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How learning effects influence knowledge contribution in online Q&A community? A social cognitive perspective

机译：学习效果如何影响在线Q＆A社区的知识贡献？ 社会认知观点

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摘要

Informative contributions are critical for the healthy development of online Q&A communities, which have gained increasing popularity in solving personalized open-ended problems. However, little is known about whether past contribution behaviors and corresponding community feedbacks received affect the characteristics of subsequent contributions. Drawing upon the social cognitive theory, we examine the learning effects on users' knowledge contribution behaviors. Specifically, we focus on two types of learning effects: enactive learning from one's past contribution experience and vicarious learning from observation of others' performances in a question thread. Using a dataset collected from one of the largest online Q&A communities in China, we find that the length feature of past user contributions that garner highly positive feedback, no matter through enactive or vicarious learning, would influence the informativeness of subsequent contributions in the community. These learning effects are more effective for users with higher social status. The enactive learning effect is stronger for contributors with higher social status. For the vicarious learning on higher-status contributors, the influence of high-vote long answers is stronger, but the high-vote short answers show a weaker effect. Our research provides a deeper understanding of knowledge contribution behaviors in online knowledge communities and guides for establishing a healthy knowledge contribution environment.
机译：信息贡献对于在线Q＆A社区的健康发展至关重要，这在解决个性化的公开问题方面取得了越来越普及。然而，对于过去的贡献行为和相应的社区反馈来说，知之甚少，所接受的影响会影响后续贡献的特征。借鉴社会认知理论，我们研究了对用户知识贡献行为的学习效果。具体来说，我们专注于两种学习效果：从一个人过去的贡献经验和替代地学习别人在一个问题线程中的表演中的替代学习。使用来自中国最大的在线Q＆A社区之一的数据集，我们发现过去的用户贡献的长度特征，无论通过提供的或替代学习，无论是对社会的贡献的信息，都不会产生高度积极的反馈。这些学习效果对具有更高社会地位的用户更有效。对于具有较高社会地位的贡献者来说，积累的学习效果更强大。对于更高地位的贡献者的替代学习，高级答案的影响力更强，但高级短篇答案表现出较弱的效果。我们的研究提供了对在线知识社区和指南的知识贡献行为深入了解，以建立健康知识贡献环境。

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