Research on Multiple Promotion Paths of Science and Technology Service Industry Cluster Cities’ Innovation Performance Based on fsQCA

Science and technology service industry cluster cities are important components and key nodes of innovation networks driving the flow and configuration of innovation. In practice, cities embedded in the same network have differentiated innovation performance owing to differing research and development (R&D) investment and personnel. Therefore, how innovation networks affect these differences, and how innovation network embedding and endogenous innovation efforts interact to affect cities’ innovation performance, requires further analysis. From the perspective of configuration, using the QCA method, this study presents structural and relational embeddedness, R&D funds, and R&D personnel into the same analytical framework using the two levels of network embeddedness and endogenous innovation efforts. The cases come from 23 science and technology service cluster cities in China. The results indicate that: (1) there are multiple concurrent causal relationships among multi-level innovation network embeddedness, endogenous innovation efforts, and innovation performance. A single condition is not enough to stimulate high innovation performance, and multiple conditions must be linked to have a combined effect. (2) There are four configurations to high innovation performance, which can be summarized into two paths: realtionship-personnel drivern and structural-fund driven. The research finds 12 cases, most of which pertain to eastern cities such as Beijing, Shanghai and Guangzhou. (3) There are two paths leading to low innovation performance, which can be summarized into two configurations: relationship-fund inhibited and structural-relationship inhibited. The results show most of which pertain to cities mostly in the Northeast and Midwest. They overcome the shortcomings of the existing single-level causality research, explain the “embeddedness paradox” in the network embeddedness theory, and help reveal the causal mechanism driving cities’ high innovation performance. From the perspective of “causal asymmetry”, this paper finds the equivalent driving path affecting high innovation performance of these cluster cities, and discusses the inhibition path of high innovation performance which broadens application of the QCA method.