The effects of network characteristics on performance of innovation clusters

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.

[1]  Robin L. Wakefield.  Networks of accounting research: A citation-based structural and network analysis , 2008 .

[2]  S. Strogatz.  Exploring complex networks , 2001, Nature.

[3]  R. Ferrer i Cancho,et al.  Scale-free networks from optimal design , 2002, cond-mat/0204344.

[4]  B. Asheim,et al.  Regional Innovation Systems: The Integration of Local ‘Sticky’ and Global ‘Ubiquitous’ Knowledge , 2002 .

[5]  H E Stanley,et al.  Classes of small-world networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Andreas Pyka,et al.  Learning in innovation networks: Some simulation experiments , 2007 .

[7]  G. Huber.  Organizational Learning: The Contributing Processes and the Literatures , 1991 .

[8]  J. Brown,et al.  Organizational Learning and Communities-of-Practice: Toward a Unified View of Working, Learning, and Innovation , 1991 .

[9]  A. Barabasi,et al.  Lethality and centrality in protein networks , 2001, Nature.

[10]  R. F. Cancho,et al.  Topology of technology graphs: small world patterns in electronic circuits. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  Constance E. Helfat,et al.  INNOVATION OBJECTIVES, KNOWLEDGE SOURCES, AND THE BENEFITS OF BREADTH , 2010 .

[12]  Johanna L. Francis,et al.  Creating a Cluster While Building a Firm: Entrepreneurs and the Formation of Industrial Clusters , 2005 .

[13]  Albert-László Barabási,et al.  Linked - how everything is connected to everything else and what it means for business, science, and everyday life , 2003 .

[14]  Hans Löfsten,et al.  Science Parks and the growth of new technology-based firms : academic-industry links, innovation and markets , 2002 .

[15]  Joseph Z. Shyu,et al.  Exploring the Interaction between Incubators and Industrial Clusters: The Case of the Itri Incubator in Taiwan , 2003 .

[16]  Michael Porter,et al.  The Economic Performance of Regions , 2003 .

[17]  Roderick E. White,et al.  An Organizational Learning Framework : From Intuition to Institution Author ( s ) : , 2007 .

[18]  Christina Fang,et al.  Balancing Exploration and Exploitation through Structural Design: The Isolation of Subgroups and Organization Learning , 2008 .

[19]  M. Bell,et al.  The micro-determinants of meso-level learning and innovation: evidence from a Chilean wine cluster , 2005 .

[20]  Ard-Pieter de Man.  Knowledge Management and Innovation in Networks , 2008 .

[21]  Brian Wixted.  Innovation System Frontiers: Cluster Networks and Global Value , 2009 .

[22]  J. R. Larson,et al.  Groups as problem‐solving units: Toward a new meaning of social cognition , 1993 .

[23]  Reka Albert,et al.  Mean-field theory for scale-free random networks , 1999 .

[24]  C. H. Castore,et al.  Risky Shift: Social Value or Social Choice? An Alternative Model. , 1971 .

[25]  Sridhar P. Nerur,et al.  The intellectual structure of the strategic management field: an author co‐citation analysis , 2008 .

[26]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[27]  R. Daft,et al.  Toward a Model of Organizations as Interpretation Systems , 1984 .

[28]  K. Börner,et al.  Trends in animal behaviour research (1968–2002): ethoinformatics and the mining of library databases , 2005, Animal Behaviour.

[29]  J. March.  Exploration and exploitation in organizational learning , 1991, STUDI ORGANIZZATIVI.

[30]  Mark S. Granovetter.  The Strength of Weak Ties , 1973, American Journal of Sociology.

[31]  Robert W. Holt,et al.  The decision processes of 6- and 12-person mock juries assigned unanimous and two-thirds majority rules. , 1975 .

[32]  Ramon Ferrer i Cancho,et al.  The small world of human language , 2001, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[33]  L. Argote.  Organizational Learning: Creating, Retaining and Transferring Knowledge , 1999 .

[34]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[35]  A. Salter,et al.  Open for innovation: the role of openness in explaining innovation performance among U.K. manufacturing firms , 2006 .

[36]  K. Motohashi,et al.  Are new technology-based firms located on science parks really more innovative?: Evidence from Taiwan , 2009 .

[37]  Ron Boschma,et al.  The rise of clusters of innovative industries in Belgium during the industrial epoch , 1999 .

[38]  Chun-Ping Huang,et al.  Bibliometric Analysis of Obstructive Sleep Apnea Research Trends , 2009, Journal of the Chinese Medical Association.

[39]  Ray Oakey,et al.  Clustering and the R&D Management of High-Technology Small Firms: In Theory and Practice , 2007 .