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 - Amirah, Azizah Amirah; Herachwati, Nuri; Fiona Niska Dinda Nadia (Amkop Management Accounting Review (AMAR), 2025)
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.