Tourists' trust and behavioral intention toward AI-generated destination recommendations

Artificial intelligence (AI) is increasingly influencing tourism decision-making by supporting information search, destination evaluation, and travel planning. Despite the growing use of AI-generated recommendations, limited empirical evidence exists regarding the factors associated with tourists' trust in such systems. This study examines the relationships among perceived usefulness, the importance attached to AI transparency, trust, behavioral intention, and AI and digital familiarity in the context of tourism destination selection. Data were collected through an online survey of 227 respondents and analyzed primarily using partial least squares structural equation modeling (PLS-SEM). The measurement model demonstrated satisfactory reliability, convergent validity, and discriminant validity. The structural results showed significant positive relationships between AI and digital familiarity and perceived usefulness, between perceived usefulness and trust, and between both trust and perceived usefulness and behavioral intention. The relationship between the importance attached to AI transparency and trust was not statistically significant in the structural model. Trust and perceived usefulness explained 65.8% of the variance in behavioral intention. Significant indirect relationships were also identified from perceived usefulness to behavioral intention through trust, from AI and digital familiarity to behavioral intention through perceived usefulness, and through the serial pathway AI and Digital Familiarity → Perceived Usefulness → Trust → Behavioral Intention. Within the predominantly young, female convenience sample examined in this study, greater perceived usefulness and trust were associated with stronger behavioral intention to use AI-supported destination recommendations, while the role of the importance attached to AI transparency was more nuanced. The study contributes to the growing literature on AI adoption in tourism by integrating multiple determinants of AI acceptance within a single framework and provides practical insights for tourism organizations and developers seeking to strengthen user confidence in AI-supported decision-making within the digital economy.