From Word-of-Mouth to Word-of-Machine: Examining AI-Generated Travel Recommendations Through the Word-of-Machine (WOMa) Perspective

Abstract

Generative artificial intelligence (AI) is increasingly used in travel planning, yet limited research has examined how travelers evaluate AI-generated travel recommendations as an information source. This study examines travelers’ evaluations and behavioral intentions toward such recommendations, extending and contextualizing the Word-of-Machine (WOMa) perspective within tourism using an Information Adoption Model (IAM)-informed framework. An online survey of 485 U.S. respondents with prior experience using AI for travel planning measured trust, perceived usefulness, perceived ease of use, information presentation, and behavioral intention. Respondents generally evaluated AI-generated travel recommendations favorably, and all four predictors were positively associated with behavioral intention. The regression model explained 84.5% of the variance in behavioral intention (R2 = 0.845), while demographic differences were observed, particularly across household-income groups. The study contributes to tourism research by extending the WOMa perspective to AI-generated travel recommendations and examining the factors associated with travelers’ willingness to use them. Practically, the findings highlight the importance of useful, trustworthy, clearly presented, and easy-to-use AI recommendations, while maintaining opportunities for verification and human judgment.