This study develops and validates a multilevel framework explaining how contextual, causal, intervening, and strategic conditions shape AI adoption in hospitality and tourism. Integrating the Unified Theory of Acceptance and Use of Technology (UTAUT), the Diffusion of Innovation (DOI), and the Resource-Based View (RBV), it examines how consumer acceptance relates to organizational outcomes such as sustainability and operational efficiency.
A sequential mixed-methods design (Qual → Quan) was employed. Eight expert interviews, analyzed using grounded-theory coding, identified key AI-adoption conditions, which informed a survey of 499 AI-aware consumers.
Contextual and causal conditions positively influence AI adoption, while strategic actions – such as training, empowerment, and data analytics – further strengthen adoption. Intervening factors, including cybersecurity concerns and financial barriers, also show positive associations, indicating that consumers perceive these risks as manageable trade-offs. AI adoption significantly enhances organizational outcomes, including technology management, sustainability, and cost efficiency.
The study integrates UTAUT, DOI, and RBV into a holistic framework linking micro-level acceptance drivers to macro-level strategic outcomes, extending traditional technology acceptance models toward a process-oriented understanding of AI adoption in hospitality and tourism.
Managers are advised to enhance personalization, simplify user experiences, invest in employee training, strengthen cybersecurity, and leverage data analytics to improve performance, reduce costs, and support sustainable operations.
This study is among the first to empirically integrate UTAUT, DOI, and RBV into a unified AI-adoption framework in hospitality and tourism, demonstrating how adoption extends beyond consumer intentions to generate strategic organizational outcomes.
