Visiting, Following, and Knowledge Sharing: Mining Network-Network Relationships in Crowd-Empowered Open Innovation

Social network analysis offers a unique perspective for research on crowd-empowered open innovation. Despite the abundant literature on crowd-based knowledge collaboration, it remains unclear how and to what extent crowd members’ knowledge sharing dynamics are influenced by their other online activities when participating in collective knowledge collaboration. This paper offers an innovative approach to evaluating how crowd-empowered knowledge collaboration can be influenced by crowd members’ different types of social network configurations when they respond to open innovation challenges in such areas as healthcare, technological invention, environmental sustainability, and social equity. Analyzing the behavioral networks of 3844 online open innovation participants, we conducted a series of QAP correlation and regression tests that demonstrate significant network-network relationships when participants are involved in visiting, following, and knowledge sharing activities. Our findings suggest that open innovation participants deliberately construct knowledge sharing networks based on their previous communicative actions rather than doing it blindly. Our examination of network-network relations contributes to the scholarship of open innovation and social network analysis by providing insights for the development of theoretical frameworks that incorporate network-level constructs to better explain the complexities of collaborative open innovation.