CLUSTER ABSORPTIVE CAPACITYWHY DO SOME CLUSTERS FORGE AHEAD AND OTHERS LAG BEHIND?
Elisa GiulianiUniversity of Sussex, UK; University of Pisa, Italy; European University Institute, Florence, Italy
One of the most interesting challenges foreconomists and social scientists in general is tounderstand why some countries forge ahead andothers fall behind (e.g. Abramovitz, 1986; Romer,1992; Verspagen, 1993; Fagerberg et al., 1994). Witha great level of detail, some of them look atunderstanding regional disparities (Martin andSunley, 1998; Caniels and Verspagen, 2001; Cooke,2001) and the phenomena that lead to theemergence and growth of dynamic, successfulindustrial clusters, while others fail miserably(among others, Pyke and Sengenberger, 1992;Steiner, 1998). This paper argues that the dynamicgrowth of a cluster depends on its absorptivecapacity (Cohen and Levinthal, 1990), conceived asthe capacity of clusters to absorb, diffuse andcreatively exploit knowledge that is acquired fromextra-cluster sources. In its definition, clusterabsorptive capacity entails two interrelated aspects:(a) the formation of linkages with extra-clustersources of knowledge (i.e. the extra-clusterknowledge system); and (b) the structuralcharacteristics of the intra-cluster knowledge system(Bell and Albu, 1999). Whereas most cluster studieshave focused on the diffusion and innovationprocess at the intra-cluster level, more limitedconsideration has been given to understanding the
process of absorption of extra-cluster knowledgeand, more specifically, the interplay between intra-and extra-cluster knowledge systems. This papertries to fill the gap. Its original contribution standson the fact that it sets the firm at the centre ofanalysis (Maskell, 2001). In more detail, it developsa conceptual framework explaining the formation ofboth intra- and extra-cluster knowledge systemsbased on the heterogeneity of the firms’ knowledgebases. The interplay between intra- and extra-cluster knowledge systems, moreover, is exploredreferring to the conceptualization of firms thatbehave as technological gatekeepers (Allen, 1977).This is considered to play a critical role inchannelling extra-cluster knowledge into the local,intra-cluster knowledge system and, thus, innarrowing down the gap with the technologicalfrontier. This paper therefore intends toconceptually explore the following researchquestions: How does the heterogeneity of firms’knowledge bases affect the structure of the intra-clusterknowledge system? And, more importantly, How doesit affect the absorption of extra-cluster knowledge?
The paper is organized as follows: The firstsection reviews the literature on clusters in bothadvanced and developing countries. This is donewith the aim of defining the conceptual boundaries
This article provides a firm-centred interpretation ofwhy some industrial clusters forge ahead and otherslag behind. It argues that the dynamic growth of acluster depends on its absorptive capacity and there-fore on the capacity of firms to absorb externalknowledge and diffuse it into the intra-clusterknowledge system. This article speculates on therelationship existing between the heterogeneity of
firms’ knowledge bases with both intra- and extra-cluster knowledge systems. It concludes by illustrat-ing that a conceptual link exists between firm-levelknowledge bases, the cluster absorptive capacity andits potential for growth.
KEY WORDS ★ absorptive capacity ★ firmknowledge base ★ industrial clusters
of the cluster definition. This concept was in factwidely studied during the 1980s and 1990s, whichposes a question of consistency betweenterminologies and concepts used in the literature.The section concludes with a definition of ‘cluster’as it is used in this paper, based on meregeographical and sectoral criteria. The next sectioncritically reviews some of the most influentialcontributions dealing with issues of industrialclustering, learning and innovation at the intra-cluster level, and develops an original conceptualframework which links firm-level knowledge basesto the nature of the intra-cluster knowledge system.It continues by reviewing the literature on extra-cluster linkages and provides a definition oftechnological gatekeeping in the cluster. It moreoverconceptually explores the link between theknowledge base of firms and their propensity toestablish external linkages and to behave astechnological gatekeepers. The third section definesthe concept of cluster absorptive capacity, consideringfirm-level knowledge bases, intra- and extra-clusterknowledge systems. It then, through a review ofexisting empirical studies, explores the relationshipbetween cluster absorptive capacity and clusterdynamism. The concluding section provides a guidefor further research and briefly discusses somepolicy implications.
From industrial districts to clusters: a‘workable’ definition
Cluster studies in the advanced and developingworlds
On the stream of the seminal contribution by AlfredMarshall (1920), several studies have beenundertaken over the past 30 years, which have welldocumented the strength and weaknesses of whatfor the time being I will call ‘economic localities’ inthe world. Marshall first introduced the concept ofindustrial district as a concentration of ‘largenumbers of small businesses of a similar kind in thesame locality’ (Marshall, 1920: 277). According toMarshall, the localization of specialized activitiesproduced external economies which were generatedby the presence of three factors: local availability ofinputs; presence of a skilled labour force and
knowledge spillovers. Long after this contribution,an Italian scholar, Giacomo Becattini (1979),stressed the similarities between the metallurgicaland textile-producing areas of Great Britaindescribed by Marshall and certain areas of Italy. Hereferred to the Marshallian industrial district as alocalized social and productive ‘thickening’, heldtogether by a ‘complex and tangled web of externaleconomies and diseconomies, of joint and associatedcosts, of historical and cultural vestiges, whichenvelops both inter-firm and interpersonalrelationships’ (Becattini, 1989: 132). The interest inindustrial districts raised by Becattini at the end ofthe 1970s and during the 1980s was also shared byvarious Italian economists, who became increasinglyinterested in the economics of regions and localities(e.g. Bagnasco, 1977). The industrial district wasthen proposed as an alternative model to the largeFordist firm, and theorists attributed the success ofthis model to several interconnected meso-levelfactors. Among these: the high degree of verticaldivision of labour (Becattini, 1990), the coexistenceof competitive and cooperative behaviours of firms(Dei Ottati, 1991), the flexible use of skilledmanpower (Brusco, 1982), the diffuse innovativecapacity (Bellandi, 1989), and the role played bylocal institutions and associations (Brusco, 1982;1990).
The Marshallian industrial district representsthe historical conceptual antecedent of most clusterstudies. Much after its conceptualization, a series oftheoretical and empirical contributions wereproduced, which studied different types ofeconomic localities in the advanced and thedeveloping worlds. Among the former, one streamof studies was spurred by Piore and Sabel’s (1984)Industrial Divide, which identified flexibility andspecialization as fundamental alternatives to massproduction. The flexible specialization model – beingbased on flexible automation, differentiatedproducts and small batch production – relied ondense networks of firms and subcontractors, whichcombined competition with cooperation. Due to theconceptual similarities with the work undertaken atthat time by Italian scholars of industrial districts,Piore and Sabel contributed to generating aninternational echo on this specific subject of studies(Piore, 1990).
On the wave of the flexible specialization theory,regional studies too gained momentum in the 1980s.
