A Method to Evaluate Network Efficiency in Industrial Knowledge Transfer: Results From the Delta Region

Abstract

The efficiency of industrial knowledge transfer (IKT) directly affects the level of knowledge connection and collaborative innovation in the industry. However, there is a lack of research, particularly from the perspective of network characteristics, to investigate the efficiency of IKT. Therefore, this study proposes a methodology for measuring the efficiency of weighted industrial knowledge transfer network (IKTN) by employing multiple network indicators. Firstly, based on patent data, a weighted IKTN model with node and edge weights is constructed, and the weighted clustering coefficient and path length of the network are defined. Then, considering the indicators of node weights, edge weights, weighted clustering coefficient, and path length, an efficiency measurement model for the weighted IKTN is established. Finally, we take the environmental protection industry (EPI) in the Yangtze River Delta region of China as the practical case to verify the scientific validity and applicability of the proposed method. The results show that the measurement method proposed can effectively evaluate the node efficiency and overall efficiency of IKTN, and provide a scientific basis for relevant policymaking. This study comprehensively considered multiple factors in the IKTN efficiency measurement and used existing data from the patent database in the weight setting, avoiding the problem of excessive reliance on subjective factors in previous studies that may lead to deviations in the authenticity of the evaluation.

Looking Back to Move Forward: Bibliometric and Visual Analysis of Knowledge Transfer in University-Industry Collaboration

“…It is an indisputable fact that the creation, transfer, and application of knowledge can drive innovation and economic growth [1], [2]. Universities with scientific research missions have always been an important source of new knowledge.…”