AI-Driven Service Innovation, Customer Satisfaction, and Guest Loyalty: Evidence from Shenyang Airport Hotel, China

This study investigated how guests' technological perceptions of Artificial Intelligence (AI) applications perceived usefulness (PU), perceived ease of use (PEOU), enjoyment (ENJ), and privacy concerns (PC) influence customer satisfaction (CS) and customer loyalty (CL) at the Shenyang Airport Hotel, a three-star airport property in a secondary Chinese city. Grounded in an integrated framework combining the Technology Acceptance Model (TAM) with Customer Experience Theory. A quantitative survey of 452 guests who had experienced AI-enabled services during their stay was conducted. Data were analysed using reliability assessment, exploratory factor analysis (to verify the factor structure in a novel context), Pearson correlation, multiple regression, and bootstrap mediation analysis. The results indicated that perceived usefulness (β = 0.314, p < 0.001), enjoyment (β = 0.141, p < 0.01), and perceived ease of use (β = 0.127, p < 0.01) each positively influenced customer satisfaction, whereas privacy concerns exerted a significant negative effect (β = −0.205, p < 0.001). Customer satisfaction, in turn, significantly predicted customer loyalty (β = 0.409, p < 0.001), and partially mediated all four perception-to-loyalty pathways. One-way ANOVA further revealed significant differences in customer loyalty across age groups, educational levels, and income brackets. Theoretically, this study extended TAM by simultaneously incorporating affective and security-related dimensions into AI service acceptance within an underexamined hospitality context. Practically, the findings offer airport hotel managers in secondary cities evidence-based guidance on prioritising utility-enhancing AI features, simplifying service interfaces, enriching hedonic engagement, and communicating data governance policies transparently to mitigate privacy-driven dissatisfaction. The study was limited by its single-site, cross-sectional design; future research should adopt longitudinal or multi-site approaches to strengthen generalisability.

@article{Zu2026,
  title     = {AI-Driven Service Innovation, Customer Satisfaction, and Guest Loyalty: Evidence from Shenyang Airport Hotel, China},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Dayong Zu and Kawalin Angkananon and Yoksamon Jeaheng},
  doi       = {10.14569/IJACSA.2026.0170668},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170668}
}