AlGC techno-overload and hospitality employees’ service outcomes: a conservation of resources perspective

Purpose Artificial intelligence generated content (AIGC) has shown significant potential in the hospitality workplace. Drawing from conservation of resources theory, this study aims to examine the impact of AIGC techno-overload on hospitality employees’ proactive customer service performance (PCSP) and service sabotage via the mediating role of ego depletion and the moderating role of self-control. Design/methodology/approach Study 1 used experience sampling method (ESM) with 117 frontline hospitality employees’ 708 daily reports over 10 workdays. Study 2 used a scenario experiment with 187 frontline hospitality employees. Findings AIGC techno-overload negatively affects PCSP and positively impacts service sabotage, mediated by ego depletion. In addition, both trait and state self-control mitigated the indirect effect of AIGC techno-overload on PCSP and service sabotage via ego depletion. Practical implications Managers can mitigate the negative effects of AIGC techno-overload by providing training on time management and stress reduction. Fostering a work environment that supports high self-control can help employees manage technological demands, enhancing well-being and proactive service behavior while reducing service sabotage. Originality/value The hospitality literature lacks an understanding of the negative effect of AIGC on frontline employees’ job outcomes. This research enriches AIGC literature by providing unique insights of AIGC techno-overload’s effect on service behaviors. Furthermore, this research reveals the mediating role of ego depletion and the moderating effect of self-control.

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Publication Details

Journal
International Journal of Contemporary Hospitality Management
Published
2025-11-22
DOI
https://doi.org/10.1108/ijchm-02-2025-0196
Citations
4
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
9.92
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article

AlGC techno-overload and hospitality employees’ service outcomes: a conservation of resources perspective

Wei Wei, Keyu Chen, Yaojun Deng
4 citations
International Journal of Contemporary Hospitality Management
AI in Service Interactions
9.92
article

AlGC techno-overload and hospitality employees’ service outcomes: a conservation of resources perspective

Wei Wei, Keyu Chen, Yaojun Deng
article en
4 citations

Abstract

Purpose Artificial intelligence generated content (AIGC) has shown significant potential in the hospitality workplace. Drawing from conservation of resources theory, this study aims to examine the impact of AIGC techno-overload on hospitality employees’ proactive customer service performance (PCSP) and service sabotage via the mediating role of ego depletion and the moderating role of self-control. Design/methodology/approach Study 1 used experience sampling method (ESM) with 117 frontline hospitality employees’ 708 daily reports over 10 workdays. Study 2 used a scenario experiment with 187 frontline hospitality employees. Findings AIGC techno-overload negatively affects PCSP and positively impacts service sabotage, mediated by ego depletion. In addition, both trait and state self-control mitigated the indirect effect of AIGC techno-overload on PCSP and service sabotage via ego depletion. Practical implications Managers can mitigate the negative effects of AIGC techno-overload by providing training on time management and stress reduction. Fostering a work environment that supports high self-control can help employees manage technological demands, enhancing well-being and proactive service behavior while reducing service sabotage. Originality/value The hospitality literature lacks an understanding of the negative effect of AIGC on frontline employees’ job outcomes. This research enriches AIGC literature by providing unique insights of AIGC techno-overload’s effect on service behaviors. Furthermore, this research reveals the mediating role of ego depletion and the moderating effect of self-control.

International Journal of Contemporary Hospitality ManagementVol. 38(1)
Beijing Jiaotong University (CN)
Openalex Percentile: Top 2%
AI in Service Interactions
9.92
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