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Cluster theory evidence: What remains of the concept – some reflections

Cluster theory evidence: What remains of the concept – some reflections. Session IX – Innovation Systems Research Network Seventh Annual Meeting Toronto, May 5-6, 2005 Bjørn Asheim Lund University and University of Oslo. What’s good with Porter. Path dependency – historical trajectories

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Cluster theory evidence: What remains of the concept – some reflections

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  1. Cluster theory evidence: What remains of the concept – some reflections Session IX – Innovation Systems Research Network Seventh Annual Meeting Toronto, May 5-6, 2005 Bjørn Asheim Lund University and University of Oslo

  2. What’s good with Porter • Path dependency – historical trajectories • Continuous innovation • Competitive advantage based on unique resources, which need not be R&D-based • Focus less on R&D and more on interactive learning • From a firm to a cluster (context) perspective

  3. From ID to cluster in a globalising economy • These characteristics of IDs are disappearing, making IDs turn into regional clusters: • Embeddedness (fusion of economy and community) - FDIs • Value/commodity chain local - outsourcing • Dominance of SMEs – TNCs and regional group formations

  4. Problems with Porter’s cluster • Conceptual problems – DEFINITIONS • Original definition (1990): Economical/functional – national level (industrial/sectoral clusters within nations) • Later definition (1998): Geographical/territorial – regional level (regional clusters)

  5. Problems with Porter’s cluster • Scaling problems – location of factors of the DIAMOND: • From large nations (USA) to small nations (Norway) • From nations to regions • From large regions (in federal states) to small regions (in the Nordic countries)

  6. Problems with Porter’s cluster • Theoretical problems – the inclusiveness of the CLUSTER concept • Porter makes no distinction between cluster and RIS, as clusters contains R&D institutes and universities • Important to make this distinction – cluster is not identical with RIS (a RIS must support more than one local cluster)

  7. Knowledge bases, clusters and RIS: • The relevance of different types of RIS must be placed in a context of the knowledge base of various industries • Innovation processes of firms are strongly shaped by their specific knowledge base • Distinguish between different types of knowledge base: • a) analytical (science based) • b) synthetic (engineering based) • c) symbolic (creativity based)

  8. Cluster-RIS distinction (not identical – RIS supports more than one cluster): • The existence of ’pure’ regional clusters where relations to RIS are established at a later stage of a cluster’s life cycle in order to support localised learning and innovation in the cluster (auxiliary), and • The existence of relations between clusters and RIS from the emergence of the cluster as a necessary input in the development of the cluster (integrated)

  9. Cluster – RIS: • The traditional constellation of industrial clusters surrounded by innovation supporting organisations in a RIS is normally found in contexts of industries with a synthetic knowledge base • The existence of a RIS as a necessary part of the development of an emerging cluster will normally be the case of industries based on an analytical knowledge base

  10. Thus, different historical and emerging technological trajectories co-exist: • In traditional cluster-RIS relations, based on industries with a synthetic knowledge base, the logic behind building RIS is to support and strengthen localised learning of existing industrial specialisations in a cluster, i.e. to promote historical technological trajectories based on ’sticky’ knowledge in the region • In contexts of relations between clusters and RIS as a necessary condition for the emergence and growth of the clusters it is a question of promoting new and emerging economic activity based on industries with an analytical knowlegde base, requiring close and systemic industry-university cooperation and interaction in e.g. science parks, located in proximity of knowledge creating organisations (e.g. (technical) universities)

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