创新网络与知识流动驱动区域创新能力提升路径研究

Research on Innovation Network and Knowledge Flow Driving Regional Innovation Capacity

摘要:

基于组态视角的fsQCA方法，整合创新网络层面与知识流动层面的6个要素，以我国内地29个省市为案例样本探索驱动区域创新能力有效提升的组态构型与发展路径。结果发现，单个要素并不构成驱动高区域创新能力的必要条件，同时，高知识扩散在解释区域创新驱动原理时扮演更为普遍的角色；高区域创新能力的驱动组态有3类，即高知识流动下的创新网络均衡型、高知识流动下的网络链接主导型与知识扩散—网络规模主导型；创新网络4要素在解释区域创新驱动机制时存在替代关系；产生非高区域创新能力的组态分有4条，且与产生高区域创新能力的组态之间为非对称性关系。

Abstract:

Based on the fsQCA approach from a histological perspective, six elements at the innovation network level and knowledge flow level are integrated to explore the histological configurations and development paths that drive the effective improvement of regional innovation capabilities using 29 provinces and cities in mainland China as case samples. It is found that individual elements do not constitute a necessary condition for driving high regional innovation capacity, while high knowledge diffusion plays a more general role in explaining regional innovation driving principles; there are three types of driving groupings for high regional innovation capacity, namely, innovation network balanced under high knowledge flow, network link dominant under high knowledge flow and knowledge diffusion-network scale dominant; there are alternative relationships among the four elements of innovation networks in explaining regional innovation driving mechanisms; there are four groupings that generate non-high regional innovation capacity, and there is an asymmetric relationship between them and the groupings that generate high regional innovation capacity.