littleexploration of why these clustering ®rms capabilities to manage theopportunities and threats created by technical changes are so weak.Addresses need to study the ``system's capability to grow and innovatein'' conclusions.

Cawthorne(1990) CottonKnitwearIndustryTiruppur, India

Labor processesunder amoebic capitalaccumulation in acluster of mainly small®rms.

Intensive research based on detailed case-histories of 25 sample ®rms,focusing principally on capital accumulation and labor processes.Explicitly rejects technological factors in explaining recent expansion andexport success.Argues convincingly that stimuli for technological change are external,but does not investigate factors that in¯uence the cluster's technologicalcapability to respond to market stimuli. Questions whether Tiruppurindustry can continue to compensate with cheap labor for what it lacks intechnical sophistication. Anecdotal evidence presented suggests it cannot.

Visser (1996)GarmentManufactureLima, Peru

How clustering helpssmall ®rms performcore functions ofproduction, transac-tions, innovation andrisk management.

Carefully conceived and structured questionnaire-based sample survey of130 ®rms including (uniquely) two non-clustered control groups. One (offour) research questions is: ``do local networks add technologicalknowledge to the local environment?'' The survey analysis dwellshowever on relationship between productivity performance and staticlinkage-e�ects.The kind of linkages which might have dynamic-e�ects are found to beabsent or only incipient. Conceptual emphasis on dynamic functions ofthe ®rm, and dynamic linkage-e�ects of clustering, but no direct attemptis made to measure technological development.

a Source: Abstracted from Albu (1997).


consensus about the phenomena described as``dynamic''. Nevertheless, all six studies areconcerned with issues about `ìnnovativeness''in some form or other. None of them is cen-trally focused on questions about technologicalchange, but all of them do address such ques-tions in some form: in other words, their inte-rest in `ìnnovativeness'' is concerned in somedegree with change in the characteristics of theproducts, processes and production organiza-tion of the clusters examined, and all the studiesmake more than just incidental reference tothese kinds of change.

This does not, of course, mean that all theauthors identi®ed the presence of signi®canttechnological change in the clusters they ex-amined. It was apparently weak, intermittent orabsent in some; and in the case of the knitwearcluster in Tiruppur, for instance, Cawthorne(1990) explicitly rejects the idea that techno-logical factors could explain the rapid expan-sion of output and the considerable exportsuccess.

Independent of how much they emphasizetechnological change in their studies, some as-pects of the authors' underlying perspectives ontechnological dynamism can be roughly locatedon the stylized ``1960s±1990s'' transition out-lined above. None of them is consistently andclearly located at the 1960s end, although, someof them in some respects are nearer that endthan the other. This can be illustrated withreference to three issues: a common emphasison the introduction of machinery within a rel-atively narrow perspective on what constitutes``technological change''; a preoccupation withtechnology ``di�usion'' as the main intraclusterprocess contributing to technological change;and limited interest in the nature of intraclustercapabilities for generating innovation or in thefactors which may in¯uence whether and howthose capabilities are accumulated.

(i) Technology as machinery, and the limitedscope of ``technological change''

An emphasis on technology as machinery isquite pervasive in the studies. This is probablymost evident in Sandee's research on tile mak-ing in Indonesia and least evident in Visser'sstudy of garment-manufacturing in Lima.Others are more ambiguous. For instance, withreference to the products produced by the tex-tile industry in Tiruppur, Cawthorne (1995)notes that ``. . .the quality of such garments hasimproved beyond recognition over a 10-yearperiod. . .'' (p. 46). But she then also notes that

there is relatively little technological change orimprovement in the industry as a whole in Ti-ruppur (p. 49). This apparent contradictionseems to arise because the author's commentsabout technological change were almost en-tirely concerned with machinery, while the im-provement in product quality was not identi®edas ``technological.''

Ambiguity is also shown in Nadvi's fasci-nating study of the long-term evolution of thesurgical instrument cluster in Sialkot (1996). Alarge part of the study is concerned with issuesof product quality, and the manifold forms oftechnological change underlying quality. Inparticular, Nadvi emphasizes the importance ofwhat he calls ``soft technologies''Ðaspects ofthe procedures and organizational arrange-ments used in production. When questionsabout technology and technological change aredirectly addressed, however, machinery and itsacquisition tend to occupy center-stage. Thisemphasis is continued in a subsequent paperbased on the research: `Ìn an industry relianton traditional knowledge and metalworkingexpertise, the adoption of new technologies hasbeen relatively simple in that the logic of newmachinery can be easily understood'' (Nadvi,1997, p. 6).

Rabellotti also is concerned at one level withdi�erent kinds of technological change, but alarge part of her treatment of this issue focuseson machine-centered changes. Indeed, sheseems to identify capital goods production asthe heart of the technological change processand its absence as a major reason for the ob-servation of limited technological innovation:``...the lack of a domestic industry specialized inthe production of machinery for the shoe sectoris probably the main drawback to increasingtechnological co-operation and introducing in-novations'' (Rabellotti, 1995, p. 144).

Our emphasis on this type of ambiguity is notjust de®nitional nit-picking. The issue has im-portant implications for the conclusions onecan draw from these studies about the nature oftechnological change in clusters. If it were un-ambiguously evident that the researchers hadexplicitly sought to assess technological changeon the basis of a consistent and broad concepttheir conclusions would carry considerable in-terest and weight. In particular, observationsabout technological change in clusters beinglimited to machine-centered forms of changewould highlight important aspects of the``narrowness'' of the technological changeprocess taking place. For instance, if Cawt-


horne's conclusions about the limited signi®-cance of technological change and improve-ment in Tiruppur do re¯ect a carefuldemonstration that rapid growth in output andexports had little or nothing to do with changein the speci®cations of products, processes,materials or production organization, it wouldbe an important and revealing ®nding aboutthe nature of the dynamism underlying thedevelopment of at least this particular cluster.On the other hand, the ambiguous conceptualand methodological basis of the result leavesone wondering whether the conclusions merelyre¯ect the approach taken in observing ``tech-nological change and improvement.''

(ii) The role of extracluster innovation andintracluster di�usion

Several of the studies reviewed here throwinteresting light on aspects of technologicalchange generated by ®rms inside the cluster.Indeed, this descriptive material seems to allowcomparison across the studies, suggesting forinstance that there is considerable variation inthe relative importance of sources of technol-ogy inside and outside the clusters.

In some cases, external sources appear tohave played a large or even dominant role. Forinstance, in Nadvi's (1996) study of the Sialkotsurgical instrument cluster, links with foreignbuyers were important sources of technologicalchange, as illustrated by the case of German®rms which ``sent out metallurgical engineersfor periods of up to three months to train theSialkot partner ®rm in quality control andproduction engineering'' (p. 117). More strik-ingly, US consultants played a major role inenabling ®rms in the cluster to raise productquality and overcome the threat to their con-tinued existence posed by a US embargo onSialkot instruments on account of their poorquality. External buyers were also criticallyimportant in the initial introduction of newhand-press machinery in the tile-making clusterstudied by Sandee (1995).

