Understanding the drivers of intention to adopt AI-based conversational recommendation systems for travel planning

Although AI-based conversational recommendation systems (CRSs) are beginning to be used in the tourism industry due to the rapid development of AI technology, tourists are still skeptical about using AI-based CRSs for travel planning. Therefore, it is necessary to explore the factors that drive tourists to adopt CRSs for travel planning. Based on affordance-actualization (A-A) theory, this study explored the effects of recommendation and conversation attributes of CRSs on tourists’ cognitive trust, affective trust, and intention to adopt CRSs for travel planning. This study conducted a questionnaire survey of 415 tourists and empirically validated the model using structural equation modeling approach. The results of the study show that the personalization, explainability, understandability and naturalness of CRSs have a significant positive effect on tourists’ cognitive trust, and personalization, explainability and naturalness have a significant positive effect on tourists’ affective trust. In addition, both cognitive trust and affective trust have a significant positive effect on tourists’ adoption intention. The study also provides practical implications and related recommendations for future research.