The resurgence of regional economies (Sabel, 1989)generated a proliferation of empirical andtheoretical contributions by economic geographers,sociologists, scholars of innovation studies, and socialscientists in general. Among the most importantcontributors to this literature, there was the Frenchgroup of economic geographers, GREMI (Aydalotand Keeble, 1988), which explicitly stressed thedynamic nature of industrial agglomerations andtheir capability to generate change. They used theterm ‘innovative milieu’ to define such a concept. Bydefinition, the milieu ‘groups together in a coherentwhole a production system, a culture and actors.The coherence between various actors lies in thecommon approach to situations, problems andopportunities’ (Crevoisier et al., 1989: 11) Similarly,Maillat (1990) defined the milieu as comprisingmaterial and non-material elements:
The material elements are organised around a territorialproduction system (nature of companies and their localintegration, coherence of all the activities etc.), the locallabour market and the way it works (nature of jobs, typesof chains of mobility etc.) and the territorial scientificsystem (training system, research institutes, productionand accumulation of regions’ knowledge). These variouselements function in symbiosis and give the milieu itsspecific characteristics. The latter are reinforced by thenon-material elements, particularly by the technicalculture. . . . The technical culture is communicated,acquired and renewed by exchanges and contacts whichrequire a certain proximity. (Maillat, 1990: 345–6)
Competitiveness studies also became interestedin geography in the 1990s. In The CompetitiveAdvantage of Nations (1990), Michael Porterintroduced the importance of clusters forcompetitiveness. As he said, ‘the city or regionbecomes a unique environment for competing in theindustry’ (Porter, 1990: 156). In his work he definedclusters as ‘geographic concentrations ofinterconnected companies, specialised suppliers,service providers, firms in related industries, andassociated institutions (for example, universities,standards agencies and trade associations) inparticular fields that compete but also cooperate’(Porter, 1998: 197–8). Thus, a clear condition forthe existence of a cluster was the presence oflinkages between companies and institutions. Inparticular, these linkages were considered importantfor productivity growth:
Close linkages with buyers, suppliers, and otherinstitutions contribute importantly not only to efficiencybut to the rate of improvement and innovation. . . . Inthis broader and more dynamic view of competition,location affects competitive advantage through itsinfluence on productivity and especially on productivitygrowth. (Porter, 1998: 209)
While linkages were considered fundamental,looser consideration was instead given here to inter-firm geographic proximity, since ‘the geographicscope of a cluster can range from a single city orstate to a country or even a network of neighboringcountries’ (Porter, 1998: 199). Furthermore, unlikeother approaches, social and cultural aspects wereless emphasized in Porter’s definition of cluster,even though he mentioned that the existence of‘repeated, personal relationships and communityties fostering trust facilitate the information flowwithin clusters’ (Porter, 1998: 216–17).
At the end of the 1980s, clustering of economicactivities also became an issue of interest fordeveloping countries. In particular, drawing onsuccessful experiences of more advanced countries,clustering was seen as a strategy to overcome growthconstraints of small informal firms (Aftab andRahim, 1989; Schmitz, 1989; Humphrey andSchmitz, 1996; Van Dijk and Rabellotti, 1997).Pioneering cluster studies in developing countriesemphasized the importance of ‘collective efficiency’for growth and competitiveness. According toSchmitz (1999), in fact: ‘clustering opens upefficiency gains which individual enterprises canrarely attain. These gains are captured in theconcept of collective efficiency, defined as thecompetitive advantage derived from local externaleconomies and joint action’ (Schmitz, 1999: 141).
The collective efficiency approach was based onthe idea that static efficiency gains were notsufficient to allow firms in developing countries tocompete on a ‘high road’ of development (Pyke etal., 1990). This was instead possible when firms inclusters fostered joint action and thereforehorizontal and vertical cooperation between localproducers and local institutional bodies (Schmitz,1995). Thus, this approach emphasized theimportance of intra-cluster cooperative productivelinkages, which constituted a fundamental part ofthe collective efficiency framework. This approachadopted a rather simple conceptualization of cluster(Schmitz, 1995; Humphrey and Schmitz, 1996),
which in fact was defined as: ‘A sectoral andgeographical concentration of enterprises. Whetherspecialization and cooperation develop is considereda matter for empirical research and not subsumed inthe definition’ (Humphrey and Schmitz, 1996:1863). This definition reflects the fact that thecontext of developing countries is such that thetypical features of advanced countries’ economiclocalities (e.g. presence of social linkages, ofinstitutions etc.) are not easy to find empirically.
Finally, during the 1990s, Latin American scholars(e.g. Cassiolato and Lastres, 1999; Cassiolato et al.,2003) proposed analysing the processes of learningand capability accumulation of what they defined aslocal productive systems by using a systems ofinnovation approach (Lundvall, 1992; Nelson,1993). By local productive systems they meant:
Any productive agglomeration involving economic,political and social agents localized in the same area,performing related economic activities and presentingconsistent articulation, interaction, co-operation andlearning processes. It includes not only firms (producersof final goods and services, suppliers of inputs andequipment, service providers etc.) and their differentforms of representation and association, but also otherpublic and private institutions and organisationsspecialised in educating and training human resourcesR&D, engineering, promotion, financing etc. (Cassiolatoet al., 2003: 23)
Different approaches seem therefore to havedeveloped their own workable definition of theeconomic locality. In the following section, I willcompare such definitions and provide anexplanation for the definition of ‘cluster’ which isadopted in this paper.
A workable definition of ‘cluster’
The overview of schools of thought and definitionspresented in the previous section covers onlypartially the enormous empirical and theoreticalliterature which has been produced on the issue inrecent years. One result of this enormousproduction is that there has been a tendencytowards hybridization of the original concept ofMarshallian industrial district, so that differentdefinitions have been proposed to address very
similar economic phenomena, while, in other cases,very similar definitions have been adopted todescribe different concepts. Beside this, the lack ofcommon agreement on the definition of what I haveso far called ‘economic locality’ has generated aterminological melting-pot which blurs theboundaries of the phenomenon studied. As anexample, Table 1 shows how concepts varyaccording to whether, beyond geographicconcentration, the definition also includes the samesectoral specialization of firms (the rows) combinedwith: (a) no other a priori characteristics; (b) thepresence of inter-firm social linkages; (c) thepresence of inter-firm learning and innovationlinkages (the columns).
Clarification of the various definitions of‘economic localities’ is necessary for several sets ofreasons: first, because, as suggested by Markusen(2003), in regional studies, fuzzy concepts haveproliferated which lack conceptual clarity and aretherefore difficult to operationalize – a conditionwhich makes it difficult to test any cause–effectrelation between the presence of givencharacteristics (at the micro or the meso level) andtheir outcomes. Second, definitions matter becauseviewing the Marshallian Italian industrial district asa dominant paradigmatic model – whose virtues areso unique that they cannot even be compared toother similar forms of localized industrialagglomerations – seems an excessively myopicperspective (Rabellotti, 1995). Third, clusters tendto change over time, both in absolute terms,consistent with the evolution of their member firmsand workers, and in relative terms, compared withother clusters. On the basis of this, attributing acrystallized set of attributes to these ever-changingagglomerative productive phenomena is of limitedvalue. It is more useful, instead, to focus on what isless likely to change over the long term: thegeographic space and the productive specialization.