In other cases, extracluster sources of newtechnology seem to have been much less im-portant. For instance, Rabellotti's study ofMexican shoe manufacturers suggests thatbuyers played a much less signi®cant techno-logical role, though this seems to have re¯ecteda limited implementation of change derivedfrom any source rather than a di�erent relativeimportance of technology sources within andoutside the cluster. In other cases however, in-ternally generated change seems to have been

the main driving force behind continuing im-provements to products and processes. For in-stance, Tewari emphasizes that the key reasonfor the successful growth of the Ludhianametalworking cluster was the innovativeadaptability of the small ®rms themselves. Inparticular, their cost-cutting process improve-ments and low-cost adaptation and replicationof machinery allows small ®rms to upgradetheir production processes at a relatively rapidrate and low cost.

Thus, the studies appear to have observeddi�erent types of technological change. In sev-eral cases the observations were made througha research lens with a high enough level ofresolution to detect at least some of the ®nedetails of locally generated incremental inno-vation. In this respect they are far removedfrom the 1960s perspective on technologicalchange which, presuming that locally generatedinnovation would be absent, emphasized therole of technology di�usion as the dominanttechnological change process.

On the other hand, one cannot be totallycon®dent in using these observations to drawconclusions about intercluster variation in therelative importance of di�erent sources andprocesses of technological change. One won-ders again about the extent to which the ap-parent di�erences are more a re¯ection ofdi�erent approaches used in the research. Withthe methodologies still in a rather opaque andidiosyncratic phase of development, suchquestions are not easily answered by the reader,and the potential insights from cumulatingcomparative analysis must be largely forgonefor the time being.

This doubt is reinforced by a curious contrastwithin this body of research. On the one hand,as summarized above, several studies identifyinternally generated change as important in atleast some of the clusters and externally ac-quired technology as a major source of changein most of them. Yet, when analysis shifts todraw more general conclusions, for example,about the in¯uence of clustered production onthe process of change, little attention is given toeither of those processes, and interest is focusedalmost exclusively on the di�usion of existingtechnology among ®rms within the cluster. Forinstance, despite the evident importance of ex-ternal linkages as sources of technology in mostof the clusters studied, only Nadvi investigateshow these actually assist industrial clusters indeveloping the means to e�ect technologicalchange. We explore this issue further in the


discussion of ``open'' and ``closed'' knowledgesystems in Section 3(c).

(iii) Intracluster technological capabilitiesPerhaps the most important contribution

made by research on technological change inthe large-scale industrial sector in developingcountries has been its clari®cation of the centralrole of speci®c resources (people, knowledgeand organizational arrangements) as the driversof technological change. In particular, thepervasive signi®cance of these resources hasbeen emphasized. They are not merely con-centrated in a few supply centers for innova-tions but, blurring the boundary betweeninnovation and di�usion, they are widespreadand deeply rooted across the whole spectrum of®rms in technologically dynamic industries andsupply chains.

Given the apparent importance of these re-sources, considerable e�orts have been made tounderstand how they are accumulated and, es-pecially important, how key characteristics ofthese resources change as technology-following®rms and industries in developing countriesevolve along trajectories of increasingly com-plex technology to ``catch up'' with interna-tional technological frontiersÐfor instance,Hobday (1995) on the electronics industry inseveral Asian countries, or Kim (1997) on largechaebol operating in several industries in Ko-rean. To a lesser extent, e�orts have been madeto understand why these innovative techno-logical capabilities are accumulated in di�erentways and at di�erent rates across ®rms andindustries (Katz, 1987; Bell and Pavitt, 1993).

It is in this area where research on industrialclusters in developing countries is still perhapsclosest to the ``1960s'' perspective. As summa-rized in the right-hand column of Table 1, mostof the studies reviewed here gave no attentionto the nature of the knowledge-resources andother capabilities underlying the technicalchange observed in the clusters. None raisedquestions about how such change-generatingcapabilities were acquired and accumulated.This neglect of the resource base for techno-logical dynamism seems to arise partly becauseresearch on clusters has emphasized the im-portance of inter®rm links within spatiallyconcentrated groupings. Intra®rm issues haveattracted much less attention, inevitably in-volving only limited e�orts to identify and un-derstand the speci®c resources underlyingtechnological change. This trend has been re-inforced by perceptions of passive technology

di�usion, rather than creative technical change,as the dominant intracluster process contrib-uting to technological dynamism.

3. KNOWLEDGE SYSTEMS ANDTECHNOLOGICAL CAPABILITIES

In this section we explore a number of con-ceptual issues which seem important in movingforward to build a deeper understanding of thetechnological dynamism of industrial clusters indeveloping countries. These issues hingearound three important distinctions: between``production systems'' and ``knowledge sys-tems'' involved in clustered industry; between``knowledge-using'' and ``knowledge-changing/creating'' elements within knowledge systems;and between ``open'' and ``closed'' knowledgesystems.

(a) Production systems and knowledge systems

Leaving aside their spatial structure, the keycharacteristics of industrial clusters have usu-ally been de®ned in terms of the materials theyuse and the goods they produce. For instance,``horizontal'' clusters are typically de®ned bythe similarity of the ®rms products; and in``vertically'' linked clusters it is the ¯ows ofmaterials and goods which constitute the keylinkages. When attention has turned to tech-nological change in clusters in developingcountries, these materials-centered structuresand ¯ows have usually remained at the centerof the analysis, both de®ning the system to beobserved and describing its key characteristics.For example, it is not uncommon to considerhow the density of linkages concerned withgoods and materials might in¯uence the di�u-sion of technology.

Yet technological change is essentially aknowledge-centered process. It is knowledgestocks within ®rms and knowledge ¯ows tothem, between them and within them whichunderlie change in the types of goods theyproduce and the methods they use to producethem. We will call these stocks and ¯ows ofknowledge the ``knowledge system,'' and assertthat it is the structure and functioning of thatknowledge system which generates technologi-cal change at particular rates and with partic-ular degrees of continuity and persistence.Consequently, an important initial step in try-ing to understand the varying technologicaldynamism of industrial clusters is to develop a


``map'' of what seem to be the key knowledge¯ows and processes.

This kind of knowledge-centered perspectivehas been emphasized in other related strands ofresearch, for instance by those who have triedto examine technological change in ``innovativemilieux'' in Europe. As noted by Lawson (1997,p. 11), this body of research has sought to``emphasize the importance of linkages between®rmsÐlinkages which are not simply concernedwith material transfers.'' This perspective isdeveloped furthest in Camagni's (1991) explo-ration of learning processes underlying inno-vation among networks of ®rms in innovativemilieux.4 It is also central in a totally di�erentresearch tradition which has focused on inter-actions among ®rms and associated knowledgeinstitutions constituting ``technology systems''Ðthe institutional basis for industrial technol-ogy di�usion and innovation (Carlsson, 1995;Carlsson and Jacobsson, 1997).

