Research On Chinese Inter-city Innovation Network's Formation Mechanism And Its Impact On City Innovation Performance

In the era of knowledge economy,the competition for the innovation of science and technology in cities has become increasingly fierce.To promote the ability of selfindependent innovation and occupy the top of the development of science and technology has been a strategy for worldwide countries to seek for new economical prosperity.Cities are gathering space for innovation elements and important bearing platform to promote the implementation of national innovation-driven development strategies.Accelerating the construction of innovative cities is of great significance for realizing the goal of building innovative power.With the acceleration of the pace of technological development,the demand for innovation has become increasingly complex and cities have turned to collaborative innovation.Inter-city innovation network has become an important carrier for cities to acquire external innovation resources and build innovative advantages.Therefore,analyzing the Chinese inter-city innovation network's proximity mechanism and its impact on city innovation performance is of great practical significance to enhance Chinese cities' innovation capability.Reviewing the existing research results,there is still limited research on this aspect.So,this article uses co-patent dataset to construct Chinese inter-city innovation network and analyzes its status quo.Then,by summarizing predecessors' research on innovation network,this article empirically analyzes the proximity mechanisms of Chinese intercity innovation network,as well as the relationship between innovation network structure embeddedness and the city innovation performance.The main research findings are as follows:The structure of Chinese inter-city innovation network shows two distinct characteristics: obvious hierarchical differentiation and unbalanced spatial distribution.In terms of topological structure,Chinese inter-city innovation network is large-scale but low-density,with a clear central agglomeration trend,forming five-level circle structure.In terms of spatial structure,the spatial distribution of Chinese inter-city innovation network is unbalanced,with the Hu line as the boundary.The network links is intensive in the east and spare in the west.The city level is high in the east and low in the west.Geographic proximity,institutional proximity and cognitive proximity all have a significant role in promoting innovation cooperation among cities in China.However,the effects of the three are different.The geographical proximity has the strongest effect and the effect of institutional proximity is the weakest.Geographic proximity and institutional proximity reinforce each other's role in promoting innovation cooperation among cities.There is a certain degree of alternative relationship between cognitive proximity and geographic proximity.Cognitive proximity can take the place of geographical proximity and expand the geospatial boundaries of the cities' innovation cooperation.Cities that are geographically close will reduce the need for cognitive proximity.The location of cities in the innovation network is different,and their ability to obtain knowledge spillovers and improve their innovation performance is also different.Specifically,cities which occupy central location in the network are easier to acquire and control network resources and better to select partners.Occupying a large number of structural holes has little effect on improving cities' innovation performance.The cities' absorptive capacity play an important intermediary role in the relationship between innovation network structure embeddedness and the cities' innovation performance.Based on the above conclusions,in order to promote the innovation performance of backward cities on the fringes of the inner-city innovation network,this paper puts forward two policy Suggestions.Firstly,increasing innovation cooperation and enhancing network status by improving three-dimensional proximity.Secondly,enhancing absorptive capacity to maximize innovation network knowledge spillover.