The effects of network characteristics on performance of innovation clusters

  • Authors:
  • Jinho Choi;Ahn Sang -Hyun;Min-Seok Cha

  • Affiliations:
  • School of Business, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul 143-747, South Korea;Department of Physics, College of Natural Science, KAIST, 335 Gwahakro, Yuseong-gu, Daejeon 305-701, South Korea;Department of Business Administration, College of Economics and Business, Changwon National University, Sarim-dong 9, Uichang-gu, Changwon City, Gyeongsangnam-do, South Korea

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2013

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Abstract

Industry clusters provide not only economic benefits but also technological innovation through networking within a cluster. In this study, we analyze network-specific structural and behavioral characteristics of innovation clusters with the intention of delving into differences in learning performance in clusters. Based on three representative networks of real world, scale-free, broad-scale, and single-scale networks, the learning performance of entire organizations in a cluster is examined by the simulation method. We find out that the network structure of clusters is important for the learning performance of clusters. Among the three networks, the scale-free network having the most hub organizations shows the best learning performance. In addition, the appropriate level of openness that maintains long-lasting diversity leads to the highest organizational learning performance. This study confirms the roles of innovation clusters and implies how each organization as a member of a cluster should run their organization.