These various perspectives all seek to distin-guish knowledge systems from the associatedmaterials-centered systems of production andtradeÐwhat one might call ``production sys-tems''. This distinction is so fundamental to ourargument that is worth explicating our modelhere in more detail.

The production system can be understood toencompass the product designs, materials,machines, labor inputs, and transaction link-ages involved in production of goods to agiven speci®cation. In this sense the concept isvery broad. For example, to describe the ma-chinery component it might be necessary tospecify the type of equipment, but also theparticular operating settings and maintenanceprocedures involved in its use. Materialsspeci®cations and purchasing systems, detailedproduct designs and blue-prints, particular la-bor skills and operating routines, speci®cquality assurance standards, distribution ar-rangements etc. all form elements of a pro-duction system. The existence and nature oftrading linkages and contractual arrangementswith suppliers, buyers, subcontractors are alsoincluded in the concept, as might be details ofthe physical, social and legal infrastructurewhich supports the industry in its currentstate.

In another sense, the production systemconcept is purposely shallow. It de®nes only thestatus quo. A description of the productionsystem in this sense tells us nothing about theevolution of the ®rm or cluster it describes: itshistory, current trajectory or capacity for future

technological change. Although every work-shop, factory or industrial cluster is to someextent an expression of past and presentknowledge embedded in people, organizationsand social institutions, that knowledge stock isnot revealed in a mere description of the pro-duction system as de®ned above.

The knowledge system concept on the otherhand, encompasses those ¯ows of knowledge,stocks of knowledge and organizational sys-tems involved in generating and managingchanges in the products, processes or organi-zation of production. Knowledge ¯ows (in acluster context) can occur into ®rms fromoutside the system, between ®rms (and otherinstitutions) within the system or indeed inter-nally within ®rms themselves. All of these var-ious kinds of knowledge ¯ows may contributeto the accumulation of those knowledge stocksand resources often labeled ``technologicalcapabilities.''5 At one end of a complexity spec-trum these include the most routine productioncapabilities required simply to maintain thee�ciency of an established production systemgiven inevitable variations in the characteristicsof materials, labor and customer requirements.At the other extreme are the most innovativecapabilities required to specify and design newproducts, develop novel machines and installnew processes, establish new channels of supplyand distribution.

Knowledge systems and production systemsobviously overlap, but they are not identical.Actors in one may not be actors in the other.Similarly, knowledge ¯ows may be carriedalong the same channels as those concernedwith market transactions over goodsÐas forinstance in the common case of ¯ows of newtechnology into clusters from external buyers oftheir products. But it is also very clear that insome situations goods-centered linkages playlittle or no role in creating or di�using know-ledgeÐas, for instance, in the case of the linksbetween external buyers and the Mexican shoeclusters examined by Rabellotti (1995).

Thus although knowledge and productionsystems interact with each other, the nature ofthis interaction is highly variable. For instance,in reviewing learning and innovation systems intechnology districts in France, Italy and theUnited States, Storper (1993), p. 450) notes thatthe mere existence of cluster-type productionsystems does not provide an explanation fortheir technological dynamism. Instead, he sug-gests that dynamism emerges from the inter-action between learning processes and


production systems. He illustrates how socialprocesses and institutional structures mediatethat interaction, describing industrial districtswhich exhibit greater technological dynamismas ``learning-rich production systems'' (p. 435).

Understanding why some industrial clustersin developing countries are ``learning-rich'' andothers ``learning-poor'' would make an impor-tant contribution to understanding the tech-nological dimension of their long-termdynamism. As we have described, an initial stepin that direction is to distinguish knowledgesystems from production systems.

(b) Knowledge-using and knowledge-changingelements

Most of the issues which are discussed in thisarticle relate to the processes by which know-ledge stocks or technological capabilities areacquired and accumulated. Before addressingthese however, we must also recognize that in acluster context di�erent kinds of technologicalcapability play di�erent (and possibly unequal)roles in sustaining long-term competitiveness.

(i) Di�erent categories of technologicalcapabilities

Following Dahlman, Ross-Larson andWestphal (1987 it has been usual to placetechnological capabilities in functional catego-ries related to the kind of activities which theyfacilitate, for instance: production capabilities,investment capabilities and linkage capabilities;but often with ``innovation capabilities'' iden-ti®ed separately from these functional catego-ries. As Lall (1992, p. 167) illustrated, however,it is conceptually helpful to locate capabilitiesnot only horizontally in functional categoriesbut also vertically according to the degree ofinnovativeness of those activities. Lall recog-nized that for most functions, capability may liesomewhere on a scale ranging from doingsimple routine activities, through adaptive andduplicative activities, to more original innova-tive activities. In this sense ``innovativeness'' isnot a separate functional category of capabili-ties, but a quality or depth which may beachieved to di�erent extents in all functionalareas.

When we leave aside the functional catego-rization, and concentrate on the depth or in-novativeness of technological capabilities, animportant distinction becomes apparent: someelements of the knowledge system tend to bemore concerned with using, replicating and re-

circulating knowledge that is already estab-lished within the production system, whereasother elements are more involved in acquiring,creating, processing and accumulating newknowledge, so that it can be brought into playin the system. The knowledge-using elements areinvolved, for example, in maintaining or ex-panding capacity using given modes of pro-duction; training workers in establishedoperating procedures, or within a cluster con-text, the imitation of production techniquesused by neighboring ®rms. The knowledge-changing elements are involved, for example, inthe management of innovation processes; inproduct design and development; or in thesearch for, selection, adaptation and assimila-tion of new product or process technology(from outside the cluster).

Although the concepts of knowledge-use andknowledge-change illustrate di�erent ends ofthe same spectrum, the distinction is importantin analyzing clustered knowledge systems as awhole. For instance, one ®rm's search for andadoption of new technology copied entirelyfrom within the cluster, while indicating someinnovativeness on the part of the individual®rm, may add little or nothing to the know-ledge stock of the cluster as a whole. A greatdeal of knowledge exchange, use and replica-tion can occur within a rather inward-lookingcluster systemÐcreating the impression of dy-namism at the individual ®rm levelÐbut nev-ertheless leaving the cluster as a wholetechnologically static.

(ii) Acquisition of technological capabilitiesThere are a wide range of processes by which

®rms may add to their knowledge stock. Fol-lowing Romijn's (1998a) review of studies de-scribing the acquisition of capabilities, we cancollapse most of these into three generalmechanisms.

