Abstract: Traffic conversion from question browsing to answering is of great significance to Q&A community. It is regarded as a multi-factor concurrent process of the synergy of information clues within the Q&A interface. Employing fuzzy-set qualitative comparative analysis, this study examines 2,085 questions from Zhihu to explore how traffic conversion in the Q&A community can be facilitated, with supplementary insights from regression analysis. The findings reveal that the configuration path of high traffic conversion rate includes readable question type of "low competition-high social concern" and "low competition-strong social influence". Conversely, the configuration path for non-high traffic conversion rates is causal asymmetry. Furthermore, the configuration paths of high traffic conversion are different for questions with different degrees of knowledge structure. These findings elucidate the user attention distribution in Q&A communities during the transition from browsers to answerers and reveal the mechanisms of traffic conversion, which helps to facilitate traffic conversion of knowledge sharing platform and optimize the Q&A interface design.

Key words: Q&A community, Traffic conversion, Attention distribution, Endogenous attention, Exogenous attention, Fuzzy-set qualitative comparative analysis(fsQCA)