A Mixed-methods Multi-study Approach to Unveil the Implications of AI Integration for Employee Outcomes in the US Hotel Industry

Abstract

Artificial intelligence (AI) has emerged as a pivotal technology reshaping workplace dynamics and influencing employee behavior in the hotel industry. While existing research has largely emphasized operational efficiency and technological outcomes, a broader understanding of how employees perceive, experience, and respond to AI adoption remains limited. Addressing this gap, this dissertation employs a mixed-methods approach across three interrelated studies to examine the implications of AI for employee attitudes, behaviors, and managerial practices in the U.S. hotel industry. Drawing on the Transactional Theory of Stress and Coping, the Job Demands–Resources (JD–R) model, and Conservation of Resources (COR) theory, this research integrates quantitative and qualitative insights to uncover the psychological and organizational mechanisms through which AI shapes employee outcomes. Survey data collected from 413 hotel employees in the US reveal that different approaches to AI implementation produce divergent employee responses. Specifically, AI adoption is associated with lower levels of quiet quitting, whereas AI integration is linked to higher levels of withdrawal behaviors. Competitive psychological climate emerges as a key mediating mechanism, whereas technostress does not explain these relationships in this context. Complementing these findings, qualitative evidence from semi-structured interviews with 19 hotel managers demonstrates that AI adoption significantly influences employees’ career development, well-being, and work outcomes. Managers report shifts in employees’ career perceptions, including turnover intentions, job search behaviors, and career satisfaction, as well as both positive outcomes (e.g., improved efficiency) and negative outcomes (e.g., strain and techno-anxiety). Managerial support and transparent communication are identified as critical factors in mitigating adverse effects. Further quantitative analyses show that perceived AI job-replacement threat is positively associated with technostress and turnover intention, with technostress serving as a key mechanism linking AI-related threats to turnover intention and service sabotage. Collectively, this dissertation advances theoretical understanding of AI-enabled workplaces by clarifying how AI-related demands and resource threats influence employee withdrawal and deviant behaviors. The findings offer actionable insights for hospitality organizations seeking to implement AI in ways that support employee well-being and promote sustainable service performance.

Keywords: AI Threat, Hotel Employees, Competitive Psychological Climate, Technostress, Quiet Quitting, Service Sabotage