An Empirical Research About How Geographic Proximity And Cognitive Proximity Influence On The Innovation Of High-tech Industrial Clusters

With the rise of knowledge-based economy, high-tech industry cluster becomes the key point in its industry, and the symbol of competitiveness which one country or region owns. Innovation is the source of the competitiveness of the high-tech industrial clusters. With the tacit knowledge and knowledge spillover theory proposed, the discussion of how to improve innovation in one high-tech industry is no longer confined to the simple input-output relationships. More and more ideas about Return in Scale and External Effects are added to the cost-oriented industrial production theory. Under the background mentioned above, related academia begins to pay more attention to the new view of Dimensions of Proximity in order to explore the essential factors to the innovation of high-tech industry cluster.From the view of Dimensions of Proximity, this article analyzes the bottlenecks of related research paper, and finds that the ill-defined concept of proximities is the key problem needed to be solved. Based on that, this paper re-classified and defined the concepts of different proximities. And finally re-classified them in three types: geographic proximity, cognitive proximity and organizational proximity. According to this reclassification and lots of related theories, this paper deducts the dynamic relationship between proximities and the innovation of high-tech industrial cluster. This relationship explains how those proximities works on the innovation of high-tech industrial cluster.Upon completion of the discussion about the mechanism between the high-tech industry cluster innovation and proximities, this article proposes four hypothesizes about the relationships between the geographical proximity, cognitive proximity and cluster innovation. And the empirical tests to them are based on the data of national software industrial parks in China for recent five years. There are two methods used:artificial neural network and ordinary least squares (OLS). During the process of artificial neural network analysis, there are three networks be built, and each of them stimulates the relationships between four innovation factors and innovation. And the networks show the trend lines of those relationships. Following the trend lines, this paper builds a mathematical model which reflects the relationship between innovation and its factor. And the data mentioned above are used in the OLS estimate. Finally, the results are reached as a concrete mathematical function. According to the comparison among theory mechanism and two empirical analysis results, this paper can get following conclusions:firstly, at the development phrase and mature phrase of high-tech industrial cluster development, geographic proximity has a positive influence on innovation appearance of high-tech industrial cluser, but the marginal effects is decreasing according to the developing of cluster, and the increasing of innovation. Secondly, cognitive proximity has an active influence on cluster innovation. Moreover, the achievement of external knowledge promotes the increase of innovation very much. Finally, the direct investment on research and development can enforce the capacity of innovation, but the marginal return is decreasing. Based on the conclusions above, there are some policy suggestions for how to promote the innovation performance of high-tech industrial cluster.