First, they may be acquired through variousinternal technological activities. These may in-clude the observation of routine productionactivities; the acquisition of knowledge fromundertaking the repair, maintenance or recon-ditioning of equipment; more systematic re-verse engineering or experimentation; or moreformally organized technology development oreven applied research.

Second, knowledge may be acquired fromexternal sources, either relatively passively as aby-product from various kinds of interactionwith the outside world or from a range of moredeliberate and active search e�ortsÐthough the


deliberation and activity may sometimes bemore evident on the part of the knowledgesource than the recipient.

Finally, capabilities may be augmentedthrough various kinds of human capital for-mation at the ®rm levelÐeither via formal andinformal training activities or simply by hiringpeople who already embody the knowledgebeing sought.

However one classi®es learning mechanismsand channels, it is important to recognize thedistinction between strengthening the know-ledge-using-and-replicating elements andstrengthening the knowledge-changing ele-ments of the system. For instance, at one level,internal technological learning in ®rms mayinvolve simply the transmission of existingknowledge and skillsÐas, for instance, in theobservation of production operations by newemployees in order to learn routines and pro-cedures. This ``multiplication'' of existingknowledge may be used, for instance, to expandexisting levels of knowledge-using capability inorder to support a larger scale of production.At another level, however, the e�orts of ®rms tolearn from production, or from the repair,maintenance and reconditioning of equipment,may add to their deeper innovative capabilities.This may be extended into more systematic

e�orts to learn from observation, reverse engi-neering and practical experimentation in orderto enhance capabilities for generating change.

These various sources of skill and knowledgethat contribute to both kinds of learning aresummarized in Table 2, and an important stepin developing deeper understanding aboutlearning in industrial clusters in developingcountries will be to generate descriptive andcomparative material within frameworks alongthese or similar lines. Equally important, re-search needs to take into account that the rel-ative importance of di�erent learningmechanisms changes over time.

(c) ``Open'' and ``closed'' knowledge systems

In seeking to explain the economic andtechnological performance of clustered pro-duction, much analysis has focused on the im-portance of the internal characteristics ofclustersÐfor instance, the characteristics of theproduction/market links between ®rms in thecluster;6 various aspects of the organizationand control of production; the intensity of ac-tive co-operation among ®rms and the partic-ular form it takes; or the organizationalmechanisms for the di�usion of knowledge andskills among ®rms. Considerable attention has

Table 2. Illustrative sources of increased capability in cluster knowledge systems

Sources of increase in knowledge-usingcapabilities

Sources of increase in knowledge-changingcapabilities

Intra®rm sources Passive experience of production (``learningby doing production'')

Generally applicable technological under-standing gained from undertaking investmentactivities (``learning by doing investment'')Active e�orts to adopt or improve speci®c

technologiesImproved practices derived from trial anderror experimentation on speci®c tasks

Generic technological insights gained fromadapting and improving existing technology inuse (``learning by changing'')

Intracluster sources Intracluster mobility of skilled labor Cluster-sourced training in planning, designand technology management

Cluster mediated training in operating skillsand procedures

Creative collaboration between ®rms andcluster-based technology institutions

Know-how di�usion between intraclusterproducers and users of machinery orproduction-related services

Intracluster collaboration in tests and experi-ments to adapt machinery or develop productdesigns

Sources outsidethe cluster

Customers and traders knowledge,speci®cations and product/process advice

Externally sourced training in planning, designand technology management

Machinery and other input suppliersoperational knowledge, advice and training

On-the-job experience in design and engineer-ing with machinery and other input suppliers

Externally linked technical advice andconsultancy services

Collaborative testing or technology develop-ment with technology institutions or ®rmsoutside the clusterExternally linked trade and marketing

information services


also been given to more deeply rooted institu-tional variables underlying such organizationalaspects of clusters,7 for instance, cultural co-herence and social identity; the extent and na-ture of trust relationships; and politicalstructures and processes. These features of theinternal structure and institutional basis ofclusters are evidently important. But this in-ternal orientation of analysis may seriously in-hibit e�orts to explain the longer-termtechnological dynamism of clustered produc-tion.

We have suggested that the characteristics ofknowledge systems associated with clusteredproduction may be a centrally important in-¯uence on its longer-term dynamism, and wehave noted how frequently sources of know-ledge located outside clusters have been identi-®ed as major contributors to their technicalchange and dynamism. In particular, amongthe case studies described in Table 1, Nadvi(1996), Visser (1996) and Sandee (1995) allemphasize the importance of such externalknowledge ¯ows.

This suggests that key features of the know-ledge systems of clusters include not just theirinternal mechanisms for circulating the know-ledge already available and for acquiring newknowledge from the experience of various kindsof ``doing.'' Possibly more important is theiropenness to knowledge ¯ows from outside. Ascattering of empirical material about theseexternal ¯ows suggests that relatively ``closed''knowledge systems may be associated with aninability to sustain competitiveness in thelonger term. For instance, the weak perfor-mance of Mexican footwear producers de-scribed by Rabellotti (1995) might beinterpreted in these terms, as might the deep-ening crisis of the Sialkot surgical instrumentproducers until rescued by a large in¯ow of newknowledge from outside (Nadvi, 1996). More-over, it is interesting that Tewari (1996), havinggiven considerable emphasis to the internalknowledge-circulating and knowledge-accu-mulating processes among Ludhiana metal-workers, speculated that it would not bepossible for the cluster to sustain innovationonly on the basis of endogenous incrementalchange. At some point it would be necessary toacquire much more substantial in¯ows of ex-ternal knowledgeÐto turn outward as she putit. The importance of this distinction betweenrelatively `òpen'' and ``closed'' knowledgesystems has been stressed in di�erent language,by Visser (1996, p. 67): `ìf both data and

preferences have local origins, path and contextdependence may produce lock-in, cognitive in-breeding or èntropic death' Camagni (1991, p.140). External sources of data and experienceare more likely to challenge the receiver, triggerlearning and enhance creativity.''

All this suggests the need to give greater at-tention to the openness of the knowledge sys-tems supporting the technological dynamism ofclusters.8 But questions then arise about theorganizational basis for this technologicalopenness. One of these questions concerns thesize of ®rms which play the role of technolog-ical ``gatekeepers'' in clusters. Although small®rms may play this role, it is evident that manyplay no part at all in the technological opennessof their clusters On the other hand, large ®rmswithin clusters may be critically important inproviding new knowledge inputs for smallerclustered ®rms. This certainly seems to havebeen the case in several industrial clusters inEuropeÐfor instance, in Baden Wurttemberg(Cooke and Morgan, 1998; Schmitz, 1992).Scott (1992) has also highlighted this role in thecase of large systems houses in industrial dis-tricts in southern California. With reference todeveloping countries, Rabellotti (1995) andNadvi (1996) stress that large ®rms may cometo play an important role in the production-systems of industrial clusters, but whether theyplay a similarly important role as ``gatekeep-ers'' in the clusters' knowledge-systems is notclear.

