Enterprise AI Analysis

Understanding AI Technostress and Employee Career Growth from a Socio-Technical Systems Perspective: A Dual-Path Model

The rapid advancement of Artificial Intelligence (AI) has profoundly transformed organizational systems, reshaping how employees interact with technology and adapt to changing work environments. However, the systemic mechanisms through which Al-induced technostress influences employee career growth remain insufficiently understood. Grounded in a socio-technical systems perspective, this study conceptualizes organizations as adaptive systems where technological, organizational, and human subsystems dynamically interact. We propose a dual-path framework that distinguishes between challenge-related technostressors (a resource-gain process) and hindrance-related technostressors (a resource-loss process), elucidating how AI-related pressures can simultaneously foster and hinder career development. Furthermore, employee resilience and organizational AI support are incorporated as systemic moderators that modulate the intensity of these effects within the human-AI–organization system. Using two-stage survey data from 326 matched pairs of employees and supervisors, results largely support the proposed model, with some pathways showing marginal significance. The findings reveal that AI challenge-related technostressors stimulate proactive adaptation and skill development, whereas hindrance-related technostressors generate anxiety and insecurity, thereby impeding growth. This research extends systems theory by demonstrating how technostressors function as an emergent property of human-technology interactions and provides actionable insights for designing more adaptive and resilient socio-technical work systems.