Research About The Influence Mechanism Of The Social Capital On Knowledge Spillovers In Industry University And Research Institute Cooperation Network

Enterprises, universities and research institutions form Industry University and Research Institute(IUR) cooperative innovation network through the cooperating, and which become a channel that can’t be ignored to obtain social capital, enterprises gain new information and new technologies through participating in the innovation network, universities and research institutions take part in cooperation networks and help enterprises translate the knowledge into technological achievements, so IUR cooperative innovation network become an important way of knowledge spillover. Taking Guangdong Province as an example, using the 2006-2014 patent application data of IUR cooperative innovation, combining with the social network analysis method, we construct the IUR cooperative innovation network model, and inspect the social capital structure dimension of IUR cooperative innovation network through analysing the structure index of the whole network and single node. The results show that: Firstly, the scale of the innovation cooperation network is expanding, the number of foreign nodes is gradually increasing, while the network density is decreasing, the network relationship is gradually sparse. Secondly, the degree of cohesion and centrality of the network are not obvious, but advancing step by step, and the center and the surrounding of the network are not clear. Lastly, the social capital of network main part is not balanced, the majority of nodes which are in the center position of network and has advantage of structural holes are colleges or universities, so there is fierce competition for enterprises to obtain innovation resources. At the end the paper also built knowledge spillovers function model, used econometrics to analyse the influence of IUR cooperation network on knowledge spillovers. The results show that there are very significant influences of the network structures on knowledge spillovers, as well as vertices closeness centrality, and the number of exogenous nodes. But knowledge spillovers would decline along with the increase of network density, the average degree of the network, and the betweenness centrality of the network. Based on the results, the paper put forward some policy suggestion to advance knowledge spillovers in the IUR cooperation network.