Dynamic Evolution Model of a Collaborative Innovation Network from the Resource Perspective and an Application Considering Different Government Behaviors

The evolution of a collaborative innovation network depends on the interrelationships among the innovation subjects. Every single small change affects the network topology, which leads to different evolution results. A logical relationship exists between network evolution and innovative behaviors. An accurate understanding of the characteristics of the network structure can help the innovative subjects to adopt appropriate innovative behaviors. This paper summarizes the three characteristics of collaborative innovation networks, knowledge transfer, policy environment, and periodic cooperation, and it establishes a dynamic evolution model for a resource-priority connection mechanism based on innovation resource theory. The network subjects are not randomly testing all of the potential partners, but have a strong tendency to, which is, innovation resource. The evolution process of a collaborative innovation network is simulated with three different government behaviors as experimental objects. The evolution results show that the government should adopt the policy of supporting the enterprises that recently entered the network, which can maintain the innovation vitality of the network and benefit the innovation output. The results of this study also provide a reference for decision-making by the government and enterprises.

[1]  M. Hekkert,et al.  Smart innovation policy: How network position and project composition affect the diversity of an emerging technology , 2015 .

[2]  Julie M. Hite,et al.  The evolution of firm networks: from emergence to early growth of the firm , 2001 .

[3]  Keith Dickson,et al.  Development of national innovation policy in small developing countries: the case of Cyprus , 2001 .

[4]  Zhang Yong Research on the Innovation Network Structural Impact on Innovation Resource Utilization , 2010 .

[5]  E. V. van Raaij,et al.  Framing and Interorganizational Knowledge Transfer: A Process Study of Collaborative Innovation in the Aircraft Industry , 2014 .

[6]  P. Ritala,et al.  Incremental and Radical Innovation in Coopetition—The Role of Absorptive Capacity and Appropriability , 2013 .

[7]  Cao Li-li A Comparison Research on Networks Structure of Industrial Cluster , 2008 .

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

[9]  Ann-Kristin Zobel,et al.  Benefiting from Open Innovation: A Multidimensional Model of Absorptive Capacity * , 2017 .

[10]  Jianyu Zhao,et al.  Evolution of the Chinese Industry-University-Research Collaborative Innovation System , 2017, Complex..

[11]  Uwe Cantner,et al.  The Network of Innovators in Jena: An Application of Social Network Analysis , 2006 .

[12]  Xiang Li,et al.  A local-world evolving network model , 2003 .

[13]  Cornelia Dröge,et al.  Market orientation, knowledge competence, and innovation , 2015 .

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

[15]  Wei Li,et al.  Empirical Analysis on Evolution and Small World Effect of Chinese Enterprise-Enterprise Patent Cooperation Network: From the Perspective of Open Innovation , 2013, Inf..

[16]  Silvio M. Brondoni,et al.  Innovation and Imitation: Corporate Strategies for Global Competition , 2012 .

[17]  Cai Li The Empirical Research on the Relationship between Shared Resources and Innovation Performance of Technology-Based Firm in Entrepreneurial Cluster , 2008 .

[18]  Ivan Savin,et al.  Emergence of Innovation Networks from R&D Cooperation with Endogenous Absorptive Capacity , 2013 .

[19]  R. Gulati,et al.  STRATEGIC NETWORKS , 2000 .

[20]  Devi R. Gnyawali,et al.  Cooperative Networks and Competitive Dynamics: a Structural Embeddedness Perspective , 2001 .

[21]  J. H. Dyer,et al.  Creating and managing a high‐performance knowledge‐sharing network: the Toyota case , 2000 .

[22]  Colin C. J. Cheng,et al.  Breakthrough innovation: the roles of dynamic innovation capabilities and open innovation activities , 2013 .

[23]  Markku Sotarauta Policy Learning and the ‘Cluster-Flavoured Innovation Policy’ in Finland , 2012 .

[24]  B. Loasby The External Control of Organizations. A Resource Dependence Perspective , 1979 .

[25]  Hugo Pinto,et al.  Tracing the flows of knowledge transfer: Latent dimensions and determinants of university–industry interactions in peripheral innovation systems , 2016 .

[26]  H. Chung,et al.  Market orientation, guanxi, and business performance , 2011 .

[27]  W. Powell,et al.  Network Dynamics and Field Evolution: The Growth of Interorganizational Collaboration in the Life Sciences1 , 2005, American Journal of Sociology.

[28]  Thomas Brenner,et al.  Policy Measures and their Effects in the Different Phases of the Cluster Life Cycle , 2011 .

[29]  R. Gulati,et al.  Environmental Demands and the Emergence of Social Structure , 2016 .

[30]  Sven-Volker Rehm,et al.  Information management for innovation networks - an empirical study on the "who, what and how" in networked innovation , 2016, Int. J. Inf. Manag..

[31]  Abdelillah Hamdouch,et al.  Multiscalar Clusters and Networks as the Foundations of Innovation Dynamics in the Biopharmaceutical Industry , 2010 .

[32]  Fr¬¥ed¬¥erique Sachwald,et al.  Co-operative R&D: why and with whom?: An integrated framework of analysis , 2003 .

[33]  B. Dousset Innovation and network structural dynamics: Study of the alliance network of a major sector of the biotechnology industry , 2005 .