Publication details
GenAI in hotel operations: ChatGPT adoption among front-office employees
| Authors | |
|---|---|
| Year of publication | 2026 |
| Type | Peer-reviewed scientific article |
| Magazine / Source | Current Issues in Tourism |
| MU Faculty or unit | |
| Citation | |
| web | https://www.tandfonline.com/doi/full/10.1080/13683500.2025.2498598 |
| Doi | https://doi.org/10.1080/13683500.2025.2498598 |
| Keywords | Generative AI; knowledge application; ChatGPT adoption vs. rejection; front office employees; hotel operations |
| Attached files | |
| Description | This study examines the adoption of ChatGPT and other generative artificial intelligence (GenAI) technologies by hotel front-office employees to support task performance. Data were collected from multiple chain hotels across the United States, with responses from 425 front-office employees analysed using structural equation modelling (SEM). A purposive sampling approach was employed, and data collection was conducted via the Qualtrics platform, with analysis performed using SmartPLS Ver. 3.0. The findings reveal that key adoption factors, including effort expectancy and facilitating conditions, significantly influence employees’ intentions to integrate ChatGPT into their workflow. The results underscore the role of GenAI in enhancing information accessibility and operational efficiency within the front-office environment. From a practical perspective, the study offers insights into evolving knowledge requirements, the necessity for continuous technological adaptation, and the importance of ensuring accuracy when utilising ChatGPT. Furthermore, the study provides critical policy implications, emphasising the need for updated valuation frameworks, GenAI competency development, and revised professional standards in the hospitality sector. As one of the first empirical investigations into the application of GenAI in hotel front-office operations, this research advances the theoretical understanding of AI-driven workplace transformation while offering actionable recommendations to optimise employee performance and customer service outcomes. |