How to Achieve Maximum Participation of Users in Technical Versus Nontechnical Online Q&A Communities?

ABSTRACTABSTRACTLow user participation in online questions-and-answers (Q&A) communities to contribute knowledge is a serious threat to the sustainability of online forums. This subject is rarely addressed in the context of content shared at these forums. Furthermore, no study has introduced solutions to reduce low participation exclusively considering participants’ age, gender, and education level. User participation in the online question and answer (Q&A) communities to contribute knowledge was mostly examined by applying variance-based methodologies using primary and secondary datasets. This study has targeted the asymmetrical relationship between variables to achieve users’ participation in technical and nontechnical knowledge-sharing communities. We have collected valid responses, 382 from nontechnical and 395 from technical knowledge-sharing communities, and applied fuzzy-set qualitative comparative analysis (fsQCA) to get various equally effective configurations. These configurations explain maximum users’ participation in sharing knowledge in online Q&A communities. fsQCA results have also revealed that to achieve maximum user participation and knowledge contribution in technical knowledge-sharing communities, a sense of reciprocation and online social interaction are the necessary conditions, whereas for nontechnical knowledge-sharing communities, online social interaction is the necessary condition. Study findings are important for online Q&A community managers to minimize low user participation and achieve maximum knowledge contribution in online communities.KEYWORDS AND PHRASES: Fuzzy-set qualitative comparative analysis (fsQCA)Q&A communityknowledge-sharing communitiestechnical Q&A communitiesnontechnical Q&A communitiesfactor configurations AcknowledgmentsThis research is funded by the National Natural Science Foundation of China under grant nos. 72174018, 71932002, 72172145 and 71722007 Beijing Natural Science Fund under grant nos. 9222001 and 9212020; and the Philosophy and sociology science fund from the Beijing Municipal Education Commission [SZ202110005001].Disclosure StatementNo potential conflict of interest was reported by the authors.Supplemental MaterialSupplemental data for this article can be accessed online at https://doi.org/10.1080/10864415.2022.2123645Additional informationNotes on contributorsSohaib MustafaSohaib Mustafa (Sohaibmustafa44@gmail.com;corresponding author) is a Ph.D. scholar at the College of Economics and Management, Beijing University of Technology, China. He has published more than 10 papers in internternatoinal journals and conference proceedings. His recent research interests include knowledge management, human–computer interaction, electronic commerce, fuzzy-set qualitative comparative analysis (fsQCA), Big Data management, social psychology, and technology adoption.Wen ZhangWen Zhang (zhangwen@bjut.edu.cn;corresponding author) is a professor at the College of Economics and Management, Beijing University of Technology. He received his Ph.D. degree in knowledge science from the Japan Advanced Institute of Science and Technology. He has published more than 50 papers in internternatoinal journals and conference proceedings. Dr. Zhang’s recent research interests include online communities, electronic commerce, data analytics, and information systems.