An empirical study on the dark side of service employees' AI awareness: Behavioral responses, emotional mechanisms, and mitigating factors

• Main Authors: Zhou, Shuai, Yi, Ni, Rasiah, Rajah, Zhao, Haipeng, Mo, Zile
• Format: Article
• Published: Elsevier 2024
• Subjects: HD28 Management. Industrial Management
• Online Access: http://eprints.um.edu.my/45294/; https://doi.org/10.1016/j.jretconser.2024.103869
• Institution: Universiti Malaya

Drawing on the stressor-emotion model, our study unveils a multi-faceted moderated mediation model that delineates how artificial intelligence (AI) awareness influences frontline service employees' counterproductive work behavior towards customers (CWBC) and work-family conflict (WFC), mediated by negative emotions (NE). We introduce promotion focus and empowering leadership as first-stage moderators, and family motivation as a second-stage moderator, to explore their buffering effects on the negative outcomes triggered by AI awareness. Employing an experience sampling methodology, we gathered data from 92 frontline service employees in hospitality over two working weeks. The findings indicate that heightened AI awareness correlates with increased emotional distress, which in turn exacerbates WFC and CWBC. Notably, our analysis reveals that employees with a pronounced promotion focus and those under empowering leadership regimes exhibit reduced negative emotional responses to AI-induced stressors. Moreover, individuals driven by strong family motivation not only demonstrate unique resilience in managing the interplay between work-induced stress and family wellbeing but also show a significant reduction in counterproductive work behaviors. Overall, this research provides a novel lens to understand the broader implications of AI in the service industry by employing a dynamic and multilevel approach.