Can AI improve hotel service performance? A systematic review using ADO-TCM
Tong Wang, Eugene Cheng‐Xi Aw, Garry Wei‐Han Tan, Erose Sthapit, Xi Li · Tourism and Hospitality Research · 2025
This study systematically reviewed studies on artificial intelligence (AI) technology and hotel employees’ service performance through bibliometric analysis and content analysis based on the ADO-TCM framework. 72 relevant literature included in the Scopus database between 2017 and 2024 were selected for the study. R Studio and VOSviewer software were used to analyze the data. This study also examines four major thematic clusters in the research area over the last 5 years: (1) the impact of automation on hotel personnel: Employment and training perspectives; (2) technology adoption in the hospitality industry: The interrelationship of customer perceptions, artificial intelligence, and employee roles; (3) employee outcomes related to AI adoption in the hospitality industry; (4) automating decision-making from the hotelier’s perspective. In addition, this study constructs an integrative ADO-TCM conceptual framework that systematically links antecedents, decisions, outcomes, theoretical foundations, contexts, and methods pathways. Based on the findings, the study proposes a research agenda covering aspects such as AI design, culture, ethics, tourism employment, and employees’ psychological responses.