For these reasons, I will use the concept ofcluster to refer to a geographical agglomeration offirms operating in the same industry – as defined inCell A of Table 1. This definition is very simple.With few exceptions, it is considerably simpler thanmost of those discussed above at the beginning ofthe first section. However, it does not imply thatwithin a cluster there is nothing more thangeographic proximity and productive specialization,but that what is there – in terms of, for example,
social and learning linkages – is not there bydefinition, as previously remarked by Schmitz(1995).
Intra- and extra-cluster knowledgesystems and the knowledge base of firms
The intra-cluster knowledge system
Literature review on concepts related to the intra-cluster knowledge system The intra-cluster knowledgesystem is defined here as the flows of knowledgelinking firms in the cluster. This section reviewssome of the main influential contributions relatingindustrial clusters to processes of knowledgediffusion and generation at the intra-cluster level.
In this respect, it seems fair to start this sectionby suggesting that flows of knowledge in clusters areoften associated with the concept of knowledgespillovers, which are conceived as leaks of knowledgewhich diffuse into the economic system and are ‘inthe air’, available as a public good (Arrow, 1962).One of the claimed characteristics of knowledge
spillovers is that they tend to be highly localized(Jaffe, 1989; Jaffe et al., 1993) and severalcontributions have shown that a relationship existsbetween spatial clustering, knowledge spillovers,learning and innovative output (Audretsch andFeldman, 1996; Baptista, 2000).
In particular, what makes geographicallybounded spaces highly conducive to knowledgespillovers is the fact that they allow tacit knowledge,which is sticky (von Hippel, 1994) and highlylocalized in principle (Nelson and Winter, 1982;Pavitt, 1987), to be transferred easily.1 Tacitknowledge is embodied in people and difficult toexpress through codified language, therefore itneeds face-to-face contact and direct interaction tobe transmitted. Thus, informal conversationsbetween technicians or workers, labour mobility,imitative behaviours, are all means of transmittingsuch tacit knowledge.
Clusters are seen as an ideal locus for this type ofknowledge diffusion process. This is considered tobe due not only to the reduced geographical distancebetween people and firms, but also to the fact thatthey share the same or complementary interests intheir work, that is by operating within the same
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GIULIANI: CLUSTER ABSORPTIVE CAPACITY 273
Table 1 Different definitions of ‘economic localities’
No other a priori Learning andcharacteristics Social linkages innovation linkages
Geographical A B Cagglomeration plus Industrial district Industrial district Technology districtsectoral specialization Marshall, 1920 Becattini, 1979; 1990 Storper, 1997
Cluster Industrial locality Local innovation systemSwann & Prevezer, 1998 Scott, 1998 Cassiolato et al., 2003
Cluster Innovative cluster Local productive systemHumphrey & Schmitz, 1996 Simmie & Sennet, 1999 Cassiolato et al., 2003
Localized productive Local productive system Clustersystem Garofoli, 1989; 1991 Porter, 1998Belussi & Pilotti, 2001
Specialized area Industrial clusterCapello, 1999 Morosini, 1994
Geographical D E Fagglomeration only Regional cluster Milieu Technological district
Enright, 1996 Capello, 1999 Antonelli, 2000
Productive arrangement System area Innovative milieuCassiolato et al., 2003 Garofoli, 1991 Camagni, 1991; Gordon, 1991
industry. Furthermore, whenever the cluster ischaracterized by a vertical division of labour, it islikely that user–producer linkages are established,which promote processes of informal interactionand incremental learning (Lundvall, 1988). Thesame is considered to occur in horizontal relationsbetween potentially rival firms: unintentional leaksof knowledge are believed to happen between nearbyfirms (Bellandi, 1989). Finally, industrial clusters,being a spatially localized set of economic activities,are also envisaged as a locus where social relations areentangled with productive ones. Socialembeddedness (Granovetter, 1985) is said togenerate an environment of trustworthy relationswhich enhance knowledge exchange and at the sametime promote a process of social monitoring amongcolocalized producers and technicians (e.g.Becattini, 1990).
According to what has been suggested above,clusters are often considered loci where knowledgeis easily diffused, and, particularly, where tacitknowledge – which is inherently a private good(Nelson and Winter, 1982) – becomes a public,freely available good. This serendipity creates anenvironment which not only supports technicaldiffusion processes, but also favours incrementalinnovation through joint problem solving ofcolocalized producers.
The arguments outlined above were seminallydescribed by A. Marshall (1920), who, in hisPrinciples of Economics, wrote:
. . . so great are the advantages which people followingthe same skilled trade get from near neighbourhood toone another. The mysteries of the trade become nomysteries; but are as it were in the air, and children learnmany of them, unconsciously. (Marshall, 1920: 225)
In Industry and Trade (1919), Marshall defined thisas ‘industrial atmosphere’,2 which was conceived asa sort of technological externality from which firms took advantage, in a rather unstructured way,for being located within the district. In particular,the industrial atmosphere concept has beenassociated with the idea that knowledge was ‘in theair’ and hence freely available locally as a publicgood.
While Marshall described Lancashire’s cottonand Sheffield’s cutlery industrial districts of the19th Century, more recent contributions have
stressed similar phenomena in other industrialdistricts of that same period: for example, Allen(1983) described the collective invention process ofblast furnaces in the Cleveland industrial district inEngland. He showed that the free exchange ofinformation about new techniques and plant designsamong firms of the cluster generated a spiral ofincremental innovation which was directed toimprove the efficiency of blasts by progressivelyincreasing their height and temperature. As Allenput it, the process of collective invention occurredbecause, first:
. . . the large overall increases in height and temperaturewere the culmination of a series of small increments.Second, firms made public the operating results of theirnew furnaces. Third, firms that build taller or highertemperature furnaces used the information generated byexisting furnaces. (Allen, 1983: 4–5)
A similar account was reported by Nuvolari (2004),who provided a collective invention interpretation ofthe steam pumping engine’s technical changeprocesses in Cornwall during the British IndustrialRevolution.
These accounts have shown that the joint effortof different entrepreneurs in districts managed tofoster a wave of incremental improvement on agiven technological process. In more recent times,the relationship between clustering and learning hasalso been emphasized by many scholars. In most ofthese accounts, learning and incremental innovationare considered highly interwoven activities. Andmeso-level forces are often advocated to explain theprocess of knowledge generation in clusters.
In the 1980s and 1990s, several Italian scholarshighlighted the importance of the industrialatmosphere for the innovation potential of Italianindustrial districts (Becattini, 1990). This wasbecause industrial districts were seen as loci ofunique competences, which have been accumulatedover time (Bellandi, 1989; Belussi and Gottardi,2000): ‘Agglomeration favours the transmission ofinformation even among rival firms through inter-firm mobility of skilled personnel; exchange of ideaswithin local institutions; locally rooteddemonstration effects – I see my competitor manageto solve certain sub-furniture problems: so showingme a way of solving my own problems, which aresimilar to his’ (Bellandi, 1989: 163 [my translation]).
The industrial district was therefore viewed as acognitive laboratory (Becattini and Rullani, 1993) ora creative milieu almost by definition.
