<Articles>Cluster Evolution and Proximity Dynamics, From a Learning and Network Perspective (Special Issue : Networks of Learning)

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  • <論説>産業集積の進化と近接性のダイナミクス : 知識学習とネットワークの視点から (特集 : 学びのネットワーク)
  • 産業集積の進化と近接性のダイナミクス : 知識学習とネットワークの視点から
  • サンギョウ シュウセキ ノ シンカ ト キンセツセイ ノ ダイナミクス : チシキ ガクシュウ ト ネットワーク ノ シテン カラ
  • Cluster Evolution and Proximity Dynamics, From a Learning and Network Perspective

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Abstract

This paper aims to explore the dynamic understanding of learning and networks in industrial clusters. In recent years, learning and networks have been key concepts in economic geography. Local agglomerations of industry have been thought to facilitate regional development through learning among geographically proximate actors. However, this localization of economies in clusters is not necessarily permanent. We need dynamic perspectives on learning and networks in clusters. Additionally, learning does not necessarily require geographical proximity, and geographical proximity cannot be a sufficient condition for learning. For a better understanding of learning in clusters, the concept of proximity needs to be reconsidered, and proximity dynamics should be explored. In the first half of this paper, the author considers recent arguments focused on cluster evolution. Specialized clusters often gradually mature and decline over time. As a cluster matures over time, the specialization of the cluster increases and the heterogeneity of accessible knowledge in the cluster decreases. This can lead the cluster to negative lock-in situations, which make it vulnerable to rapid changes in external economic environments and lead it to decline. The decline of clusters is likely to be caused by factors that were advantages in the past. In the cluster lifecycle approach, clusters are seen as following a kind of life cycle with stages from emergence to growth, maturity, and decline. However, this is not a predetermined process. It is true that some clusters gradually decline because of negative lock-in processes. But other clusters escape negative lock-in situations and create new growth paths. We have to discuss how clusters gain the adaptability to avoid negative lock-ins and create new paths. Several possibilities enable clusters to create new paths. One of the possibilities is the existence of redundancies and heterogeneities of accessible knowledge and capacities in clusters. Diversity in clusters promotes innovative activities by the recombination of existing varieties. Especially, related variety is likely to enable actors more mutual learning than unrelated variety. Another possibility lies in diversifying from existing industries or technologies in a cluster into related industries or technologies. This can increase the diversity of the cluster, and this diversity can, in turn, enable the cluster to diversify further. We have to understand various types of cluster evolution, and not be limited to the life-cycle model.

Journal

  • 史林

    史林 101 (1), 261-292, 2018-01-31

    THE SHIGAKU KENKYUKAI (The Society of Historical Research), Kyoto University

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