Technology support organizations may alsoplay important knowledge ``gatekeeper'' rolesat the boundary of cluster knowledge-systems.Some of these may be public sector institutes,with varying degrees of support from local®rms, which carry out research, or providetechnical or training servicesÐfor instance, theMetal Industries Development Center in Si-alkot Nadvi (1996, p. 132), or SITRA in theTiruppur knitwear cluster (Cawthorne, 1995).Others may be collectively created organiza-tions acting on behalf of associations of ®rms.Yet others may be commercial organizationsproviding services for fees and so virtually in-distinguishable from industrial ®rms operatingwithin the cluster system - for instance, the re-conditioning teams described by Tewari (1996,p. 176) in Ludhiana. As with large ®rms,however, variability in their e�ectiveness withincluster knowledge systems seems to be com-mon. Some institutes seem to have been highlysuccessful in bringing new knowledge intoclustersÐfor instance, CITER in the textile


district in Carpi, Italy (Murray, 1996). Othershave evidently been much less e�ective.

Understanding the roles played by large®rms, technological ``institutes'' or other orga-nizations as technological ``gatekeepers'' inopen knowledge systems therefore seems to beimportant. E�orts to build that understandingare likely however to be inhibited by clustermodels which overemphasize the importance ofspatial proximity. Humphrey (1995), for in-stance, has noted such constraints and sug-gested setting research on clusters in theframework of commodity chains, as developedby Gere� and others. Important elements ofknowledge systems are likely to run throughsuch chains and may therefore span far morethan ``local'' distances. Indeed, the spatial di-mensions of the knowledge systems associatedwith clustered production may radically changeas the clusters evolve, and research that is tootightly bounded by concepts of spatial prox-imity may be unable to reveal such patterns ofgeographical re-structuring.

4. THE ORGANIZATIONAL BASIS OFTECHNOLOGICAL DYNAMISM: A

FRAMEWORK FOR EXPLORATION

This section seeks to provide a framework forfuture research. As stressed by Schmitz (1995,p. 24) future research on industrial clustersneeds an approach which is dynamic, compar-ative, and includes external linkages. Our keypoint in Section 3 was that if research is to re-veal technological dynamism, a re-focusing isalso needed from production systems toknowledge systems.

In seeking to explain di�ering paths of tech-nological dynamism, it is not clustering per sethat seems to be the most relevant issue. Ratherit is the properties of what we have called theknowledge systems associated with clusteredproduction that are likely to be most relevant,and more speci®cally the kinds of organiza-tional and structural characteristics of know-ledge systems we have discussed. These maywell be in¯uenced by various aspects of clus-tering, but they may also vary widely acrossclusters which are very similar in other respects.Consequently, exploration of the relationshipbetween clustering and technological dynamismneeds to center on the mediating role of theseorganizational characteristics of knowledgesystems. In this last section of the paper wetherefore take a few tentative steps toward the

development of a framework for comparativeanalysis of the organizational basis of thetechnological dynamism of clustered industrialproduction in developing countries.

(a) Key organizational characteristics of clusterknowledge systems

We outline here a framework of organiza-tional characteristics9 which may provide atleast some of the elements of a taxonomicframework for comparative analysis. As indi-cated in Table 3, this framework deals sepa-rately with the two di�erent processesdistinguished earlier: the di�usion and replica-tion of knowledge within clusters, and the ac-quisition and generation of knowledge which isnew to the cluster. Important organizationalcharacteristics of these two processes are listedunder column A.

Drawing on fragments of empirical materialscattered through the literature on industrialclusters and knowledge networks, we then in-dicate under column B how each of these or-ganizational characteristics may take di�erentforms in speci®c situations, suggesting in eachcase a continuous dimension of variation. Forinstance, with respect to the main basis forknowledge di�usion within a clusterÐRow (1)in Table 3Ðwe identify two ends of a spectrumas follows.

ÐAt one extreme, spatial proximity, ratherthan any actively structured organizationalmechanism, is the main basis for di�usion,and knowledge ¯ows between organizationsin a cluster, largely as an unintended ``by-product'' of other activities. Similarly,knowledge ¯ows between people within®rms largely through unstructured learningmechanisms (``by watching,'' etc.). In otherwords, knowledge di�usion arises as a ``pas-sive'' externality derived from the spatialclustering of production activities and theirtechnological similarity. Visser's clothingcluster in Lima seems to have many of thesecharacteristics.ÐAt the other extreme, the movement ofknowledge between agents is to a large ex-tent managed purposefully as an activity inits own right, and organizational structureshave been deliberately created to achievethat di�usion. These may include both ar-rangements and activities within ®rms (e.g.,formally organized training schemes whichacquire knowledge from outside the ®rm


and di�use it among employees), and ar-rangements which act as di�usion channelsbetween ®rms (e.g., training institutes or de-sign/market intelligence organizations).Thus, knowledge di�usion is `àctively''sought and managed, and part of this activemanagement is likely to rest on deliberatecooperation among actors (e.g., in creatingand supporting a design/market intelligenceorganization). Some of the Third Italy clus-ters demonstrate this form of organization(see, for instance, Murray, 1996).

Similar spectra of di�ering organizationalforms underlie the acquisition and generationof new knowledgeÐillustrated in the lower halfof Table 3. For instance, with respect to intra-®rm learning mechanismsÐRow (7)Ðwe candistinguish between two ends of a spectrum asfollows.

ÐAt one extreme, ®rms play a very limitedrole in either acquiring new knowledge fromoutside the cluster or generating such newknowledge themselves. If they do acquirenew knowledge from outside the cluster,they do so ``passively''Ðfor instance, acquir-ing it as a ``by-product'' from transactionssuch as selling products to customers. Simi-larly any new knowledge generation withinthe ®rm is likely to consist of limited exten-sions of existing knowledge arising ``passive-ly'' as a result of learning-by-doing routineproduction tasks. Corresponding to thesepassive learning processes, there are few spe-ci®c organizational arrangements within®rms designed explicitly to acquire or gener-ate knowledge. Several of the clusters in tra-ditional industries in developing countriesappear to demonstrate this form of organi-zational arrangement.

