The Structural Evolution and Driving Mechanism of Intercity Technology Transfer Networks in the Middle Reaches of the Yangtze River: Evidence from TERGM

Technology transfer is an important way to promote technology sharing, optimize resource allocation, and improve the levels of innovation and overall efficiency. Existing research on regional technology transfer primarily focuses on eastern and developed regions of China, such as the Yangtze River Delta, Beijing-Tianjin-Hebei, Guangdong-Hong Kong-Macao, and the Pearl River Delta; therefore, inland regions are relatively underreported. The middle reaches of the Yangtze River are one of China's five national-level urban agglomerations. There is a gap between academic attention to its technology transfer system and the practice of regional development planning and construction. Against this backdrop, this study collected patent transfer information from 2010 to 2021 from the IncoPat patent service website and constructed intercity technology transfer networks in the middle reaches of the Yangtze River for four periods. Based on the application of network analysis methods and temporal exponential random graph models, the structural evolution and driving mechanism of intercity technology transfer in the middle reaches of the Yangtze River were quantitatively analyzed. The results showed that: (1) Changsha and Wuhan were consistently at the core of regional technology transfer. After the release of the urban agglomeration development plan for the middle reaches of the Yangtze River, Nanchang emerged as a new growth pole. The leading roles of Wuhan and Changsha in this region were primarily supported by technology diffusion, whereas Nanchang's technology diffusion and absorption were relatively balanced. The three central cities organized three technology transfer communities through a hub network-shaped structure that was highly coupled with provincial boundaries, and the inter-provincial technology flows were weak. (2) The intercity technology transfer network in the middle reaches of the Yangtze River gradually evolved to be characterized by both a core-periphery hierarchy and small-world clusters. However, the polarization of the network weakened, whereas the clustering coefficient and transfer efficiency increased. Moreover, the technology transfer paths present a stable trend with slight changes, suggesting incremental innovation. (3) Intercity technology transfers are not only affected by innovation size, intercity spatial distance, and the provincial boundary effect, but also by the endogenous mechanism of network self-organization. The reciprocity, activity, popularity, and hierarchical transitivity of intercity technology transfers positively affect network development, and the stability effect is stronger than the innovation effect in the network evolution process. Based on these findings, policy suggestions for optimizing technology transfer paths are proposed from the perspectives of promoting inter-provincial technology flows, enhancing spatial spillover and sector integration, and taking advantage of the network self-organizing laws of preferential attachment and clustering development. This study contributes to the literature by applying network econometric models for panel data to the analysis of the influential mechanism of innovation networks and corresponding the statistical properties of networks at different scales to the variable specification of endogenous and exogenous driving factors. Empirical research has verified the applicability of this analytical framework and methods in innovation geography, which could also provide new findings from the endogenous micro-mechanisms of the network to better understand the processes of intercity technology transfer.