This view of knowledge diffusion and generation is also found in the French literature ofGREMI. As suggested by Perrin (1991), the localmilieu is viewed as a generator of innovativebehaviour:
. . . the fact that physical externalities and the know-howof the localized actors are internalized within the sameterritory involves a potential reinforcement process.Moreover, since externalities are collective goods, theycan be appreciated not only by the actors who havecontributed to their promotion but also by larger groupsso that other local agents may be incited to join thenetworks. (Perrin, 1991: 42)
The innovative environment of the milieu is tied tothe presence of processes of collective learning and ofreduction of the elements of dynamic uncertainty.Camagni (1991: 3) defined the innovative milieu (i.e.milieu innovateur) as the ‘set, or the complexnetwork of mainly informal social relationships on alimited geographical area . . . which enhance thelocal innovative capability through synergetic andcollective learning processes’. Capello (1999) further defined collective learning as a socialprocess, in which:
. . . the mechanisms for the spatial transfer of knowledgeare social because new knowledge is transferred to otheragents, whatever the will of the original inventor, thanksto common technological, organizational andinstitutional routines and behaviours which facilitate thesharing of information and know-how. In other words,the outcome of the innovative process becomes a publicgood. (Capello, 1999: 365)
In the competitiveness literature, too, Porter (1990:156) conformed to this thinking, when he statedthat: ‘The information flow, visibility, and mutualreinforcement within such a locale give meaning toAlfred Marshall’s insightful observation that insome places an industry is “in the air”’. While heemphasized, on the one hand, that ‘a concentrationof rivals, customers, and suppliers will promoteefficiencies and specialisation’ (1990: 156), Porteralso remarked that ‘more important, however, is theinfluence of geographic concentration onimprovement and innovation’ (1990: 157), since
‘proximity increases the speed of information flowwithin the national industry and the rate at whichinnovations diffuse’ (1990: 157). In the same spirit,Saxenian (1994) described the formation of a‘technical community’ in Silicon Valley, formed bytechnician entrepreneurs with high collectiveidentity, as a critical element to generate anenvironment of informal socialization whichboosted innovation:
Every year there was some place, the Wagon Wheel,Chez Ivonne, Rickey’s, the Roundhouse, where membersof this esoteric fraternity, the young men and women ofthe semiconductor industry, would head after work tohave a drink and gossip and brag and trade war storiesabout phase jitters, phantom circuits, bubble memories,pulse trains, bounceless contacts, burst modes, leapfrogtests, p-n junctions, sleeping sickness modes, slow-deathepisodes, RAMs, NAKs, MOSes, PCMs, PROMs,PROM blowers, PROM blasters, and teramagnitudes,meaning multiples of a million millions. (Tom Wolfe,quoted in Saxenian, 1994: 32–3)
Preliminary contributions in the developingcountry literature also aligned with the viewsreported above. In fact, spillovers and information‘in the air’ were often mentioned as main explicativefactors of knowledge transfer and ultimately as anincentive to innovation. In the case of the surgicalinstrument cluster in Sialkot (Pakistan), Nadvi(1999) suggested that:
. . . upgrading requires a capacity to learn . . . both at thelevel of the individual firm and in the relations betweenfirms. In the cluster context, knowledge spillovers canfacilitate such learning (Audretsch and Feldman, 1996).Moreover, cluster-wide bodies and real service centrescan accelerate the dissemination of know-how amonglocal producers (Brusco, 1982). (Nadvi, 1999; 1606;emphasis added)
Similarly Meyer-Stamer (1998), with reference tothe tile cluster in Santa Catarina (Brazil), suggestedthat:
. . . it seems that in the ceramic tile cluster there is atleast some information in the air. There is substantialinformal information exchange going on betweenprofessionals from tile producers; unlike in otherbranches, it is perfectly normal for them to visitcompetitors’ factories. (Meyer-Stamer, 1998; 1505;emphasis added)
More explicitly, McCormick (1999) stated that‘given that one of the major characteristics ofdeveloping countries is their weak technologicalbase, technological spillovers within a cluster arecrucial to its upgrading and ultimately to industrialdevelopment’ (1999: 1533).
More recent works carried out by developingcountries’ scholars, however, have started tohighlight that knowledge diffusion in clusters maynot circulate as smoothly as in contexts where firmsperform frontier in-house R&D, employ highlyskilled human resources, which operate underconstant stimuli to innovate and improve their owntechnical background – i.e. the advanced world.More convincingly, they are characterized by ahigher variability, with differing capacities forgenerating change and innovation (Bell and Albu,1999; Caniels and Romijn, 2003; Schmitz, 2004;Giuliani et al., 2005b). This aspect is explored in thesections which follow.
Firm knowledge bases and the intra-cluster knowledgesystem Recent contributions have expressed theirconceptual discontent with the almost externality-driven interpretation of cluster capacity to learn andinnovate as discussed in the second section above(e.g. Breschi and Lissoni, 2001). Some of them havenoted the need to bring firm-level learning into theanalysis of clusters’ innovation (Maskell, 2001). Belland Albu (1999: 1722), for example, mentioned that:
. . . most of the studies . . . gave no attention to thenature of the knowledge-resources and other capabilitiesunderlying the technical change observed in clusters.None raised questions about how such change-generating capabilities were acquired and accumulated.This neglect of the resource base for technologicaldynamism seems to arise partly because research onclusters has emphasised the importance of inter-firmlinks within spatially concentrated groupings. Intra-firmissues have attracted much less attention, inevitablyinvolving only limited efforts to identify and understandthe specific resources underlying technological change.This trend has been reinforced by perceptions of passivetechnology diffusion, rather than creative technicalchange, as the dominant intracluster processcontribution to technological dynamism.
In the same vein Martin and Sunley (2003: 17)argued that: ‘In too many accounts local “territoriallearning” is privileged, yet what this process
actually is remains ambiguous and its interactionswith firm-based learning are left completelyunexamined (Hudson, 1999)’. This section tries tofill this conceptual gap and explores the followingresearch question: How do firm knowledge basesinfluence the intra-cluster knowledge system?
A relevant concept here is the ‘knowledge base ofthe firm’. On the basis of Nelson and Winter (1982)and Dosi (1988), the knowledge base of a firm isdefined as a ‘set of information inputs, knowledgeand capabilities that inventors draw on when lookingfor innovative solutions’ (Dosi, 1988: 1126).Knowledge is seen as residing in firms’ skilledemployees, who embody tacit capabilities, and at thesame time it is not merely the sum of eachindividual’s knowledge since it resides in theorganizational memory of firms. As Nelson andWinter (1982) put it:
. . . [t]he possession of technical ‘knowledge’ is anattribute of the firm as a whole, as an organized entity,and it is not reducible to what any single individualknows, or even to any simple aggregation of the variouscompetences and capabilities of all the variousindividuals, equipments, and installations of the firm.(Nelson and Winter, 1982: 63).
