The effects of network characteristics on performance of innovation clusters

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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. (C) 2013 Elsevier Ltd. All rights reserved.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2013-09
Language
English
Article Type
Article
Keywords

TECHNOLOGY-BASED FIRMS; SMALL-WORLD NETWORKS; COMPLEX NETWORKS; SCIENCE PARKS; TRENDS

Citation

EXPERT SYSTEMS WITH APPLICATIONS, v.40, no.11, pp.4511 - 4518

ISSN
0957-4174
DOI
10.1016/j.eswa.2013.01.052
URI
http://hdl.handle.net/10203/173947
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