Determinants of Users' Intentions to Use AI-Enabled Technological Innovations in Hotels: A Hybrid Approach Using PLS-SEM and fsQCA

Abstract

This study investigates the factors influencing hotel guests' intentions to adopt next-generation technologies enabled by artificial intelligence (AI). Both affective and cognitive processes, which led to guests' intentions to adopt these new technologies, were considered to have antecedents in the form of intrinsic and extrinsic motives, respectively. The data collected from 331 respondents were analyzed using a combination of methods, including the asymmetrical fuzzy set qualitative comparative analysis (fsQCA) and the symmetrical partial least square-structural equation modeling (PLS-SEM). The results of the symmetrical study indicated that novelty and compatibility have a good impact on both enjoyment and usefulness, which ultimately lead to behavioral intentions. In contrast, asymmetrical studies have shown that all the criteria are necessary conditions to produce users' intention to embrace AI-based technology. By integrating IDT and TAM, this study extends the comprehension of factors driving customers to use AI-enabled technologies during their hotel stays. This study also adds to the existing literature by exploring configurational modeling with fsQCA, as opposed to prior studies that have relied on net impact modeling via SEM.