This knowledge base is considered the result of aprocess of cumulative learning, which is inherentlyimperfect, complex and path-dependent (Dosi, 1997):imperfect, because of the uncertain nature oftechnical change and of agents’ bounded rationality;complex, because – as suggested by Kline andRosenberg (1986) and Freeman (1994) – learningand innovation are not linear processes but ratherthe result of ‘persistent feedback loops betweeninnovation, diffusion and endogenous generation offurther opportunities of advancement’ (Dosi, 1997:1536); and path-dependent because ‘pasttechnological achievements influence futureachievements via the specificity of knowledge thatthey entail, the development of specificinfrastructures, the emergence of various sorts ofincreasing returns and non-convexities in thenotional set of technological options’ (Dosi, 1991:183). The features of the learning and technicalchange process deliver persistent heterogeneityamong firms in the economic system and,understandably, within the cluster’s boundaries.
bases within the cluster makes it reasonable to claimthat firms are able to establish knowledge linkages atintra-cluster levels, according to the absolute andrelative strength of their knowledge bases. Absolute,because I expect that firms with stronger knowledgebases will be more likely to establish knowledgelinkages than firms with weaker knowledge bases.On the one hand, this is due to the fact that theformer know more and therefore have more totransfer. On the other hand, it is associated with thefact that firms with particularly strong knowledgebases are likely to be perceived by other clusterfirms as ‘technological leaders’ or ‘early adopters’ oftechnologies in the local area, leading to them beingsought out as sources of advice and knowledge moreoften than firms with weaker knowledge bases. Bythe same token, firms with very limited or weakknowledge bases are less exposed by being askedtechnical knowledge by other cluster firms.
The transfer of knowledge should also bedependent on the relative cognitive distancebetween any two firms in the cluster (Lane andLubatkin, 1998). Several contributions have in factemphasized that the propensity of firms to establishknowledge linkages with other firms is associatedwith the degree of similarity/dissimilarity in theirknowledge bases (Rogers, 1983). In particular, firmswith too distant knowledge bases might be incapableof fruitfully transferring each other’s knowledge.Consequently, it seems reasonable to assume thatfirms will show different ‘cognitive positions’(Giuliani and Bell, 2005), depending on: (a) theamount of knowledge they have accumulated overtime and can therefore release to others; and (b)their capacity to decode and absorb knowledge thatis potentially transferable from other firms. If theserules apply, and given the heterogeneity of firms’knowledge bases, I expect that knowledge does notdiffuse in clusters evenly as if it were ‘in the air’(Marshall, 1920), or mutually between firms. On theone hand, I agree that there is a tendency towardsmutual knowledge exchange (Coleman, 1990), as‘reciprocity appears to be one of the fundamentalrules governing information trading’ (Schrader,1991: 154). Nevertheless, this is most likely to occurwhen there is a high degree of similarity betweenthe level of firms’ knowledge bases so that theyshare similar experiences and new, recently acquiredpieces of technical knowledge. In this case, firms
would behave as ‘mutual exchangers’ of knowledge.The inter-firm transfer of knowledge (within thecluster), however, need not involve only a set ofbalanced, mutual exchanges between firms withsimilar knowledge bases. It seems likely thatdifferences between the knowledge bases of firmswill lead them to play differing, sometimesasymmetric roles within the cluster knowledgesystem. Heterogeneous knowledge bases are likely tolead to a degree of imbalance in the knowledgeinteractions of the firms. Firms with strongknowledge bases, for example, are less likely to seekout useful knowledge from firms with weakerknowledge bases – as suggested by Schrader (1991:166): ‘even if the inquirer is eager to reciprocate, hisor her cooperativeness remains without economicvalue if no relevant information exists that could bereturned’. Some firms may therefore transfer moreknowledge than they receive from other local firms,so acting as net ‘sources’ within the clusterknowledge system.
As said, moreover, the propensity to acquireknowledge from other cluster firms is also shaped bythe perceived relative cognitive distance from thesource of knowledge. Firms have more incentives toask for technical advice when they know that theywill be able to decode and apply the receivedknowledge (Carter, 1989). Consequently, while thesimilar levels of their knowledge bases may lead somefirms into balanced exchange, other firms with lowerbut still significant capacities are likely to absorbmore knowledge than they release, so acting as net‘absorbers’ within the cluster knowledge system.
Finally, however, the knowledge base of somefirms may be so low that it neither offers anything ofvalue to other firms nor provides a capacity toacquire and exploit knowledge that others may have.Such firms are likely to be ‘isolated’ within thecluster knowledge system.
Hence, the intra-cluster knowledge system islikely to be characterized by different structuralcharacteristics according to the absolute and relativeknowledge bases of firms. Densely connectedknowledge systems should be associated with thepresence of firms with strong knowledge bases.Conversely, the predominance of firms with weakknowledge bases is associable to highly disconnectedand fragmented knowledge systems at the intra-cluster level.
Literature review on concepts related to the extra-cluster knowledge system At the end of the 1980s,special attention was given to the nexus betweenlocal–global communities and worlds of production(Alger, 1988). The process of globalization (Cookeand Kirkpatrick, 1997) and the new internationaldivision of labour (Frobel et al., 1980; Dicken, 1992)emphasized the importance of local productionsystems being interconnected with distant markets,both in terms of demand and supply. Increasedinterdependence on global resources, though, didnot mean undermining local resources andcapabilities (Dicken, 1994). In fact, to sustaininternational competition, local and globalknowledge systems should be aligned (Kim and vonTunzelmann, 1998).
While neo-Marshallian studies have longemphasized the endogenous potential of knowledgegeneration in districts, other contributions havestarted to view districts as neo-Marshallian nodes inglobal networks (Amin and Thrift, 1992). Thisperspective suggested the importance of extra-cluster networking and the acquisition of extra-cluster knowledge to avoid phenomena of entropicdeath and negative lock-in (Grabher, 1993) and toallow local competencies to be nurtured byknowledge transferred from non-local resources. Asstressed by Camagni (1991:4):
The attraction of external energies and know-how isexactly the objective we assign to innovation networks:through formalized and selective linkages with theexternal world (or, very often, with external andspecialized ‘milieux’) local firms may attract thecomplementary assets they need to proceed in theeconomic and technical race. Internal, mainly informaland tacit linkages may not be sufficient to achieve themain goal, especially in times of rapid economic andtechnological change.
The importance of extra-cluster networking hasbeen increasingly highlighted by the clusterliterature in both advanced and developing countrycontexts (Becattini and Rullani, 1993; Bell and Albu,1999; Humphrey and Schmitz, 2002; Bathelt et al.,2004) and several contributions have now exploredthe processes by which the integration of
extra-cluster and intra-cluster knowledge occurs(see e.g. Schmitz, 2004; Giuliani et al., 2005b).Meanwhile, other studies have documented thepresence of firms that are relevant for the externalopenness of a cluster. They have shown that theinflow of knowledge into a cluster can be bothdriven by actors from outside which are attracted intothe cluster by the availability of natural orknowledge resources as well as by local actors whotry to tap into outside knowledge (Cantwell andIammarino, 2003). Among the former, key actors ofthe local–global nexus are those multinationalcorporations (Belderbos et al., 2001; Castellani andZanfei, 2002) that establish production plants in alocal cluster or operate as global buyers, exerting aquasi-hierarchical form of governance on clusteredfirms (Gereffi and Korzeniewicz, 1994; Giuliani et al.2005a; Halder and Nadvi, 2002; Humphrey andSchmitz, 2002; Kishimoto, 2003). Among the latter– i.e. the local actors who try to tap into outsideknowledge – the literature emphasizes the role ofthe leading firms (e.g. Lazerson and Lorenzoni,1999), which are typically large, technologicallyadvanced firms (Albino et al., 1999) and areregarded as engines of cluster development.
