Do smart hotel technologies enhance guest experience? Evidence from online review analytics in the UAE Hospitality Industry

Introduction Smart hotel technologies are increasingly transforming guest experiences in the hospitality industry. However, limited evidence exists on how these technologies are reflected in online hotel reviews and how technology-enabled experiences influence customer satisfaction in the United Arab Emirates (UAE). This study examines the representation of smart hotel technologies in online reviews and their relationship with customer satisfaction. Methods This study employed a quantitative secondary-data design using publicly available hotel review datasets and a structured coding framework for transparent text analytics. The analytical sample comprised 220 review-level observations, including ratings, city, review text, sentiment score, smart-technology theme indicators, smart-technology intensity, and experience-quality index. Descriptive statistics, sentiment classification, theme-frequency analysis, Pearson correlation, and ordinary least-squares (OLS) regression were applied. Results Positive sentiment represented the largest proportion of hotel reviews. Mobile check-in, keyless entry, smart-room controls, contactless services, digital concierge functions, and automated service systems emerged as the most frequently discussed smart-service themes. The mean customer rating was 4.05, the mean sentiment score was 0.45, the sentiment–rating correlation was 0.822, the smart-technology intensity–rating correlation was 0.682, and the OLS model explained 68.5% of the variance in customer satisfaction ( R ² = 0.685). The findings indicate that smart technologies positively influence customer satisfaction when they reduce service friction, support personalization, and complement responsive human service. Discussion The findings demonstrate that smart technologies enhance guest satisfaction when effectively integrated with human-centered service delivery rather than replacing it. This study contributes a transparent secondary-data methodology for smart hospitality research and provides a reusable coded dataset to support future replication and comparative studies.