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Abstract

The rapid adoption of artificial intelligence (AI) is reshaping organizational structures and employment landscapes, raising concerns about job insecurity and employee well-being. This study examines the effect of AI-induced job insecurity on burnout among frontline employees in Indonesia, drawing on the Job Demands–Resources (JD-R) framework. Specifically, it investigates job stress and meaningfulness of work as mediating mechanisms and self-efficacy in AI learning as a moderating resource. A cross-sectional survey was conducted with 325 frontline employees across sectors where AI adoption is increasing, and data were analyzed using Structural Equation Modeling in Jamovi. The results indicate that AI-induced job insecurity significantly increases employee burnout, both directly and indirectly. Job stress was confirmed as a positive mediator, while meaningfulness of work functioned as a negative mediator, highlighting the dual role of demands and resources in shaping burnout. Furthermore, self-efficacy in AI learning moderated the insecurity–burnout relationship, such that employees with higher efficacy were less adversely affected. These findings extend the JD-R model by integrating AI-related job insecurity and demonstrating the dual processes of stress elevation and resource erosion. The study offers practical implications for organizations to prioritize transparent communication, reskilling initiatives, and meaning-enhancing practices to safeguard employee resilience in the era of digital transformation.

Keywords

AI-induced job insecurity Job Demands–Resources model Job Stress Burnout Frontline employees.

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Amirah, A. A., Herachwati, N. ., & Fiona Niska Dinda Nadia. (2025). From Ai-Induced Job Insecurity to Burnout: A Job-Demands Resource Model of Job Stress, Meaningfulness of Work, And Self-Efficacy in Ai Learning Among Front-Line Employees in Indonesia. Amkop Management Accounting Review (AMAR), 5(2), 547–566. https://doi.org/10.37531/amar.v5i2.3273
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