Predicting users knowledge contribution behaviour in technical vs non-technical online Q&A communities: SEM-Neural Network approach

Abstract

ABSTRACT 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.