Article contents
Team Members’ Value Heterogeneity and Innovation Performance: Research Based on Behavioral Science
Abstract
In order to cope with the increasingly diverse and severe challenges, more and more enterprises adopt the strategy of building efficient and responsive teams to cope with the challenges, and the team gradually becomes the basic unit to carry out various social activities, including innovation, rather than individuals. From the perspective of implicit cognition of team heterogeneity, this study uses behavioral coding and analysis methods to integrate online innovation into the context of post-epidemic and Internet era to study the role of innovation behavior in the innovation process from a micro level. Based on the summary of previous research in the field of behavior observation, this paper proposes and improves the online team innovation experiment, and supplements and validates the coding scheme of "Analyzing Idea Finding Interactions (AIFI)". Furthermore, empirical research and experimental research are combined to analyze the relationship between team heterogeneity and innovation performance and the role of innovation behavior in the process of innovation. The results of the empirical study found that: value heterogeneity of online innovation team members negatively affects team innovation performance. idea facilitation behavior and idea inhibition behavior weaken the negative correlation between team members’ value heterogeneity and team innovation performance; team spirit facilitation behavior and process organization behavior strengthen the relationship between team members’ value heterogeneity and team innovation performance. Theoretically, this study deepens the research of the team innovation process and expands the research methods. In practice, it provides decision-making reference for innovation process control of enterprise innovation team.
Article information
Journal
Journal of Humanities and Social Sciences Studies
Volume (Issue)
5 (6)
Pages
32-45
Published
Copyright
Copyright (c) 2023 Xinghui Piao, Lu-ying Li, Huang-yi Gui Alhaj, Yan Zhao
Open access
This work is licensed under a Creative Commons Attribution 4.0 International License.