Table 3. Di�erences in the organizational basis of clusters' knowledge systems

A. Organizationalcharacteristics

B. How the characteristics vary

With respect to intra-cluster knowledge di�usion and replication(1) The basis for di�usionamong small ®rms

Spatial proximity and ``passive''externalities

ÿÿÿÿÿÿ! Structured and `àctive''co-operation

(2) The dominant``direction'' of knowledge¯ows

Horizontal: between ®rmsproducing similar gods

ÿÿÿÿÿÿ! Vertical: through user-producer links andproduction chains

(3) The role of technology/training Institutes

Non-existent or crisis-driven/intermittent

ÿÿÿÿÿÿ! Pervasively andconsistently important

(4) The role of large ®rms Unimportant ÿÿÿÿÿÿ! Pervasively importantComposite of 1±4 above Unstructured and ``passive'' ÿÿÿÿÿÿ! Structured, cooperative

and `àctive''

With respect to acquiring or generating new knowledgeWithin a low total of new knowledgeacquisition:

Within a high totalof new knowledgeacquisition:

(5) Knowledge sourcesinternal/external to cluster

High proportion originatingoutside the cluster

ÿÿÿÿÿÿ! high proportion origi-nating inside the cluster

(6) Channels for externalsourcing

Limited and informal channels ÿÿÿÿÿÿ! Pervasive informalchannels plus organized``gatekeepers''

(7) Role of ®rms inlearning

Firms' role relatively small ÿÿÿÿÿÿ! Firms' role relativelylarge

(8) Type of ``learning'' High proportion as a ``by-product''from doing

ÿÿÿÿÿÿ! High proportiongenerated by purposefulsearch

Composite of 5±8 above Unstructured, undirected and``closed''

ÿÿÿÿÿÿ! Structured, purposefuland `òpen''


ÐToward the other extreme, `àctive'' learn-ing processes based on speci®c organization-al structures within ®rms account for arelatively large proportion of the total ¯owof new knowledge that is either acquiredby, or generated within, a cluster. For in-stance, at least in some degree, ®rms developand create their own designs (rather thancopying wholesale the existing speci®cationsof other ®rms products), and they do so onthe basis of some degree of organizationalspecialization within the ®rm (e.g., there ex-ists a person or even a ``department'' whichhas the specialized role of ``designing''). Sev-eral of the Third Italy clusters seem to exhib-it this form of organizational arrangementfor acquiring and generating new know-ledge.

We suggest that these kinds of di�erence inorganizational form are likely to be highlycorrelated, and this permits the construction ofmore aggregated composite categories of or-ganizational di�erence. For instance, with re-spect to knowledge di�usion (upper half ofTable 3), clusters in which knowledge is dif-fused primarily in the form of passive exter-nalities are also likely to be clusters in which alarge proportion of those externalities arise inthe form of ``horizontal'' knowledge ¯ows be-tween ®rms producing similar goods, while thedi�usion role of technology/training institutesand large ®rms is limited. A composite syn-thesis of these forms of organizational ar-rangement for di�usion might be described asorganizationally `ùnstructured and passive.''In contrast, in other clusters which have de-veloped more specialized organizational struc-tures, the organizational form might bedescribed as organizationally ``structured, co-operative and active.''

Similarly, there is likely to be a close corre-lation between di�erences in the organizationalcharacteristics underlying the acquisition ofnew knowledge (lower half of Table 3). Someclusters will acquire and generate relatively lowtotal ¯ows of new knowledge in any period,and a large proportion of this ¯ow is likely tobe acquired from outside the cluster via rela-tively few channels which are mainly informallyorganized. At the same time, in such clusterslittle new knowledge will be acquired or gen-erated by learning within ®rms, with most ofthat arising ``passively'' as a by-product fromvarious kinds of doing. A composite charac-terization of this combination of organizational

forms might be described as organizationally`ùnstructured, undirected and closed.''

Conversely, a cluster in which a relativelylarge proportion of a large total ¯ow of newknowledge originates internally, is also likely tobe a cluster where there are pervasive informalprocesses for acquiring knowledge from outsidethe cluster, together with more formally struc-tured ``gatekeeper'' arrangements for doing so.This combination is likely to be associated witha large proportion of the internally generatednew knowledge being produced within ®rms,with much of that arising from purposefulsearch activities undertaken in relatively spe-cialized organizational structures. This combi-nation of organizational forms might bedescribed as organizationally ``structured, pur-poseful and open.''

(b) Knowledge systems and the long-runevolution of clusters

The taxonomic propositions summarized inTable 3 provide a framework for cross-sec-tional comparative analysis of the knowledgesystems underlying technological dynamism inindustry clusters in developing countries. Thisframework is summarized in a di�erent way inFigure 1 where the vertical and horizontal axesre¯ect respectively di�erence in the organiza-tional basis for (i) intracluster knowledge dif-fusion and (ii) the acquisition/generation ofnew knowledge.

We suggest that comparative analysis withinthis framework would con®rm two broad em-pirical propositions about clusters in more``traditional'' industriesÐthe kinds of clusterwhich have been the subject of most research sofar. First, the organizational bases of theknowledge systems could be relatively un-structured and passive with respect to know-ledge di�usion, and undirected and closed withrespect to creating and acquiring new know-ledge. Second, however, there could neverthe-less be considerable di�erences between clusterswithin that southeast quadrant of the ®gure.

It is tempting to argue that these di�erencesare associated with di�erences in the long termtechnological dynamism and sustainability ofclusters. That might be so, but might also betoo simplistic. The e�ectiveness of di�erentkinds of knowledge system is contingent onbasic characteristics of the particular industriesand technologies involved. The importance ofsuch contingent variables has been noted inresearch on industry clusters and innovation


networks in the industrialized countriesÐforinstance, the importance of the stage of the lifecycle of a cluster's product/technology(Audretsch and Feldman, 1996). In the contextof developing countries, two other kinds ofcontingent variable seem especially impor-tantÐthe complexity of technology involved inthat product sector and the cluster's distancefrom the international technological frontier.

ÐThe underlying ``complexity'' of technolo-gy in any product sector is likely to have asigni®cant in¯uence on the kind of know-ledge system needed to support high ratesof technological dynamism. For instance,the technological dynamism needed to sus-tain the competitiveness of a spatially clus-tered network of ®rms supplyingcomponents and services to the automobileindustry or the oil industry will depend ona knowledge system that is much more orga-nizationally structured and active than thesystem needed to sustain the competitivenessof a cluster of rattan furniture producers.ÐWithin any particular product sector, thedistance behind the international technologi-cal frontier at which the cluster is operatingwill also have an important in¯uence on theorganizational form of a cluster's knowledge

system. When a cluster is operating far behindthe frontier, there will exist a large body of ex-isting knowledge which can be acquired anddi�used to contribute to rapid technologicaldynamism. This will depend on a knowledgesystem with organizational characteristicswhich are very di�erent from those neededto generate, acquire and di�use knowledgewhen ®rms are closer to the internationalfrontier. For instance, as ®rms in the electron-ics industry in Korea and Taiwan closed up onthe international technological frontier, theydeveloped knowledge systems with increas-ingly structured organizational mechanismsto support increasingly active processes forknowledge acquisition and di�usion, withthose systems involving both greater internalknowledge generation and greater opennessto external knowledge sources (Kim and Tun-zelmann, 1998). In e�ect, they moved in anortheasterly direction across Figure 1.

