The typology of technology clusters and its evolution — Evidence from the hi-tech industries
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Article history:Received 22 November 2009Received in revised form 29 November 2010Accepted 16 January 2011Available online 21 February 2011
Keywords:Technology clustersAgglomerationPatent analysis
Technological Forecasting & Social Change 78 (2011) 945952
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Technological Forecasting & Social ChangeClusters are geographic concentrations of interconnected companies, specialized suppliers, service providers, rms in relatedindustries, and associated institutions (for example, universities, standard agencies, and trade associations) in particular elds thatcompete but also cooperate . We noted that, from literature research, there are many different ways of dening a cluster. In theearliest study in this eld, conducted by Alfred Marshall , the notion of industrial districtswas developed and it was referringto agglomerations of rms operating in one industry sector in a well-dened and relatively small geographic area. Building onMarshall's notion, other regional innovative models, such innovative milieu and technology districts have been offered asframeworks for discussing the pattern of agglomerations. What distinguishes clusters from these other models has not been
clearly dened . Indeed, these concepts used almost interchangeably despite havingstudies attempting to distinguish the industwere not consistently acknowledged by othebecause of the inuence of Porter's researc
Corresponding author. Tel.: +1 201 216 5018.E-mail addresses: firstname.lastname@example.org (J. He), hfallah@
0040-1625/$ see front matter 2011 Elsevier Inc.doi:10.1016/j.techfore.2011.01.005Clustering is one of the key drivers for regional economic growth. Development of clusters is adynamic process shaped by a variety of internal and external factors such as availability ofskilled labor, presence of functioning networks and partnerships, technological changes, andmarket competition, etc. As a result, the patterns of cluster growthmay differ from one another.Although each cluster is unique in some way, previous research has attempted to identify fewsimplified models of evolution of clusters. In this study, we briefly reviewed the literature on avariety of models of clusters. Based on these models, we investigated 15 hi-performingmetropolitan-based clusters in the United States, covering communications equipmentmanufacturing, information technology, and biopharmaceutical industries, in order to findout the similarities and differences between real-world clusters. Specifically, by examining thecomposition of these high-tech clusters, we attempted to find out the following: 1) What arethe typologies of these technology clusters? 2) Whether different industries tend to supportdifferent cluster typologies? and 3) How do clusters change their typologies over time? Ouranalysis results suggest that the real-world clusters rarely feature any single type of typology; amixed type of typology is much more prevalent in reality.We also found that different industries tend to support different types of cluster typologies. Inother words, an individual cluster's typology is to some extent shaped by the industry group itbelongs to. In addition, we note that, as a cluster goes through different stages of its lifecycle, itstypology may change significantly.
2011 Elsevier Inc. All rights reserved.Regional planningThe typology of technology clusters and its evolution Evidence from thehi-tech industries
Jiang He, M. Hosein FallahHowe School of Technology Management, Stevens Institute of Technology, Hoboken, NJ 07030, USA
a r t i c l e i n f o a b s t r a c tclusters, industrial districts, innovative milieu, and technology districts are oftenorigins in different conceptual contexts . Although we've recognized that there arerial clusters from technology clusters , the differences identied by certain authorsr studies/researchers. In this study, we follow Porter's  denition of clusters, mainlyh in this eld.
stevens.edu (M.H. Fallah).
All rights reserved.
946 J. He, M.H. Fallah / Technological Forecasting & Social Change 78 (2011) 945952Despite the potential confusion caused by the inconsistence in terminology, the contribution of clustering to regional economicdevelopment has been well accepted and documented [6,7]. Companies cluster together to benet from the availability andquality of local labor pools, well-developed intermediate input suppliers, and better information ows which can facilitate thegeneration of new ideas. Among those factors, knowledge spillovers between economic actors are believed to be a particularimportant one promoting the development of modern clusters .
