Abstract
This study examines how travellers evaluate online hotel reviews in an increasingly AI-influenced context and how these evaluations relate to booking intention. Using a sequential mixed-methods design, data were collected through a survey of 197 travellers followed by 15 semi-structured interviews to provide deeper insight into review evaluation processes. The quantitative findings indicate that review authenticity and review credibility are distinct yet closely related dimensions of review evaluation. Review credibility was more strongly associated with booking intention than review authenticity, while awareness of AI-generated content showed only weak associations with authenticity and credibility perceptions. The qualitative findings provide additional insight into how travellers assess review trustworthiness in practice in an AI-influenced environment. Participants reported relying not only on review content, but also on platform reputation, reviewer history, verification mechanisms, review volume and cross-platform consistency when evaluating reviews. Negative reviews, user-generated photographs and transparency cues were frequently viewed as important indicators of trustworthiness. The findings further suggest that travellers do not evaluate all forms of AI involvement in the same way. AI-generated summaries were generally viewed as useful tools for processing large volumes of information, whereas greater caution was expressed toward fully AI-generated reviews claiming to represent personal experiences. Overall, the study suggests that AI does not fundamentally alter travellers’ reliance on online reviews; rather, travellers appear to place greater emphasis on contextual and platform-related cues when evaluating review information. The study contributes to understanding trust evaluation in emerging AI-influenced hospitality review environments and offers implications for hotels, review platforms and policymakers.