GenAI in hotel operations

ChatGPT adoption among front-office employees

Bibliographic Data

ID21699890
AuthorsM Omar Parvez (0000-0003-4533-631X, Florida State University), Kayode Kolawole Eluwole (0000-0001-9993-3449, Masaryk University, corresponding author), Taiwo Temitope Lasisi (0000-0003-1912-5391, University of Hradec Králové), Woo Gon Kim (0000-0002-5898-7528, Florida State University)
Year2025
Pages1-15
Publication date2025-05-10
Open AccessNo
TypeARTICLE
VenueCurrent Issues in Tourism (JOURNAL)
Journal identifiersISSN: 1368-3500 • E-ISSN: 1747-7603
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/13683500.2025.2498598
OpenAlexW4410259196
LanguageEN
Citations received5
References cited41

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

Advertising · Business · Front office · Geography · Operations management · Tourism · AI in Service Interactions · Engineering · FinTech, Crowdfunding, Digital Finance · Technology Adoption and User Behaviour · Marketing

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