Predicting customer intentions to use hotel chatbots: the role of perceived competence and usefulness

This research examines the adoption of AI-powered chatbots in the hospitality industry by extending theconventional Technology Acceptance Model (TAM) to include perceived competence and perceived understandability.Using data from 266 hotel guests, the study applies Partial Least Squares Structural EquationModeling (PLS-SEM) to test the hypothesized relationships. The results indicate that perceived competenceand perceived understandability significantly shape customers’ perceptions of ease of use and usefulness.Among these, perceived ease of use emerged as the dominant factor influencing customers’ attitudes towardhotel chatbots, which in turn strongly affected their behavioral intentions. In contrast, perceived usefulnessshowed no meaningful association with attitude development in this context. The study contributes to thehospitality chatbot literature by extending TAM in the hotel context by including perceived understandabilityand perceived competence, and it offers practical guidance for improving chatbot adoption in hotel settings.