Unlocking the power of social capital: How social capital embedding shapes users' continuous knowledge sharing intention in social networks

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In the evolving landscape of new media, particularly with the rise of social networks, the dynamics of information dissemination have undergone significant transformations. Social networks, encompassing platforms like WeChat, Weibo, and online communities, have become pivotal in shaping user interactions and knowledge sharing. This study delves into the mechanisms by which social capital, categorized into bridge social capital and bonding social capital, influences users' continuous intention to share knowledge within social networks. Utilizing a comprehensive model that includes relational embeddedness and structural embeddedness as mediators, and metacognitive experience and perceptual fit as moderators, this research employs Structural Equation Modeling (SEM) and Multilevel Modeling to analyze data collected through a questionnaire survey. The findings reveal that both bridge and bonding social capital positively affect continuous knowledge sharing intention through the mediating roles of relational and structural embeddedness. Additionally, metacognitive experience and perceptual fit significantly enhance the impact of these mediators. The study also highlights differential effects of social capital across gender, age, and education levels. The implications of these findings for both theory and practice are discussed, underscoring the importance of understanding and leveraging social capital in fostering continuous knowledge sharing in social networks. • First integration: social capital, embeddedness, reinforcement theories • Novel cross-level moderators: metacognitive experience, person-group fit • Differential pathways: relational versus structural embeddedness mechanisms • Large-sample validation across Chinese social media platforms • Reinforcement mechanisms enhance social capital model predictive power

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