Industry-University-Research collaboration networks: the identification and driving factors of key technologies

ABSTRACT Purpose This study aims to analyze the key technologies in Industry–University–Research (IUR) cooperation within higher education institutions, deepen the understanding of the mechanisms of IUR cooperation and the process of technological innovation, and reveal the dynamic evolution patterns and driving mechanisms of key technologies in IUR cooperation alliance networks at different stages. It also provides clear directions and strategic recommendations for cooperation among universities, enterprises, and research institutions. Methodology This study uses patents applied for through IUR cooperation by Chinese Double First-Class universities from 2015 to 2024 as the data basis and employs the Louvain algorithm to divide IUR cooperation applicants. Subsequently, a Technology–Applicant network is constructed at two-year intervals, and key technologies are extracted using network information entropy. The evolution paths of technological characteristics are then thoroughly analyzed. Finally, the study proposes three hypotheses and employs the Exponential Random Graph Model (ERGM) to systematically elucidate the endogenous driving mechanisms of key technology characteristics in the applicant. Findings Over the past decade, IUR cooperation in Chinese Double First-Class universities has undergone a transformation from single technological fields to the deep integration of multiple technological fields and from traditional application areas to emerging ones. The knowledge depth, knowledge width, and knowledge combination capabilities of IUR applicants, as core independent variables, have had varying impacts on network formation across different time periods. Among them, knowledge combination capability has played a significant role in promoting network formation. Research limitations On the one hand, this study mainly focuses on the Double First-Class universities in China and does not cover other types of universities. On the other hand, while the study mainly focuses on the analysis of the IUR technology network, the analysis of the cooperation network between applicants is still insufficient. Practical implications This study provides practical guidance for optimizing IUR cooperation networks by emphasizing the integration of multiple technological fields, balancing knowledge depth and width, enhancing knowledge combination ability, and optimizing the internal network structure. These measures help to strengthen the stability and efficiency of cooperation networks, boost innovative outcomes, and provide strong support for scientific and technological progress as well as economic development. Originality/value This study examines the evolution of key technologies and their impact on IUR cooperation networks in China over ten years. It shows a shift from single to multiple technological fields and from traditional to emerging applications, highlighting Chinese global competitiveness. Core variables like knowledge depth, width, and combination ability differently affect network formation over time, with knowledge combination being consistently significant. Network structural characteristics also crucially regulate stability and efficiency. The findings offer theory-based practical guidance to optimize these networks.