Consequently, as illustrated in Figure 2, onemust disaggregate observed intercluster di�er-ences in knowledge systems into three compo-nents:

(i) knowledge system di�erences arising fromdi�erences in the complexity of technologies

Figure 1. Di�ering organizational forms of knowledge systems in industrial clusters: traditional industries in developingcountries.


in that product sector (i.e. di�erences be-tween A and C or between B and D in Fig-ure 2)(ii) knowledge system di�erences arisingfrom varying distances from the internation-al technological frontier (i.e. di�erences be-tween A and B or between C and D inFigure 2)(iii) knowledge system di�erences that maycontribute to di�ering e�ectiveness in sus-taining the technological dynamism of clus-ters with similar technologies operating atsimilar distances from the international fron-tier (i.e. di�erences within the areas A, B, Cand D).

(c) Concluding questions

The framework we have presented in thissection, illustrated by Table 3 and Figure 2,suggests a number of interesting areas for fu-ture research.

Understanding more about the knowledgesystem di�erences of clusters with similar tech-nologies operating at similar distances from theinternational frontier, should yield useful con-clusions about policy in the relatively short term.But insights into change over time provided bythe framework outlined here may be just as in-

teresting and useful as those derived from cross-sectional di�erences. In particular, it would beilluminating to be able to answer even simplequestions about the paths of change that aresketched as A ) B and C ) D in Figure 2.These trajectories are likely to underlie Pyke andSengenberger's ``high road'' to competitiveness.

In particular, it would be helpful to knowwhether clusters knowledge systems evolvealong the lines suggested in Figure 2 as theymove closer to the international frontier in theirexisting areas of technology. If so, how doesthis organizational evolution take place, whatfactors in¯uence it, and does the rate at which itis pursued by clusters signi®cantly a�ect theirrate of technological development and long-term sustainability?

As this evolution takes place, do the charac-teristics of e�ective knowledge systems continueto di�er between clusters based on more andless complex technologies? If so, what processesof organizational change might be involved ifclusters seek to move along ``super high roads''such as A ) DÐsimultaneously shifting theirproduction into more complex areas of tech-nology and narrowing the distance from theinternational frontier within those areas?

Finally, the framework outlined in this paperprompts questions about yet longer-term evo-

Figure 2. Change in the organizational forms of knowledge systems in industrial clusters: di�ering technologies andcontexts.


lution in the organizational basis of localknowledge systems. As clusters move in anortheasterly direction across Figure 2, movingtoward increasingly active, organizationallystructured and open knowledge systems, doesthe importance of spatial proximity becomeprogressively less important for the e�ective

functioning of those systems? In other words,as clusters catch up with the internationaltechnological frontier, and as they shift pro-duction into increasingly complex areas oftechnology, does clustering become less im-portant for the e�ective functioning of theirknowledge system?

NOTES

1. One striking exception is Romijn (1998a, b).

2. See, for instance: Camagni (1991), Carlsson (1995),

Carlsson and Jacobsson (1997), Coombs et al. (1996),

Edquist (1997), Freeman (1991), Lundvall (1992) and

Nelson (1993).

3. We are deliberately selectively concerned here with

micro-level processes behind technological change in

industrial production systems. Factors at meso- and

macro-level (e.g., the nature of regional and national

innovation systems, economic policy and regulatory

frameworks) clearly also in¯uence the characteristics of

these systems. Our interest here however, lies in under-

standing the processes of learning and change occurring

within localized production systems. Speci®cally we use

research on knowledge systems associated with large

integrated ®rms to suggest new approaches to the

analysis of these processes in systems comprising clusters

of smaller ®rms.

4. It is striking how much of the work on `ìnnovative

milieux'' has been concerned with the characteristics of

the materials they use and what they produce, rather

than with issues about how they acquire and use

knowledge in order to innovate. Camagni (1991) is an

exception.

5. Technological capabilities can be thought of as

bundles of complementary skills and knowledge which,

together with the organizational structures in which they

are embedded, facilitate particular activities in the

production system. They include knowledge which is

embodied in people, codi®ed in manuals and blueprints,

or embedded in organizational arrangements and pro-

cedural routines. Many of the organizational structures

in which those resources are located are private to the

®rm, but they may also be shared or collective in the

sense that they are embodied in creative linkages with

and between other ®rms and institutions.

6. Given the signi®cance of spatial proximity as a core

de®ning feature of industrial clusters, it is surprising how

little systematic attention has been given to the spatial

characteristics of these links. Does the geographical

distance they span have any signi®cant bearing on how

e�ectively clusters function? This issue has been more

systematically addressed in a few studies of industrial

clusters in the advanced industrial economies (e.g.,

Lawson et al., 1997).

7. We follow here the kind of distinction between

òrganizational' and ìnstitutional' dimensions of inno-

vation systems suggested by Edquist and Johnson (1997):

institutions refer to things that pattern behavior such as

routines, norms, shared expectations, and the ground

rules for economic behavior; organizations refer to more

speci®c and concrete formal structures, usually con-

sciously created with explicit purposes (e.g., ®rms, tech-

nical institutes, training centers, business associations).

8. An interesting step in this direction has been taken

recently by Kim and Tunzelmann (1998) who suggest

that an important feature of the electronics industry in

Taiwan was not just the dense patterns of knowledge-

centered interaction among local and national networks

of predominantly small ®rms. It was also the fact that

this `ìnternal'' system of knowledge ¯ows was strongly

coupled via speci®c institutional arrangements into an

international knowledge system.

9. We use the term `òrganizational'' here in the same

sense as indicated in note 7 above, so distinguishing

organizational from `ìnstitutional.'' But we do not use it

in a way which includes the speci®c `òrganizational''

aspects of industrial production itselfÐe.g., JIT produc-

tion methods.

REFERENCES

Albu, M. (1997) ``Technological learning and innovationin industrial clusters in the South,'' SPRU ElectronicWorking Paper No. 7, University of Sussex, Brighton.

Audretsch, D. and Feldman, M. (1996) `Ìnnovativeclusters and the industry life cycle.'' Review ofIndustrial Organisation, Vol. 11, 253±273.


Bell, M. and Pavitt, K. (1993) ``Technological accumu-lation and industrial growth: Contrasts betweendeveloped and developing countries.'' Industrial andCorporate Change, Vol. 2, (2) 157±209.

Bell, M., Scott-Kemmis, D. and Satyarakwit, W. (1982)``Limited learning in infant industry: A case study.''In The Economics of New Technology in DevelopingCountries, eds. Stewart, F. and James, J. FrancesPinter, London, 1982.

