Deconstructing clusters: chaotic concept or policy panacea?

Martin, Ron; Sunley, Peter

doi:10.1093/jeg/3.1.5

“…Although it is not possible to answer this question conclusively, due to the many definitions and forms of clusters (see MARTIN and SUNLEY, 2003), an approximation of the basic structure of the concept is necessary. There are two reasons for this.…”

Section: Elements Of Clustersmentioning

confidence: 99%

“…POUDER and ST. JOHN (1996) argue that the movement through the life cycle is shaped by a biased cognitive focus of the cluster's companies towards each other. However, their cluster life cycle ultimately results in a negative lock-in and decline (MARTIN and SUNLEY, 2003), thereby neglecting the possibility of entering new growth phases.…”

Section: Introductionmentioning

confidence: 99%

Cluster Life Cycles - Dimensions and Rationales of Cluster Development

Menzel

¹

,

Fornahl

²

,

Planck³

2007

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractWe present a model that explains how a cluster moves through a life cycle and why this movement differs from the industry life cycle. The model is based on three key processes: the changing heterogeneity in the cluster describes the movement of the cluster through the life cycle; the geographical absorptive capacity enables clustered companies to take advantage of a larger diversity of knowledge and the stronger convergence of clustered companies compared to non-clustered companies results in a reduction of heterogeneity. We apply these processes to four stages of the cluster life cycle: emergence, growth, sustainment and decline.JEL-classification: R11, O30, L25

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“…Policies aiming at the promotion of clusters are frequently conducted but only seldom evaluated (Martin & Sunley 2003, Brenner & Schlump 2011. The aim of this study was to add to our understanding of the effects and mechanisms of cluster policies by analyzing the impact of the German "Leading-Edge Cluster Competition" on the underlying network structure.…”

Section: Discussionmentioning

confidence: 99%

Policy Induced Innovation Networks: The Case of the German “Leading-Edge Cluster Competition”

Cantner

¹

,

Graf

²

,

Hinzmann

³

2013

The Geography of Networks and R&D Collaborations

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe last decades saw a pronounced shift in innovation policy in Germany and many other countries towards increased funding of cooperative R&D. Over the last years, competitions between regional initiatives pushed this trend even further by adding a regional perspective, by increasing the scope of funding, and by fostering interaction between a large number of actors. In 2008 the German ministry for education and research (BMBF) started the "Leading-Edge Cluster competition" ("Spitzencluster-Wettbewerb") in which 15 clusters were selected in three waves (2008, 2010, 2012) and are funded for a five-year period with up to 40 million Euro each. Our paper presents selected results regarding the influence of government funding on cooperation networks within five of the clusters that were successful in the first wave of the "SpitzenclusterWettbewerb". More specifically, we analyse the extent of policy influence on the network of most important cooperation partners, its geographic reach, and the changes of network structure in general. Our empirical analysis is based on original data that was collected in 2011 with cluster actors (firms and public research) who received government funding. Our results indicate that the program was quite effective in initiating new cooperations between cluster actors and in intensifying existing linkages. The vast majority of the linkages which are influenced by the cluster competition are between actors located in the cluster region. With respect to the influence of the cluster competition on network structure, we find an increase in network centralization. Small and medium sized enterprises used the chance to connect with the local 'stars', but not as much among each other.

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“…En este trabajo, se ha considerado que un área funcional tiende a especializarse cuando el valor de su QL en algún tipo de KIS supera el 1,1; en cambio, otros estudios han aplicado un umbral de 1,25 (Miller et al, 2001) o aún más elevados de 3 (Isaksen, 1996). Los criterios para establecer este límite dependen de la variabilidad y de los valores máximos del coeficiente, pero no se aplica un único umbral (Martin y Sunley, 2003;O'Donoghe y Gleave, 2004).…”

Section: Gruposunclassified