Determinants of Using AI-Based Chatbots for Knowledge Sharing: Evidence From PLS-SEM and Fuzzy Sets (fsQCA)

While adopting chatbots powered by artificial intelligence could enhance knowledge sharing, it also causes challenges due to the dark side of these agents. However, research on the factors influencing chatbots for knowledge sharing is lacking. To bridge this gap, we developed the integrated chatbot acceptance-avoidance model, which looks at the positive and negative determinants of using chatbots for knowledge sharing. Through a comprehensive questionnaire survey of 447 students, the research model is evaluated using the partial least squares-structural equation modeling (PLS-SEM), a symmetric approach, and fuzzy set qualitative comparative analysis (fsQCA) as an asymmetric approach. The PLS-SEM results supported the positive role of performance expectancy, effort expectancy, and habit and the negative role of perceived threats in affecting chatbot use for knowledge sharing. Although PLS-SEM results revealed that social influence, facilitating conditions, and hedonic motivation have no impact on chatbot use, the fsQCA analysis revealed that all factors might play a role in shaping the use of chatbots. In addition to the theoretical contributions, the findings provide several managerial implications for universities, instructors, and chatbot developers to help them make insightful decisions and promote the use of chatbots.

Automated summary

While adopting AI-powered chatbots could enhance knowledge sharing, it also presents challenges. However, research on the factors influencing chatbot use for knowledge sharing is lacking. To bridge this gap, a chatbot acceptance-avoidance model was developed and evaluated using survey data from 447 students. The results revealed the positive role of certain factors such as performance expectancy and habit, as well as the negative role of perceived threats. Additionally, the findings showed that different analysis approaches may provide different insights regarding the impact of factors on chatbot use.