Great interest has recently been expressed in thetechnological gatekeeping process of such firms (Belland Albu, 1999; Giuliani and Bell, 2005). Theconcept of technological gatekeeping was originallydeveloped in the arena of intra-organizationalstudies (Allen, 1977) to indicate those ‘key peoplewho differed from their colleagues in the degree towhich they exposed themselves to sources oftechnical information outside their organization [. . .] and they tend to be the same people to whomothers come for information’ (Allen, 1977: 145).Similarly, in a more recent contribution,MacDonald and Williams (1994: 123) definedtechnological gatekeeper as ‘an individual whofunnels information into an organisation from theoutside world’. In this context, technologicalgatekeepers were professionals who, due to theirhigher propensity to search for new knowledge fromoutside the firm, became acknowledgeable referencepoints for other people inside the firm to go to foradvice. For this reason, these professionals showed anatural propensity to transfer extra-firm acquiredknowledge to other colleagues:
. . . the gatekeeper is selective in the information heacquires and proactive in acquiring it. . . . Although thegatekeeper may well have his own use for theinformation he acquires, he is also keenly interested inpassing it on to others in the organisation for their use.(MacDonald and Williams, 1994: 125)
In the context of this paper, technologicalgatekeepers are conceived as firms which channelextra-cluster knowledge into the local, intra-clusterknowledge system (Giuliani, 2002). Indeed, thisdefinition can be attributed to Gambardella (1993),who, in a conceptual paper, pointed out that forsmall regional systems in Italy to have access to(extra-cluster) abstract knowledge they needed tocreate the conditions for the existence ofintermediate agents (which he named technologicalgatekeepers) which connected extra-cluster abstractknowledge and concrete knowledge accumulated bylocal firms. Gambardella argued that these types ofagents needed to be private firms, which operatedaccording to the rules and incentive systems of themarket. Similarly to the intra-organizationalliterature, technological gatekeepers are notmanifestly so and they are not easy to detect amongother firms in the cluster. This is because, first,their role is not institutionalized, which means thatthey have not been formally attributed this role atthe local level; and, second, their function is likely tobe the result of highly informal interactions withboth intra- and extra-cluster actors – a conditionwhich renders their visibility limited to the externalobserver. In spite of their latent nature,technological gatekeepers are vital nodes ofinterconnection between intra- and extra-clusterknowledge systems and, therefore, they maypositively impact on the cluster absorptive capacity.Identifying them, understanding theircharacteristics vis a vis those of the other clusterfirms, should therefore be a priority in research.Gambardella (1993) suggested technologicalgatekeepers are characterized by high technicalcompetencies and by being able to translate theacquired external knowledge into know-how thatcan be used by those other firms in the clusterwhich have concrete idiosyncratic knowledge. Thefollowing section will elaborate on this further.
Firm knowledge bases and extra-cluster knowledgeabsorption This section intends to explore theconceptual link between the knowledge base offirms and the capacity of a cluster to link up withextra-cluster knowledge. In particular, How do firmknowledge bases influence the extra-cluster acquisitionof knowledge and the processes of technologicalgatekeeping?
Given the heterogeneity of firm knowledgebases, it is possible to argue that some firms wouldbe less distant than others from the technologicalfrontier. Consistent with Cohen and Levinthal(1990), who argue that the capacity of a firm toconnect with external sources of knowledge dependson the firm’s prior related knowledge, those firmswith less cognitive distance from the technologicalfrontier would be more likely to absorb extra-clusterknowledge and creatively exploit it than firms whoseknowledge base is weak. The absorption of extra-cluster knowledge by firms is an important learningphase for the cluster. For this reason, it is desirablethat the absorbed extra-cluster knowledge isdiffused at intra-cluster level and manages also toreach those firms that have no or very limitedexternal linkages. Hence, as said, the interfacebetween the external linkages and the intra-clusterknowledge system is considered here a criticaldimension in the cluster knowledge absorptionprocess. Technological gatekeepers, defined in the foregoing section, play the dual role ofacquiring new knowledge from extra-clustersources, and of transferring knowledge to intra-cluster firms.
In the previous section my proposition was thatfirms with stronger knowledge bases should be morelikely to transfer knowledge to other cluster firms,behaving as ‘sources’ of knowledge. In this section, Ihave discussed the fact that firms with strongerknowledge bases should also be more likely to inter-connect with extra-cluster sources of knowledge.Hence, the conceptual consequence of these twoabove mentioned propositions is that technologicalgatekeepers will be among the firms with strongerknowledge bases in the cluster. Recent empiricalstudies have supported this view (e.g. Giuliani,2005; Giuliani and Bell, 2005) but further researchis still needed in this direction.3
Cluster absorptive capacity: towards aconceptual framework
The concept of cluster absorptive capacity
As said, the absorptive capacity of a cluster dependson the capacity of firms to establish intra- and extra-cluster knowledge linkages. In the second sectionabove I have discussed how the heterogeneousdistribution of firms’ knowledge bases is related toboth the intra- and the extra-cluster knowledgesystems. I propose here a conceptual framework of‘cluster absorptive capacity’, which includes firm-level knowledge bases, intra- and extra-clusterknowledge systems.
The concept of absorptive capacity did originallyrefer to the intra-firm ability to ‘recognize the valueof new, external information, assimilate it, and applyit to commercial ends’ (Cohen and Levinthal, 1990:128). In this present work I adapt this concept toclusters of firms. Hence I define cluster absorptivecapacity as the capacity of a cluster to absorb, diffuseand creatively exploit extra-cluster knowledge. Byanalogy with Cohen and Levinthal’s (1990) originalconcept, cluster absorptive capacity depends on theknowledge bases of its member firms: ‘thedevelopment of an organization’s absorptive capacitywill build on prior investment in the development ofits individuals’ absorptive capacities’ (Cohen andLevinthal, 1990: 131). However, it is not understoodas the mere sum of firm-level capabilities: ‘a firm’sabsorptive capacity is not however, simply the sumof the absorptive capacities of its employees’(Cohen and Levinthal, 1990: 131). In fact, in orderto understand the level of cluster absorptive capacityit is necessary to look at both intra-cluster knowledgelinkages as well as those established by cluster firmswith external sources of knowledge: ‘to understandthe sources of a firm’s absorptive capacity, we focuson the structure of communication between theexternal environment and the organization, as wellas among the subunits of the organization, and alsoon the character and distribution of expertise withinthe organization’ (Cohen and Levinthal, 1990: 132).As in Cohen and Levinthal (1990) the cluster absorbsexternal knowledge through ‘receptor’ firmscharacterized by extraordinarily strong knowledgebases – i.e. the technological gatekeepers. Consistentwith the Cohen and Levinthal (1990) model,moreover, the weaker the knowledge base of firms
the lower the probability of absorbing extra-clusterknowledge and therefore the more limited theincidence of technological gatekeepers in the cluster.
