Unveiling key drivers for social robot adoption in the hospitality sector: Two-phase confirmatory factor analysis and structural equation modeling approach

Abstract

This study explores the critical factors influencing the intention to adopt social robots in the hospitality sector, addressing a significant gap in the existing literature. While technology adoption has been extensively studied across various industries, specific insights regarding social robots in hospitality remain limited. Recognizing the unique attributes of the hospitality environment - such as guest interactions and service quality. This data set aims to provide an empirical understanding of how these factors impact the willingness to integrate robotic technologies. Data was collected from employees of five-star hotels through a well-structured questionnaire. The research employs a rigorous methodology, including exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), to robustly evaluate the proposed hypotheses related to robotic adoption. The findings of this study reveal several key drivers that motivate the adoption of social robots, including perceived benefits, ease of use, and organizational readiness. These insights are invaluable for industry practitioners, as they highlight critical areas that must be addressed to facilitate smoother integration of robotic technologies. Hence, by reducing the identified barriers and enhancing the adoption process, this research ultimately seeks to improve service quality and operational efficiency in the hospitality sector, paving the way for future innovations. This work not only contributes to the academic discourse on technology adoption but also serves as a practical guide for hospitality managers aiming to harness the potential of social robotics to enhance customer experiences.