Tips and trips: a structural model of guests’ intentions to stay and tip for AI-based services in hotels

Abstract

Purpose-Given the rapid development in artificial intelligence (AI), the hotel industry is deploying AI-based systems. In line with this important development, this study aims to examine the impact of trust in the hotel and AI-related performance ambiguity on consumers’ engagement with AI-based systems. This study ultimately examined the impact of engagement on consumers’ intentions to stay in hotels offering such systems, and intentions to tip. Design/methodology/approach-This study developed a conceptual model based on the social cognition theory. The study used an online survey methodology and collected data from a nationwide sample of 400 hotel consumers from the USA. The data analysis was conducted with structural equation modeling. Findings-Consumers’ engagement is strongly influenced by their trust in the hotel but not by performance ambiguity associated with AI. In turn, engagement strongly influenced consumers’ intentions to stay in hotels that have such systems and their intentions to tip. Originality/value-As AI systems capable of making decisions for consumers are becoming increasingly present in hotels, little is known about the way consumers engage with such systems and whether their engagement leads to economic impact. This is the first study that validated a model that explains intentions to stay and tip for services facilitated by autonomous AI-based systems that can make decisions for consumers.