Cluster absorptive capacity is, however, not just amatter of absorption and diffusion by cluster firms.In fact, the absorption of knowledge from extra-cluster sources is only one face (Cohen andLevinthal, 1989) of the learning and innovativepotential of clusters. Indeed, it is the accumulationand generation of knowledge at the firm-level thatconditions the capacity of firms to exploit creativelythe externally absorbed knowledge, in a path-dependent fashion. Hence, the cluster absorptivecapacity influences and is itself influenced by theeffort undertaken by firms to accumulate newknowledge: ‘to develop an effective absorptivecapacity, whether it be for general knowledge orproblem solving or learning skills, it is insufficientmerely to expose an individual briefly to the relevantprior knowledge. Intensity effort is critical’ (Cohenand Levinthal, 1990: 131).
On the basis of this, I propose here a taxonomyof levels of cluster absorptive capacity, consideringthat it might evolve over time (or not), contextuallywith firm heterogeneous patterns of knowledgeaccumulation (or lack thereof). The taxonomy(Table 2) ranges from basic to advanced clusterabsorptive capacity. I assume here that there are twoextreme cases, a non-desirable one, when clusterabsorptive capacity is basic, and a desirable onewhen cluster absorptive capacity is advanced.
Cluster absorptive capacity is in a basic statewhen firms have very weak knowledge bases, theintra-cluster knowledge system is weaklyinterconnected and the degree of external opennessis very limited. It is advanced when cluster firmsboth absorb knowledge from extra-cluster sourcesand contribute to the creation of knowledge byinvesting in in-house R&D. Clusters with anadvanced knowledge system are characterized by anintra-cluster innovative environment, where firmsestablish dense knowledge linkages. Between basicand advanced levels, I identify an intermediate levelof cluster absorptive capacity which includes firmswith highly heterogeneous knowledge bases.Following from this, the intra-cluster knowledgesystem will only partially be disconnected, while afew firms will behave as technological gatekeepers,thus connecting the intra- and the extra-clusterknowledge systems.
The reason why one should care about thedeterminants of improved cluster absorptivecapacity is that they may influence the dynamicgrowth of the cluster. Even though such arelationship is a very complex one to test (Morosini,2004), various studies support the view that one ormore of the mentioned dimensions of the absorptivecapacity of a cluster are related also to its growthtrajectory (e.g. Giuliani, 2003; see also Mytelka andFarinelli, 2003). This paper elaborates on this,providing a systematic literature review of empiricalcase-studies of clusters around the world. For thispurpose, a selection of existing empirical studies hasbeen organized on the basis of the following twodimensions: (1) the levels of cluster absorptivecapacity, defined as basic, intermediate andadvanced; and (2) their dynamism. The dynamismof the clusters refers to their capacity to grow overtime. Hence, a static cluster is one where no relevant
growth is observed over time, and a dynamic clusteris one where a rapid and sustained growth has beenachieved in the recent past. Finally, a leading clusteris one with a consolidated growth trajectory. Asummary of case-studies is presented in Table 3.
From the qualitative accounts of these studies, itis possible to observe a relationship between the twodimensions. Static clusters tend to be characterizedby basic cluster absorptive capacity. In spite of thefact that the literature is never explicit on thecharacteristics of the cluster knowledge systems, in afew cases it was possible to observe that clusters thathave not grown over time are also characterized byfirms with weak knowledge bases and by limitedintra- and extra-cluster knowledge linkages. This isthe case in the Punjab tubewell industry describedby Aftab and Rahim (1989: 503), where informalfirms ‘lack of ability to absorb and attract newresources . . . sets the limit to improvement intechnology, managerial practice and expansion’.Among the deficiencies which limit the informal
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GIULIANI: CLUSTER ABSORPTIVE CAPACITY 281
Table 2 Cluster absorptive capacity: a taxonomy
Cluster Firms’ Intra-cluster Extra-clusterabsorptive capacity knowledge bases knowledge system knowledge system
Basic Firms have very weak knowledge The cluster is The cluster has no linksbases, with low-skilled human characterized by very with extra-cluster sourcesresources and very limited limited and weak of knowledge and none ofin-house knowledge generation knowledge linkages the firms play roles of(e.g. just passive learning by between firms technological gatekeepersdoing)Firms’ knowledge bases are farfrom the technological frontier
Intermediate Firms’ knowledge base is mixed, The cluster has a more The cluster has some some firms have skilled connected intra-cluster interconnection with employees and perform knowledge system but extra-cluster sources ofsome form of in-house knowledge still parts of the firms are knowledge. A few firms generation. Most innovative cognitively isolated perform the role ofeffort is directed to the adaptation technological gatekeepersof knowledge rather than to its creation
Advanced Firms have very strong The cluster has a dense The cluster is well knowledge bases and operate at intra-cluster knowledge connected with extra-the technological frontier. Human system cluster sources ofresources are highly skilled and knowledge and many firms firms perform highly innovative play the role ofR&D technological gatekeepers
sector to upgrade and grow, Aftab and Rahimmentioned the lack of linkages with the localinstitutions and the absence of any productive andtechnological linkages between modern andinformal sector firms. A similar account is that ofthe garment cluster in Nairobi (Kenya), describedby McCormick (1997; 1999) as an ‘embryonicindustrial district’ or as a ‘ghetto where marginalbusinesses congregate’ (McCormick, 1997: 109).Also in this case, firms have weak knowledge bases:‘The mini-manufacturers use their networks well,but most are poorly educated, African women whosenetworks have limited power to uplift a business’(McCormick, 1997: 125). Besides, they are weaklyconnected at both intra- and extra-cluster level:
. . . like many clusters in developing countries, thegarment markets exhibit weak internal and externallinkages. The inter-firm specialisation and division oflabour, which are supposed to be key to collectiveefficiency, are almost totally lacking. Those inter-firmlinkages that exist are often informal and at low level . . . (McCormick, 1997: 125)
In spite of the fact that she never referred toknowledge linkages explicitly, it is reasonable toassociate the lack of productive linkages also with aweak knowledge system.
Dynamic clusters tend to be characterized bymore intermediate cluster absorptive capacity. Thismeans that clusters that have grown dynamicallyover the recent past are likely to be associated with
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282 EUROPEAN URBAN AND REGIONAL STUDIES 12(3)
Table 3 Cluster absorptive capacity and dynamism: empirical evidence
Cluster Cluster dynamismabsorptivecapacity Static cluster Dynamic cluster Leading cluster
Basic Clothing, Gamarra, PeruTubewell, Punjab, PakistanPalm sugar, roof tiles, Central Java, IndonesiaVehicle repair, Kumasi, Ghana Garment, Nairobi, KenyaFootwear, Agra, India
Intermediate Knitwear, Luhdiana, IndiaKnitwear, Tiruppur, IndiaFootwear, Sinos Valley, BrazilFootwear, Guadalajara, MexicoTile, Santa Catarina, BrazilSurgical instruments, Sialkot, PakistanChair manufacturing, Manzano, ItalyHigh-tech, Zhongguancun, China
Advanced Surgical instruments, Tüttlingen,GermanySki-boots, Montebelluna, ItalyWatch-making, Jura Arc,SwitzerlandHigh-tech (microprocessor),Silicon Valley, USMachine tool industry, Baden-Württemberg, Germany
Sources: Author’s own elaboration based on Aftab and Rahim (1989), Cooke and Morgan (1994), Saxenian (1994), Maillat etal. (1995), Meyer-Stamer (1998), Knorringa (1999), Nadvi (1999), Rabellotti (1999), Schmitz (1999), Tewari (1999),Visser(1999), Weijland (1999), Belussi and Gottardi (2000), Halder and Nadvi (2002), Zhou and Xin (2003).
firms that have upgraded their knowledge base withrespect to the technological frontier but do notactively contribute to its enhancement. Moreover,dynamic clusters are likely to show a more openknowledge system, capable of absorbing extra-cluster knowledge, and with the presence of intra-cluster knowledge linkages. However, it is possiblethat not all firms which form part of a dynamicallygrowing cluster homogeneously increase theirknowledge base and form intra- and extra-clusterknowledge linkages. In this respect, intermediatecluster absorptive capacity differs from theadvanced one. Advanced cluster absorptive capacity,associated with leading clusters, may represent astate in which firms operate and contribute to theevolution of the technological frontier.
