AGENTIC AI ADOPTION IN HOTEL REVENUE MANAGEMENT: AN EXTENDED META-UTAUT FRAMEWORK

The emergence of agentic artificial intelligence (AI), systems capable of autonomous reasoning, decision-making, and cross-functional execution, represents a paradigm shift from traditional predictive analytics to autonomous decisionmakers in hotel revenue management (RM). Drawing upon the Meta-Unified Theory of Acceptance and Use of Technology (Meta-UTAUT), this study investigates the factors influencing the adoption of agentic AI among revenue managers in the Egyptian hospitality industry. A quantitative research design was employed, collecting primary data from 213 hotel revenue management managers across major Egyptian tourism destinations, including Cairo, Alexandria, Hurghada, and Sharm ElSheikh. An extended Meta-UTAUT model was developed to examine the relationships between performance expectancy, effort expectancy, social influence, facilitating conditions, attitude toward use, and behavioural intention. Structural Equa tion Modelling (SEM) was utilised to test the formulated hypotheses and determine the strength and significance of these relationships. The results indicate that all nine direct hypotheses and four mediating hypotheses were supported, demonstrating that performance expectancy, effort expectancy, social influence, and facilitating conditions significantly influence both behavioural intention and attitude toward use, while attitude toward use plays a significant mediating role in the adoption process, demonstrating that performance expectancy, effort expectancy, and social influence significantly affect both behavioural intention and attitude toward use, while facilitating conditions positively impact attitude toward use. The results confirm that attitude toward use serves as a crucial mediating variable in the adoption process, pr oviding empirical validation for the extended theoretical framework in the context of autonomous AI systems.