EXPLAINING TOURISM AND HOSPITALITY STUDENTS' ADOPTION OF LARGE LANGUAGE MODELS IN HIGHER EDUCATION: AN INTEGRATED TAM–UTAUT FRAMEWORK USING PLS-SEM AND NECESSARY CONDITION ANALYSIS

Abstract

The rapid integration of large language models (LLMs) in higher education has transformed students' learning practices, particularly in applied disciplines such as tourism and hospitality education. Yet, limited empirical research explains the factors driving their adoption. Drawing on the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), this study examines tourism and hospitality students' behavioural intention to use LLM-based learning tools by incorporating content reliability, learner motivation, and social influence as extended antecedents. Data were collected from 365 university students enrolled in tourism, hospitality and management courses in addition to the students enrolled in other allied programs having tourism as an elective course in India and analysed using partial least squares structural equation modelling (PLS-SEM) and Necessary Condition Analysis (NCA). The findings indicate that perceived usefulness remains central to adoption, while learner motivation and social influence play critical enabling roles. NCA further reveals that perceived usefulness, learner motivation, and social influence constitute necessary conditions for achieving high adoption intention. By integrating net-effect and necessity-based approaches, the study advances technology acceptance theory in AI-enabled education in tourism and hospitality. It offers practical insights for the responsible integration of LLMs in professional learning contexts.