An interesting comparison between a dynamicand a leading cluster is provided by Halder andNadvi (2002), who compare the surgical instrumentcluster in Sialkot (Pakistan) with the Tüttlingen onein Germany. As the authors described, Sialkot seemsto be a relatively successful cluster with high exportrates into the international market (Nadvi, 1999),whereas Tüttlingen is typically a leading cluster inthe surgical instrument industry, with a significantlyhigher output value, if compared to Sialkot.
In fact, these two clusters target different marketsegments. Sialkot operates in the production ofmature surgical instruments, whose technologyrelies on surgical mechanics. Moreover, firms inSialkot do not perform innovative activities and tendto rely on extra-cluster knowledge transfer, mainlyderiving from firms in Tüttlingen. As Halder andNadvi remarked:
Such knowledge flows essentially enhance what Bell andAlbu (1999) term knowledge-using capabilities.Acquiring know-how on adopting quality assuranceproducers in production processes, and incorporatingnew technologies in manufacture, has helped localproducers in Sialkot to produce mature instrumentsmore efficiently and meet quality assurance demands. Ithas not, however, led to the development of knowledge-changing abilities within the cluster. (Halder and Nadvi,2002: 24)
Tüttlingen, on the contrary, operates on thetechnological frontier, producing new products suchas minimal invasive instruments, endoscopes andsurgical implants. These types of products need
complementary capabilities in addition to themetalworking ones, requiring firms in Tüttlingen tocarry out intense R&D activity and also to connectwith extra-cluster sources of knowledge, as noted byHalder and Nadvi (2002: 31):
Access to such knowledge requires ties to technical skillsthat lie outside the Tüttlingen cluster. ThusTüttlingen’s endoscope producers have either acquiredor entered into collaborative joint venture agreementswith specialised firms in the field of optical lenses fromoutside the cluster.
This example makes it clear that the Sialkotcluster, characterized by firms whose knowledgebases are comparatively weaker and far from thetechnological frontier compared to those operatingin Tüttlingen, is capable of generating a differentinnovative environment, typically a knowledge-using rather than knowledge-producing one. Hencethis story provides an interesting insight into thedifferences between two different levels of clusterabsorptive capacities – which can be consideredintermediate in the case of Sialkot and advanced inthe case of Tüttlingen – and their long-termperformances.
Among the advanced cases, one can also takeSilicon Valley. As was well described by Saxenian(1994), Silicon Valley represents a good example of acluster where firms, operating on the technologicalfrontier, have strong knowledge bases; the localknowledge system is dense and firms tend to have ahigh degree of external openness. In this respect,since Silicon Valley is a place where knowledge isprimarily created, extra-cluster knowledge linkagesare more likely to flow outward than inward:‘expanding in distant locations, Silicon Valley firmssimultaneously enhanced the capabilities of theseindependent, but linked, regional economies’(Saxenian, 1994: 159). Silicon Valley is therefore agood example of a high-performing cluster with anadvanced cluster absorptive capacity.
These examples are mostly descriptive andlargely anecdotal. However, they represent astimulus to understand what determines variabilityin the key characteristics of the cluster absorptivecapacity and to explore whether and how firmknowledge bases influence the characteristics ofboth intra- and extra-cluster knowledge systems.
This paper has illustrated the existence of aconceptual link between the firm’s knowledge bases,the cluster absorptive capacity and its potential togrow dynamically. The cluster absorptive capacityframework has been developed to explain thedifferentials in clusters’ growth around the world.In particular, this paper attributes substantialexplanatory power to firm-level knowledge bases askey elements of the capacity of clusters to grow. Infact, clusters characterized by firms with strongknowledge bases are more likely to show denseintra-cluster knowledge systems and to be highlyconnected with extra-cluster knowledge. In suchcases the incidence of technological gatekeepers maybe high. Opposite to this, clusters characterized byfirms with weak knowledge bases will be characterizedmainly by a highly disconnected intra-clusterknowledge system and with poor connection to theexternal world; technological gatekeepers are unlikelyto be found in this kind of cluster. Studies carriedout in line with the conceptualization developedhere have recently provided an empirical validationof this framework (see e.g. Giuliani, 2003). However,further empirical research is needed in thisdirection. This is particularly so for the policyimplications which this type of framework mayhave. In fact, if the capacity of a cluster to growdynamically depends ultimately on firm-levelspecificities, cluster policies should be orientedtowards strengthening firm knowledge bases, ratherthan towards the cluster as a collective entity.
Acknowledgements
The author would like to express her gratitude toMartin Bell for very helpful discussions during herstudy period at SPRU, University of Sussex.Thanks go also to Mark Lorenzen, who has givenvery interesting insights for this paper. GabrielYoguel and Peter Maskell and the participants atthe DRUID 2002 Summer Conference inCopenhagen, who have provided comments on aprevious version of this paper, are alsoacknowledged. Usual disclaimers apply. This paperhas benefited from EU financial support (MarieCurie Fellowship).
Notes
1 The notion of tacit knowledge is normally attributed tothe seminal contribution of M. Polany (1967: 3), whonoted that people ‘know more than they can tell’. Hespecifically referred to the fact that not all knowledge canbe fully codified and therefore transmitted throughdistance. Instead, part of the knowledge is embodied bypeople, acquired through progressive experience andaccumulated over time (e.g. learning by doing).
2 ‘But an industry which does not use massive material, andneeds skill that cannot be quickly acquired, remains as ofyore loth to quit a good market for its labour. Sheffieldand Solingen have acquired industrial “atmospheres” oftheir own; which yield gratis to the manufacturers ofcutlery great advantages, that are not easily to be hadelsewhere: and the atmosphere cannot be moved’(Marshall, 1919: 284).
3 Interestingly, Giuliani and Bell (2005) have found thatsome cluster firms, which have strong knowledge basessimilar to those of technological gatekeepers, behave verydifferently, establishing barely any intra-clusterknowledge linkages with the other firms but beingstrongly connected externally – a role which the authorsrefer to as ‘external stars’. Hence, external stars, whichare best positioned of all the cluster firms to make positivecontributions to the cluster knowledge system, rarely doso. It is still a matter of empirical investigation whatdrives firms to behave as external stars, and what inhibitstechnological gatekeeping behaviours.
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Correspondence to:
Elisa Giuliani, SPRU, The Freeman Centre,University of Sussex, Falmer, Brighton, East Sussex BN1 9QE, UK.[email: [email protected]]
