Research On The Influencing Factors Of User’s Knowledge Activities In Online Social Q&A Communities

As social elements continue to integrate with media technology, social Q&A communities are playing a more and more important role in people’s daily lives. The opinions, insights, experiences and perspectives shared by users inject high quality content into the Internet continuously, helping information seekers find answers quickly and effectively, and promoting the circulation and exchange of knowledge. However, the combination of social elements and media technology does not mean that the exchange of knowledge in communities will c ertainly take place. The behavior of users, as the center of social Q&A communities, is related to the operating results of communities directly. Therefore, the study of users’ behavior in social Q&A community is of important practical significance and val ue to the operation and development of the community.This thesis adopts the behavior-oriented research paradigm, and explores users’ participation behavior as well as their influence factors from both the macro and micro level. At the macro level, this thesis is user-centered. Through continuous tracking users’ knowledge-contributing and seeking behaviors in social Q&A community, basing on the theories of social capital, social exchange, social cognition, this thesis researches the influence of trust, soci al feedback, social exposure, norms of reciprocity and identity on users’ knowledge-contributing behavior and continuing knowledge-seeking behavior. Considering the dependent variables are count variables and the possible problem of over-dispersion, this thesis empirically analyzes the above-mentioned factors using the Negative Binomial Regression model. At the micro level, this thesis is problem-centered. On the one hand, by continuous tracking the responses to questions and users’ information, basing on the theory of social exchange, social capital, etc., we study the influence factors of responses to questions from the perspectives of questioners’ personal characteristics, topic that the questions belong to and the questions themselves. To overcome the dependent variables’ problems of zero-inflation and over-dispersion, this thesis uses a two-stage Hurdle model to empirically analyze the above-mentioned influence factors. At the first stage we analyze the influence factors of whether a question gets a response using the binary variable Logit Regression model. At the second stage, we study the influence factors of the number of answers to a question using the zero-truncated Negative Binomial Regression model. On the other hand, we collect data of questioner’s adoption to the answers of answers and empirically study the influence of information quality, emotional support, source’s reliability, participants’ competition and the fields in which the seekers involved on users’ medical knowledge adoption behavior in social Q&A communities by using the Double Processes Theory of information processing, the techniques of data mining and sentiment analysis, and the binary variable Logistics Regression model.On the basis of previous studies, this thesis further subdivi des users’ participation behavior in social Q&A communities into knowledge-contributing behavior, knowledge-seeking behavior, knowledge-adopting behavior and knowledge-interacting behavior. By continuous tracking the data of these users’ behaviors in social Q&A communities, this thesis tries making some innovative achievements possessing theoretical and practical value in the following aspects.Firstly, the empirical research results on continuing knowledge-contributing behavior indicate that obtained responses from previous knowledge contribution behavior, social exposure, opportunities for word-of-month marketing and the performing of norms of reciprocity all have a positive influence on users’ continuing knowledge-contributing behavior. The results can help community operators to understand influence factors of users’ knowledge contribution deeply, providing a decision reference for function design and operation strategies of the communities.Secondly, the research results on continuing knowledge-seeking behavior indicate that the responses to users’ previous knowledge-seeking behavior will promote users’ continuing knowledge-seeking behavior, while the range of social exposure and identity have a significant negative effect. The researches on continuing knowledge-seeking and knowledge-contributing behavior indicate that their influence factors differ from each other significantly, and social exposure and identity have a totally opposite effect on these two kinds of behavior. The research results not only enrich the theories of users’ participation in virtual communities but also provide a reference for the function design and operation strategies of the communitiesThirdly, this thesis proposes a medical knowledge adoption model in social Q&A communities, adding the prepositive variable of emotion support and the moderating variable of participants’ competition into the traditional knowledge adoption model, and further expands the contents and adoption range of knowledge adoption model. On the one hand, this study makes it possible to identify valid knowledge from the massive amounts of chemical knowledge contributed by users, helping knowledge seekers to identify chemical knowledge of high quality quickly. On the other hand, this thesis can guide medical knowledge contributors to pose influential chemical knowledge and carry out more effective word-of-mouth marketing.Fourthly, this thesis researches the influence factors of responses to questions basing on a two-stage Hurdle model. At the first stage the thesis analyzes the influence factors of whether a question gets a response, while at the second stage, the thesis studies the influence factors of the number of answers to a question. And the empirical results show that the influence factors of the two stages have some differences. The research model can be used to uncover the general rule of the users’ choosing interaction objects more accurately. The research results can provide guidance for community operators in the aspects of question classifying and recommending, and provide supports for knowledge seekers in making seeking strategies.