Analysis on the Influence Path of User Knowledge Withholding in Virtual Academic Community - Based on Structural Equation Method-Artificial Neural Network Model

• Published in: Frontiers in psychology Vol. 13; p. 764857
• Main Authors: Le, Chengyi, Li, Wenxin
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 07-02-2022
• Subjects: artificial neural network (ANN); knowledge hiding; knowledge withholding intention; Psychology; SEM-ANN; virtual academic community; artificial neural network (ANN); SEM-ANN; knowledge hiding; knowledge withholding intention; virtual academic community
• ISSN: 1664-1078; 1664-1078
• DOI: 10.3389/fpsyg.2022.764857

The phenomenon of knowledge withholding is a vital issue that undermines knowledge sharing and innovation, hinders the development of offline and online organizations. Clarifying the relationship between influencing factors and knowledge withholding is significant to improve the phenomenon of knowledge withholding in offline and online organizations. Few types of research focus on the online virtual academic community and integrate the three factors of knowledge, individual, and environment to research knowledge withholding. To solve the limitation, this research is based on sociology and psychology-related theories. The two dimensions of enabling and inhibition are divided into factors affecting knowledge withholding. An attempt is made to explore the path between the three types of factors influencing knowledge, individual and environment, and knowledge withholding. This study collected data from 616 users in China's virtual academic community. It used a structural equation model combined with a cross-layer connected neural network to conduct an empirical analysis on the proposed hypothesis. The results found that: in the virtual academic community, knowledge power in the enabling dimension is the main reason for users to form knowledge psychological ownership, which affects users' knowledge withholding. However, the effect of professional commitment on users' knowledge psychological ownership is not significant. After SEM-ANN model fitting, the combined inhibitory effect of community privacy protection and community reciprocity on user knowledge withholding in the inhibition dimension is significantly improved. This research has a specific guiding significance for enhancing the knowledge withholding phenomenon of the virtual academic community and creating an excellent academic exchange atmosphere.