Because of the signicant benets brought by clustering, both academic researchers and regional planners seek to understandthe factors shaping the evolution of clusters. In many of the real-world cases, clusters have emerged unplanned and then gonethrough a dynamic lifecycle process, which typically consists of four stages: embryonic, established, mature and declining. It isnoteworthy that, when a cluster goes through its own evolution path, the forces that foster the subsequent growth of the clusterare not necessarily those that provide the cluster its initial advantage . From a perspective of regional planning, it seems to berather challenging, if not impossible, to develop a cluster from scratch by planning. But policy interventions, when appropriate, canfacilitate the development of an existing cluster . In order to come up with appropriate cluster policy interventions,understanding the driving forces of individual clusters would be fundamental, as clusters are all different from one another.
Despite the differences between clusters, there are some important features shared by certain groups of real-world clusters.Previous research has attempted to model the evolution of real-world clusters with those important cluster characteristics. In thefollowing, we will review the literature on cluster characteristics and a variety of cluster typologies. Following the literaturereview, we will examine 15 U.S.-based hi-tech geographical clusters, covering information technology, communicationsequipment manufacturing, and biopharmaceutical sectors. By examining the typologies of the select high-performing clusters, weaim to answer the following 3 questions: 1) What are the typologies of these industrial clusters? 2) Whether different industriestend to support different cluster typologies? and 3) How do clusters change their typologies over time? The reminder of this paperis organized as follows. Section 2 reviews the literature on industrial clusters and cluster characteristics. Section 3 describes ourmethodology for analyzing the select clusters. In Section 4, we present and interpret the data results. In Section 5, we concludewith a discussion about the implications of this study for cluster researchers and practitioners.
2. Cluster characteristics and the variety of cluster typologies
The studies of clusters date back to Marshall , who developed the agglomeration concept, based upon the cost-saving scaleeffects brought by industrial localization. According to Marshall, companies in a particular industry tend to cluster together fortaking advantage of external economies brought by co-locating, including reduced transaction costs, increased specialization, andefcient information ows. Although Marshall's original model successfully explained some earliest industrial clusters,observations from recent clusters suggest that the phenomenon of clustering appears to be much more complex than theoriginal Marshallian agglomeration model has suggested. For example, case studies on many recent clusters suggested that socialinteractions as well as knowledge ows between cluster members play an important role in the development of many modernclusters [810,13]. Social relations, however, were not considered an important element in Marshall's original cluster model.
FollowingMarshall'swork, Becattini , Brusco , and Sforzi  identied a newer type of cluster by examining the economicsuccess of several Italian cities and regions. ThemodiedMarshallianmodel not only acknowledgesMarshall's original agglomerationconcept, it also recognizes the roles of social relations as well as inter-rm cooperation/competition within industrial clusters. WhileMarshall stressed the direct benets of co-locating (e.g., input cost reduction, availability of labor, etc.); the modied Marshallianmodel (sometimes referred to as Italian version of Marshallian model in the literature) emphasized the role of social interactionsbetween co-located rms/individuals when explaining the success of clusters. The modied Marshallian model is important as itprovides a good foundation to explain many successful modern clusters including Silicon Valley and Orange County [9,10].
Even with its Italian version, the Marshallian formulation wasn't able to explain the ourishing of some clusters and demise ofothers. In order to address the increasing complexity and variety of real-world clusters, Markusen , through inductiveinquiries, broadened the picture by introducing additional models of clusters. Besides the Marshallian formulation, threeadditional models of clusters were proposed by Markusen hub-and-spoke, satellite platform, and state-centered.
Fig. 1 illustrates the typology for each of the cluster models. The links depicted in this chart reect regular inter-rm businessactivities such as suppliercustomer transactions. In the categorisation shown in Fig. 1, Markusen  stressed the distinct roles playedby different groups of cluster members, as well as the way different players interact with each other. We noted there are othercategorisations proposed by different researchers; based on our understanding, other models are not as comprehensive as theMarkusen's model when it comes to capturing the structures of real-world clusters. For example, Lorenzoni  proposed acategorisationbasedonasymmetries among the clustermembers and introduced the concept of leaderrms and surroundingrms.
