Artificial Intelligence, Job Insecurity and Employee Adaptation: A JD-R Model Perspective

Abstract

This study investigates the impact of artificial intelligence (AI) integration on employee job insecurity and its associated psychological and behavioral consequences. Grounded in the Job Demands–Resources model and resource protection theory, the research examines three dimensions of job insecurity—job holding, wage/promotion, and excessive competition insecurity—to understand how employees perceive threats emerging from AI-driven workplace transformations. Using data from 384 employees and employing exploratory factor analysis, reliability testing, correlations, and regression modelling, the study reveals a strong negative relationship between AI adaptation and job insecurity, indicating that employees who adapt more effectively to AI experience reduced insecurity. The findings further highlight tech-learning anxiety as a key mechanism through which AI-related uncertainty affects employee well-being and performance. Importantly, vocational learning ability and mindfulness emerge as significant buffering factors that mitigate these adverse effects. The study emphasizes the need for organizations to build supportive learning environments and develop interventions that strengthen employee resilience in AI-enabled work settings.