The impact of AI-enabled service attributes on service hospitableness: the role of employee physical and psychological workload

Purpose Hospitableness lies in the center of hospitality services. With the infusion of artificial intelligence (AI) technology in the hospitality industry, managers are concerned about how AI influences service hospitableness. Previous research has examined the consequences of AI technology based on customers’ assessment while ignoring the key players in service hospitableness – frontline employees (FLEs). This study aims to reveal how AI technology empowers FLEs physically, mentally and emotionally, facilitating hospitableness provision. Design/methodology/approach As the starting point, the instrument for AI-enabled service attributes was designed based on previous literature, hotel FLE interviews, expert panel and a pilot survey, and then validated using survey data. After that, a paired supervisor-employee sample was recruited in 15 hotels, and 342 valid questionnaires covering the constructs were obtained. Findings Factor analyses and measurement model evaluation suggest that the four factors, including anthropomorphic, entertainment, functional and information attributes, explain the construct of AI-enabled service attributes well, with high reliability and validity. Additionally, anthropomorphic, functional and information attributes of AI technology have been found to enable FLEs physically, mentally and emotionally, which further lead to increased service hospitableness. The entertainment attributes do not significantly reduce physical and mental fatigue but lead to positive emotions of FLEs significantly. Additionally, psychological job demand moderates the effects of AI-enabled service attributes on physical fatigue. Practical implications Practical implications can be made for AI technology application and hospitableness provision, in terms of AI technology analysis, job design and employee workload management. Originality/value This research contributes to understanding AI-enabled service attributes and their consequences, extends the conservation of resources theory to AI application context and promotes the research on service hospitableness.