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

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.