社会问答社区的持续知识贡献:自我呈现和动机启示的调节作用

Lijuan Luo, Yuwei Wang, Siqi Duan, Shanshan Shang, Baojun Ma, Xiaoli Zhou
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引用次数: 0

摘要

本文基于社会资本、形象动机和动机启示的视角,探讨基于关系的动机、基于社区的动机和基于个人的动机对社交问答社区中用户持续知识贡献的直接和调节作用。设计/方法/方法作者从中国一个流行的社交问答社区收集了10193名用户的面板数据。然后,采用负二项回归模型对收集到的数据进行分析。研究结果表明,社会学习、同伴认同和知识寻求正向影响用户的持续贡献行为。然而,研究结果也表明,社会接触会产生相反的效果。此外,自我呈现可以调节社会因素对用户持续使用行为的影响,而动机支持的调节作用不显著。首先,本研究开发了一个全面的动机框架,有助于更深入地了解社交问答社区知识贡献的潜在机制。其次,本研究进行了面板数据分析,以捕捉动机随时间的影响,而不是在固定的时间点上的意图。第三,研究结果可以帮助社交问答社区的经营者优化社区规范和激励机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Continuous knowledge contribution in social Q&A communities: the moderation effects of self-presentation and motivational affordances
Purpose Based on the perspectives of social capital, image motivation and motivation affordances, this paper explores the direct and moderation effects of different kinds of motivations (i.e. relationship-based motivation, community-based motivation and individual-based motivation) on users' continuous knowledge contributions in social question and answer (Q&A) communities.Design/methodology/approachThe authors collect the panel data of 10,193 users from a popular social Q&A community in China. Then, a negative binomial regression model is adopted to analyze the collected data.Findings The paper demonstrates that social learning, peer recognition and knowledge seeking positively affect users' continuous contribution behaviors. However, the results also show that social exposure has the opposite effect. In addition, self-presentation is found to moderate the influence of social factors on users' continuous use behaviors, while the moderation effect of motivation affordances has no significance.Originality/value First, this study develops a comprehensive motivation framework that helps gain deeper insights into the underlying mechanism of knowledge contribution in social Q&A communities. Second, this study conducts panel data analysis to capture the impacts of motivations over time, rather than intentions at a fixed time point. Third, the findings can help operators of social Q&A communities to optimize community norms and incentive mechanisms.
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