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

Abstract

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.

The effect of illegitimate tasks on hospitality employees’ service performance: a conservation of resources perspective

“…Given the expansion of role breadth in hospitality organizations as a result of staffing shortages, our work builds upon research that investigates how to deal with volume of work (e.g. Qiu et al , 2022) to also consider the scope of work performed by employees. In providing evidence that illegitimate tasks can have important effects on both in-role and extra-role performance in hospitality employees, we advance understanding of illegitimate tasks, an understudied phenomenon in organizations (Semmer et al , 2019), and, drawing on COR theory, we advance theoretical understanding of the types of role-based stressors that may impact hospitality employees’ in-role and extra-role performance.…”

A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice

“…For example, enhanced innovative behaviors were observed when AI was applied to support job autonomy, job complexity, specialization, and information processing (Verma & Singh, 2022). In a service work context, AI can enhance workers' resources by reducing mental and physical fatigue and increasing positive emotional states (Qiu et al, 2022). Such outcomes are also facilitated by AI developers effectively combining machine intelligence and human domain expertise, in conjunction with those human experts, to create a human–machine learning hybrid work practice (van den Broek et al, 2021).…”

Artificial intelligence research in hospitality: a state-of-the-art review and future directions

“…Other studies in this stream are on management optimization and antecedents to users' satisfaction with AI applications. For example, Qiu et al (2022) discovered that the anthropomorphic, functional and informational aspects of AI technologies reduced employees' physical and mental fatigue, thereby improving overall service quality. Jim enez- Barreto et al (2021) found that self-determined interaction (e.g.…”