A predictive model of the knowledge-sharing intentions of social Q&A community members: A regression tree approach

Previous research on the factors affecting knowledge sharing has focused on the relationships between a limited number of variables. However, it is unclear how these factors interact with each other and jointly influence knowledge-sharing intentions. Drawing on social cognitive theory (SCT), this paper performs a decision tree analysis to predict the knowledge-sharing intentions of social question-and-answer (Q&A) community members based on a multitude of environmental and individual factors, including a sharing culture, motivations, and individual characteristics. Data from 1,007 users were collected, and a regression tree model was built using the R package rpart. The results show that high levels of knowledge-sharing intentions occur among those who strongly enjoyed sharing and who perceived fairness within the community. For those who had a moderate or low level of enjoyment, their willingness to share knowledge was jointly affected by the sharing culture and extrinsic motivations.

Automated summary

This study, drawing on social cognitive theory, used decision tree analysis to explore the knowledge-sharing intentions of social Q&A community members. It found that high levels of sharing intentions were associated with strong enjoyment of sharing and perceived fairness, while those with lower enjoyment levels were influenced by the sharing culture and extrinsic motivations.