高校知识网络对产学协同创新绩效的影响研究

摘要/Abstract

摘要： 从依存型多层网络视角出发，利用1985-2018年42所双一流高校申请的专利数据，构建包括高校知识网络和区域合作网络在内的产学协同创新多层网络结构模型。基于社会网络理论和资源基础观，研究高校知识网络静态特征和动态特征对产学协同创新绩效的影响机制，以及区域合作网络特征的调节效应。研究结果表明：高校知识网络的多样性对产学协同创新绩效具有显著的倒U型影响；高校知识网络的独特性、结构洞、扩张以及稳定对产学协同创新绩效具有显著的正向影响；区域合作网络中心度正向调节高校知识网络的多样性、独特性、结构洞、扩张以及稳定对产学协同创新绩效的影响；区域合作网络结构洞正向调节高校知识网络的独特性、扩张对产学协同创新绩效的影响。

关键词: 依存型多层网络, 高校知识网络, 区域合作网络, 产学协同创新绩效

Abstract: With the rapid development of science and technology, innovation has become an important driving force to promote high-quality economic development. As an important innovation model in the national innovation system, industry-university collaborative innovation plays an important role in promoting the implementation of the strategy of strengthening the country through science and technology. In the process of collaborative innovation, universities play a key role in promoting collaborative innovation and enhancing national innovation strength. Therefore, how to improve the knowledge management ability of universities, enhance the development of industry-university collaborative innovation, and then heighten the overall innovation ability of the country has become a common concern issue in research and practice of innovation. Meanwhile, industry-university collaborative innovation is not only affected by the university knowledge networks, but also affected by the regional collaborative network. In addition, the university knowledge network is no longer limited to the static network structure, but also includes the network behaviors and activities, which is a dynamic process.Based on the perspective of dependent multi-layer network, this paper uses the invention patents data applied by the Double First-Rate Universities from 1985 to 2018 to construct a multiple network research framework including university knowledge network and regional collaborative network. Among them, the university knowledge network and the regional cooperation network are formed by 31 provincial-level regions in China and university knowledge elements, respectively. Further, the dependent variable is the industry-university collaborative innovation performance, that is the total number of patents jointly applied for by the Double First-Rate Universities every year. The independent variables are the static characteristics of university knowledge network, which are the diversity, uniqueness, structural holes and centrality of university knowledge network, and the dynamic characteristics of university knowledge network, which are the expansion and stability of university knowledge network. The moderating variables are the centrality and structural hole of regional cooperation network. At the same time, the number of university cooperative enterprises, the level of regional economic development and the number of scientific and technological papers published by universities are taken as control variables. Therefore, based on the social network theory and resource-based view, this paper constructs the time-fixed negative binomial regression model by using Stata 16.0 to analyze the impact of static and dynamic characteristics of university knowledge network on industry-university collaborative innovation performance, and the moderating effect of regional collaborative network. The results show that the diversity of university knowledge network has a significant inverted U-shaped effect on industry-university collaborative innovation performance; the uniqueness, structural hole, expansion and stability of university knowledge network have a significant positive impact on the industry-university collaborative innovation performance; the structural hole of regional collaborative network has a positively moderating effect between the uniqueness, expansion of university knowledge network on the industry-university collaborative innovation performance; the centrality of regional collaborative network has a positively moderating effect between the diversity, uniqueness, structural hole, expansion, stability of university knowledge network and industry-university collaborative innovation performance.The theoretical contributions of this paper include two aspects: Firstly, this study expands the impact of static characteristics of university knowledge networks on industry-university collaborative innovation performance by considering positive or negative direction. It also makes up for the inadequacy of exploring the relationship between university knowledge networks and university-industry collaborative innovation performance only from a single level. Secondly, from the perspective of interdependent multi-layer network, university knowledge networks, regional collaborative network and industry-university collaborative innovation performance are integrated into one research framework to enrich social network theory.Based on the empirical analysis, this paper puts forward some suggestions for the behavior of universities. Firstly, universities should improve the knowledge network structure, expand their own knowledge coverage areas, comprehensively utilize diverse knowledge and unique knowledge, promote interdisciplinary cross integration, enhance their knowledge reserve and knowledge reorganization ability, and create valuable heterogeneous knowledge resources to improve the efficiency of industry university collaborative innovation. Secondly, universities should take into account the advantages of disciplines and emerging disciplines, develop emerging technologies such as artificial intelligence and blockchain, and promote the dynamic improvement of knowledge base, so as to break through the limitations of existing knowledge rigidity on innovation ability. Finally, universities should give full play to their regional advantages, find the driving point from the regional innovation policies, excavate the policy support and innovation guarantee mechanism provided by the region, and cooperate with the government and enterprises through project cooperation.

Key words: interdependent multi-layer network, university knowledge network, regional collaborative network, industry-university collaborative innovation performance

王海花杜梅刘钊成. 高校知识网络对产学协同创新绩效的影响研究[J]. 科研管理, 2023, 44(2): 116-126.

Wang Haihua, Du Mei, Liu Zhaocheng. Research on the influence of university knowledge network on the industry-university collaborative innovation performance[J]. Science Research Management, 2023, 44(2): 116-126.

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