Building on the social support theory and the job demands-resources (JD-R) model, the current research explores the role of coworker task support on the perceived uncertainty, job stress, and emotional exhaustion of hospitality employees affected by the COVID-19 crisis. Moreover, this research investigates the moderating impact of supervisor support and family support on the relationship between perceived uncertainty and emotional exhaustion. The data were collected from 353 hospitality employees currently working in the hospitality industry in Pakistan. Partial least squares structural equation modeling (PLS-SEM) was employed using SmartPLS 3.3.3 software to examine the proposed hypotheses and to analyze the research model. The results point out that coworker task support has no significant relationship with emotional exhaustion. Furthermore, perceived uncertainty and job stress fully mediated the association between coworker task support and emotional exhaustion. Additionally, supervisor support and family support significantly moderated the association between perceived uncertainty and emotional exhaustion. This research contributes to the literature by expanding our knowledge of the role of social support in alleviating the perceived uncertainty, job stress, and emotional exhaustion of hospitality employees during the COVID-19 crisis. The theoretical and practical implications of the study are further discussed.
The aim of this systematic literature review is to analyze the existing literature on the impact of artificial intelligence (AI) on employee work outcomes in the hospitality industry context. This paper systematically reviews the association between AI and employee work outcomes through an extensive literature review of published peer-reviewed English articles. Eighteen articles have been found in 12 journals and analyzed through deductive approach. The findings were synthesized into three major themes: enablers or inhibitors of AI adoption, the type of AI-related technique, outcomes of AI adoption. Well-being, turnover intention, and job engagement were identified as the most significant and most commonly studied outcomes of AI adoption.
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