Understanding the drivers of intention to adopt AI-based conversational recommendation systems for travel planning
Year: 2026
Type: article
Study design: Observational Study
Abstract: 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.
Source: Discover Artificial Intelligence
Language: English
Topic: AI in Service Interactions
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,590
OpenAlex ID: W7160836368