Smart hospitality: What drives consumers to accept AI services?

Purpose This study uncovers the determinants influencing consumers' intention to use (IU) AI-enabled services within the hospitality industry grounded on the Unified Theory of Acceptance and Use of Technology (UTAUT). In addition to core constructs – performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC) – the model incorporates trust (TR), hedonic motivation (HM), anthropomorphism (ATMP) and perceived risk (PR). It further examines personal innovativeness in IT (PIT) as a moderator. Design/methodology/approach Data collection was done, employing cross-sectional design, via Google Form link using convenience and snowball sampling techniques. The data was analysed using “Partial Least Squares Structural Equation Modeling” (PLS-SEM) via SmartPLS 4.0. Findings The results reveal that PE, EE, SI, FC, TR, HM, and ATMP positively influence IU AI-enabled services, while PR has a negative effect. PIT significantly moderates the impact of PE and EE, with the model explaining 73.7% of the variance in usage intention. Research limitations/implications This study has substantial implications for hospitality businesses, AI developers, and policymakers. Understanding the factors driving AI usage allows stakeholders to design more effective implementation strategies and enhance consumer experience. Originality/value This study extends UTAUT by adding ATMP and HM and validates TR and PR in the usage of AI-enabled services within the Indian hospitality industry, a less explored area. It offers insights for designing user-friendly AI, promoting transparency, and addressing ethical and risk-related concerns in hospitality.