This study investigates the impact of knowledge diversity on technology innovation output with a particular focus on communities within intraorganizational collaboration networks. It further explores the moderating effects of the quantity of star scientists and community cohesion.
A dataset of patent information from the Chinese biotechnology industry spanning 1985 to 2022, sourced from the CNIPR platform, is used to construct intraorganizational collaboration networks and measure variables. The greedy modularity algorithm is applied to identify community structures within these networks. Additionally, a negative binomial regression model is employed to test the proposed hypotheses.
Knowledge diversity positively influences the technology innovation output of communities within intraorganizational collaboration networks. Meanwhile, the quantity of star scientists and community cohesion have negative and positive moderating effects, respectively. Furthermore, higher community cohesion attenuates the negative moderating effect of the quantity of star scientists.
Previous studies have highlighted the critical role of intraorganizational collaboration networks in facilitating the utilization of diverse knowledge. However, most research has primarily focused on organizations as a whole or teams within organizations. This study shifts the emphasis to the role of communities and pays particular attention to the quantity of star scientists and community cohesion, two critical conditional factors that moderate the influence of knowledge diversity. By incorporating these factors, this study provides a comprehensive perspective on both the microstructures and macrostructures of network communities.
