Amid the growing significance of online communities in shaping social interaction and information exchange, this study investigates how community-level dynamics influence information-sharing behavior within WeChat communities, a platform uniquely embedded in Chinese digital culture and daily life. Grounded in cognitive emotion theory, we examine community reciprocity, homogeneity, and perceived community receptivity as antecedents of information-sharing behavior, with community informativeness as a moderating factor. This mixed-method study is quantitative-dominant, supported by exploratory qualitative interviews with 32 active users, which informed the development of a large-scale survey involving 365 respondents. Findings reveal that reciprocity and homogeneity significantly enhance perceptions of receptivity, promoting users’ willingness to share information. Perceived community receptivity is a cognitive mechanism that fosters trust and psychological safety, encouraging active participation. Moreover, community informativeness significantly moderates the effect of receptivity on sharing behavior, amplifying user engagement in highly informative environments where shared content is perceived as valuable and relevant. These results offer new insights into the socio-cognitive processes underlying information-sharing in virtual settings and provide practical implications for community managers and platform designers seeking to foster participatory, trust-based digital settings.

Low user participation and less knowledge contribution seriously threaten the sustainability of online question and answers communities. Although researchers studied the different aspects of knowledge contribution and proposed useful suggestions, there is still no thorough study on the knowledge contribution pattern of consistent geeks’ that can help improve low participation. According to social cognitive and self-determination theory, peers follow credible sources or role models in their participation patterns and are influenced by the community environment. Based on social cognitive and self-determination theory, we have studied the most consistent geeks of StackOverflow for the period between 2010–2020 to employ the results to activate dormant users. Two-step system GMM results revealed that most users take a free ride and hesitate to reciprocate; knowledge-seeking negatively influences the quantity of contributed knowledge. Peer recognition and repudiation positively influence the knowledge contribution of active geeks, whereas reputation scores and badges negatively influence the contributed knowledge's quantity and quality. Social interaction's role as moderator is also different for quantity and quality of knowledge contributed. Study results improve the existing literature and provide comprehensive managerial implications to improve low participation and create a progressive knowledge contribution environment.

Apart from the well-recognized popularity and importance of online question-and-answer (Q&A) communities, the sustainable growth of these platforms is a serious concern faced by online knowledge-sharing platforms. Personality traits play a significant role in shaping human behavior and decision-making. Numerous studies have attempted to understand the volunteer knowledge contribution and provide solutions to improve low participation. However, the role of personality traits of online knowledge-sharing community users in their volunteer knowledge contribution is still unexplored. Based on the big five personality traits, we have proposed a model to understand the role of personality features of online users. We have collected a cross-sectional dataset from online Q&A community users and applied the SEM-ANN model to conclude our model results that will provide insight into online community users’ contribution behavior. Findings show that agreeableness, openness, extrovert, and conscientiousness positively and neuroticism negatively influence online participation to contribute knowledge. We have also observed that the influence of personality traits significantly differs for both genders, and negativity bias is stronger in male users. Study results provide new insight into understanding online users’ behavior and provide a distinct angle to understand and add value to previous studies’ findings on online Q&A community users.

Online question and answer (Q&A) community users’ knowledge contribution behaviour was studied using primary and secondary data and different research approaches. However, this topic is never explored in the context of content (knowledge) shared in these communities. Furthermore, online social interaction's role as a mediator is also ignored in online Q&A communities. This study model explored community recognition, online social interaction, devotion to community, self-satisfaction, and a sense of reciprocation's role in the knowledge contribution behaviour of Q&A community users. We collected 709 online Q&A community users’ responses and used SEM-ANN two-stage hybrid approach to capture linear and nonlinear relationships between variables. Results revealed that all explanatory variables are positively significant, while the sense of reciprocation is negatively significant to knowledge contribution. It strengthens the earlier researcher's claim that the term ‘tragedy of common’ implies online Q&A communities. Normalised importance results in the second stage figuring out that community recognition, online social interaction, and community devotion are the most influential factors behind knowledge contribution in online Q&A communities. Findings amplify our apprehension about the knowledge contribution behaviour of Q&A community users. It also provides evidence that dual-stage deep learning modelling can better capture variables’ linear and nonlinear relationships.