According to Markusen , a hub-and-spoke cluster, is structured around one or few dominant rms supporting the regionalcluster. In this type of cluster, suppliers and other entities spread around and are connected to the hubs like wheel spokes. In a simpleversion as depicted in Fig. 1, a giant hub rm trades with local small rms as well as external suppliers and sells mainly to externalcustomers, which could be large or small ones. In reality, single-hub-based clusters include the Seattle-based aircraft manufacturingcluster supported by Boeing, and the Toyota City-based carmanufacturing cluster supported by Toyota. Amulti-hub-based cluster canbe found in Detroit, where the Big Three auto manufacturers dominate the regional economy and are surrounded by a number ofsmall and large suppliers. In such a cluster, substantial trades take place between hub rms and local smaller suppliers. However,cooperative activities among competitors within this type of cluster are remarkably lacking. Cooperation may occur between hub-rms and surrounding suppliers, but the terms of cooperation are often set by the hub-rms. Due to its very nature, the dynamics of ahub-and-spoke cluster are largely determined by the hub rm(s), either via its success or failure.
within the regional cluster, as the local branches maintain regular business relations mainly with their remote headquarters orpartners. The future growth for such a cluster is largely determined by the region's ability of keeping its competitive advantage. Forhigh-end satellite platforms, themost challenging issue is tomaintain and develop the pool of skilled professionals; while low-endplatforms are more vulnerable to increases in the cost of running business. Given its nature, a satellite platform is less sustainableover time if the cluster fails to develop a more diversied local economy.
The last type of cluster typology proposed by Marsuken  features a state-centered structure. The local business structure inthis case is dominated by the presence of one or few large public or non-prot entities, such as universities, public researchinstitutions, or military bases. The key public entities are typically surrounded by smaller rms/organizations, thus forming a hub-based structure similar to the previously described hub-and-spoke typology. In fact, many fast-growing cities in the U.S. andoverseas, to a large extent, owe their successes to the presence of government-supported entities. For example, cities such asMadison, Wisconsin and Ann Arbor, Michigan benet greatly from the presence of the state universities in region. Over the longrun, the growth outlook for such a cluster is very much dependent on two factors: 1) the size and power of the dominating publicentities; and 2) the region's capability of supporting start-ups and entrepreneurship. Actually, the second would be an importanfactor facilitating the region to transform itself into a more dynamic and diversied regional economy.
It is noteworthy that the categorization proposed by Markusen  provides only a much simplied framework to examinecluster structures. It helps cluster researchers/practitioners understand how a cluster comes about and what it thrives on, but imay not always reect the realities of cluster development. As acknowledged by Markusen , real-world clusters often featurecharacteristics of a mix of multiple typologies. In addition, clusters evolve over time; therefore one may change its typology fromone to another as it moves through different stages of its lifecycle.
In the following sections, we examine 15 real-world strong clusters to nd out how well the above-mentioned cluster modelsworks in reality and discuss the implications of cluster typologies for regional planning.
Based on the objective of this study and the research questions proposed in Section 1, the rst step of our investigation is toidentify clusters.
Using the data from Cluster Mapping Project, which is led by Professor Michael Porter at Harvard University, we selected 15strong U.S. metropolitan city-based clusters covering information technology, communications equipment manufacturing, and
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tAnother type of cluster typology proposed by Marsuken is the satellite platform. This type of cluster typically consists of acongregation of branch facilities of externally based multi-plant rms. The Research Triangle Park located in North Carolinarepresents a high-end example of satellite platform cluster, where a number of high-tech multinational rms have establishedtheir R&D centers there. In such a cluster, the remotely located parent companies make important decisions for the local branchesthus inuence the regional cluster. A key characteristic for this type of cluster is the absence of any sort of connections or network...