Artificial Intelligence in Hospitality: Determinants of Tourists’ Behaviour Following AI-Enabled Service Experiences

Artificial intelligence (AI) is transforming hospitality by enhancing service efficiency and customer experiences through AI-enabled services, such as chatbots, automated check-in/check-out, recommendation systems, and intelligent self-service applications. This study investigates the factors shaping tourists’ behavioural responses to AI-enabled hotel services and their subsequent online review sharing behaviour. The proposed framework extends the Model of PC Utilization (MPCU) by integrating constructs from the Motivational Model and UTAUT2 with hospitality specific factors, including perceived ease of use, operational improvement, need for human interaction, perceived risk, perceived intelligence, and trust. A quantitative design and convenience sampling were employed, with data collected through a structured questionnaire from 400 tourists staying at a four-star hotel on a Greek island during summer 2025. Binary logistic regression was used to examine the proposed relationships. The findings demonstrate differentiated associations between tourists’ perceptions of AI-enabled services and positive and negative online review-sharing behaviour, highlighting the roles of ease of use, intelligence, trust, risk, and human interaction. The study contributes by linking AI service evaluations with post-consumption electronic word of mouth and provides practical guidance for combining trustworthy, user-friendly AI with personalized human service.