Abstract
The Online Q&A community provides platforms for Internet users to exchange and share knowledge. Its rapid development has caused the problem of information overload, which promotes users' demand for precise and personalized information. In light of this, we innovatively analyze the correlation between knowledge quality and user behaviors. First, we propose a novel approach method to evaluate the answer quality through a machine learning approach, which applies the information adoption theory to the design of a text classification model. Hereafter, with applications of motivation crowding theory, this paper introduces the classification results of answer quality by machine learning algorithms into the empirical research model to explore the factors that motivate users in both participation and high-quality user-generated content creation. Results show that both extrinsic and intrinsic factors determine the quality of knowledge contributors' answers. Further, certain external interventions (monetary rewards) can crowd out the effects of knowledge contributors’ intrinsic motivations (knowledge self-efficacy). It enriches the research on user knowledge contribution behavior in online Q&A communities and also provides theoretical guidance and suggestions for community operation.
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