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

Engineering notes

Key topics: autonomous driving, large language model. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original 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.

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