AI-induced job insecurity and employee burnout: The moderating role of AI self-efficacy in emerging market manufacturing

Background: The rapid integration of Artificial Intelligence (AI) in manufacturing is reshaping operational processes while simultaneously altering employee experiences, particularly in emerging economies where digital preparedness and labor protections remain limited. Although the economic benefits of AI adoption have been widely examined, its psychological implications for employees remain relatively underexplored. Purpose: This study investigates how AI-induced job insecurity influences employee burnout, examining the mediating role of psychological safety and the moderating role of AI self-efficacy. Research Design: A quantitative, cross-sectional survey design was employed, drawing on a multi-theoretical framework that integrates Conservation of Resources (COR) Theory, the Job Demands-Resources (JD-R) Model, Social Cognitive Theory (SCT), and Uncertainty Management Theory (UMT). Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to test the proposed relationships. Study Sample: Survey data were collected from 208 employees working in manufacturing firms undergoing AI integration in Pakistan. Data Collection and/or Analysis: A structured self-administered questionnaire was distributed across AI-integrating manufacturing firms. PLS-SEM was employed to evaluate measurement and structural model relationships, including mediation and moderation effects. Results: The findings indicate that job insecurity significantly increases employee burnout while simultaneously undermining psychological safety. Psychological safety mediates the relationship between job insecurity and burnout, suggesting that insecurity contributes to burnout partly by eroding employees' sense of interpersonal safety at work. In addition, AI self-efficacy moderates this relationship by buffering the negative impact of job insecurity on psychological safety. Conclusions: These results extend existing research on employee burnout by integrating both contextual and individual-level factors associated with digital transformation. From a practical perspective, the findings underscore the importance of strengthening AI-related competencies, fostering psychologically safe work environments, and promoting transparent communication during technological change in order to reduce burnout risks in resource-constrained manufacturing settings.