Structure Dynamics and Evolution Mechanisms of Technology Knowledge Transfer Networks

Abstract

Building an extensive technology knowledge transfer network (TKFNS) is critical for entities in emerging technology integration industries (e.g., biomedical engineering) to absorb external key knowledge, mitigate R&D risks, and improve knowledge innovation capability. However, existing studies mostly focus on single relational TKFNS and static network analysis, failing to reveal the structural dynamic characteristics and evolution mechanisms of TKFNS embedded with both explicit and implicit knowledge transfer. We apply the identified complex relationship data of innovation cooperation and right transfer from 2011 to 2020 to construct TKFNS and use social network analysis (SNA) and temporal exponential random graph model (TERGM) to explore its structural dynamic characteristics and evolution mechanisms. It is found that there are structure dependent effects in TKFNS, and convergence, multiconnectivity, and transfer closure are obvious. There is time dependent effect in TKFNS, and path dependence exists. Organization homogeneity, geography homogeneity, and geography proximity have significant contributions to network evolution, but the innovation capability, cognitive proximity, and social proximity do not. This study promotes our understanding of TKFNS and is of great revelation to accelerate the knowledge transfer of emerging technology industries.