Camagni, R. (1991) ``Local milieu, uncertainty andinnovation networks: Towards a new dynamic the-ory of economic space.'' In Innovation Networks:spatial perspectives, eds. Camagni, R. BelhavenPress, London.

Carlsson, B. (1995) Technological Systems and EconomicPerformance: The Case of Factory Automation.Kluwer Academic, Dordrecht.

Carlsson, B. and Jacobsson, S. (1997) ``Diversitycreation and technological systems: A technologypolicy perspective.'' In Systems of Innovation: Tech-nologies, Institutions and Organisations, eds. Edquist,C. Pinter, London and Washington DC.

Cawthorne, P. (1990) `Àmoebic capitalism as a form ofaccumulation: The case of the cotton knitwearindustry in a South Indian town,'' Ph.D. Thesis.Open University, Milton Keynes.

Cawthorne, P. M. (1995) `Òf networks and markets:The rise and rise of a south Indian town. Theexample of Tiruppur's cotton knitwear industry.''World Development, Vol. 23, (1) 43±56.

Cooke, P. and Morgan, K. (1998) The AssociationalEconomy: Firms, Regions and Innovation. OxfordUniversity Press, Oxford.

Coombs et al., R. (1996) Technological Collaboration.Edward Elgar, Cheltenham.

Dahlman, C. J. and Fonseca, F. V. (1987) ``Fromtechnological dependence to technological develop-ment: The case of the Usiminas Steel Plant inBrazil,'' 21, Economic Commission for Latin Amer-ica, Buenos Aires.

Dahlman, C. J., Ross-Larson, B. and Westphal, L. E.(1987) ``Managing technological development: les-sons from the newly industrializing countries.''World Development, Vol. 15, (6) 759±775.

Edquist, C. (Ed.), Systems of Innovation: Technologies,Institutions and Organisations. Pinter, London andWashington.

Edquist, C. and Johnson, B. (1997) `Ìnstitutions andorganisations in systems of innovation.'' In Systemsof Innovation: Technologies, Institutions and Organ-isations, ed. Edquist, C. Pinter, London and Wash-ington, DC.

Enos, J. (1997) ``The adoption of innovations and theassimilation of improvements.'' In Chinese Technol-ogy Transfer in the 1990s, eds. Feinstein, C. andHowe, C. Edward Elgar, Cheltenham.

Freeman, C. (1991) Networks of innovation: A syn-thesis of research issues. Research Policy, Vol. 20,499±514.

Hobday, M. (1995) Innovation in East Asia: The Chal-lenge to Japan. Edward Elgar, Cheltenham.

Hollander, S. (1965) The Sources of Increased E�ciency:Du Pont Rayon Plants. MIT Press, Cambridge MA.

Humphrey, J. (1995) Industrial reorganization in devel-oping countries: from models to trajectories. WorldDevelopment, Vol. 23, (1) 149±162.

Katz, J. (Ed.), Technology Generation in Latin AmericanManufacturing Industries. Macmillan Press, London.

Kim, L. (1997) From Imitation to Innovation: TheDynamics of Korea's Technological Learning. Har-vard Business School Press, Boston, MA.

Kim, S. R. and Tunzelmann, N. V. (1998) `Àligninginternal and external networks: Taiwan's specializa-tion in IT,'' SPRU. University of Sussex, Brighton.

Lall, S. (1992) ``Technological capabilities and indus-trialization.'' World Development, Vol. 20, (2) 165±186.

Lawson, C. (1997) ``Territorial clustering and hightechnology innovation: From industrial districts toinnovative milieux,'' Centre for Business ResearchWorking Paper 54. Cambridge University, Cam-bridge.

Lawson, C. et al. (1997) `Ìnter®rm links betweenregionally clustered high-technology SMES: A com-parison of Cambridge and Oxford innovation net-works.'' In New Technology-based Firms in the 1990sv.4, eds. Oakey, V. and During, R. Paul Chapman,London.

Lundvall, B.-�A. (1992) National Systems of Innovation:Towards a Theory of Innovation and InteractiveLearning. Pinter, London.

Meyer-Stamer, J. (1998) ``Path dependence in regionaldevelopment: Persistence and change in three indus-trial clusters in Santa Catarina, Brazil.'' WorldDevelopment, Vol. 26, (8) 1495±1511.

Murray, R. (1996) `Ìtaly ± a sectoral approach.'' InTechnology Institutes: Strategies for best practice,eds. Rush, H. et al. International Thompson Busi-ness Press, New York.

Nadvi, K. M. (1996) ``Small ®rm industrial districts inPakistan,'' IDS D.Phil thesis. University of Sussex,Brighton.

Nadvi, K. (1997) The cutting edge: Collective e�ciencyand international competitiveness in Pakistan. IDSDiscussion Paper No. 360, Institute of DevelopmentStudies, University of Scussex, Brighton.

Nelson, R. R. (Ed.) (1993) National Innovation Systems:a comparative analysis. Oxford University Press,Oxford and New York.

Rabellotti, R. (1995) `Èxternal economies and cooper-ation in industrial districts: A comparison of Italyand Mexico, IDS D.Phil thesis''. University ofSussex, Brighton.

Romijn, H. (1998a) Acquisition of Technological Cap-abitilities in Small Firms in Developing CountriesMacmillan, Basingstoke.

Romijn, H. (1998b) ``Measuring technological perfor-mance in manufacturing ®rms: A review,'' QueenElizabeth House. University of Oxford, Oxford.

Sandee, H. (1995) `Ìnnovation adoption in rural indus-try: Technological change in roof tile clusters inCentral Java, Indonesia,'' Doctoral thesis. VrijeUniversiteit, Amsterdam.

Schmitz, H. (1992) `Ìndustrial districts: model andreality in Baden-Wurttemberg.'' In Industrial Dis-tricts and Local Economic Regeneration, eds. Pyke, F.


and Sengenberger, W. International Inst. for LabourStudies, Geneva.

Schmitz, H. (1995) ``Small shoemakers and fordistgiants: Tale of a supercluster.'' World Development,Vol. 23, (1) 9±28.

Scott, A. J. (1992) ``The role of large producers inindustrial districts: A case study of high-technologysystems houses in Southern California.'' RegionalStudies, Vol. 26, (3) 265±275.

Storper, M. (1993) ``Regional worlds of production:Learning and innovation in the technological dis-

tricts of France, Italy and the USA.'' RegionalStudies, Vol. 27, (5) 433±455.

Tewari, M. (1996) ``When the marginal becomes main-stream: Lessons from half-century of dynamic small-®rm growth in Ludhiana, India,'' Ph.D Thesis.Massachusetts Institute of Technology, Boston.

Visser, E.-J. (1996) ``Local sources of competitiveness:Spatial clustering and organisational dynamics insmall-scale clothing in Lima, Peru,'' TinbergenInstitute Ph.D Thesis. University of Amsterdam,Amsterdam.


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