Abstract
The increasing adoption of AI-enabled inspection robots in metro systems introduces autonomy, adaptive diagnostics, and algorithmic opacity, creating both opportunities and technostress challenges for inspection workers. This study investigates how configurations of challenge and hindrance technostressors shape their job engagement, burnout, and productivity in AI-enabled inspection workplaces. Drawing on 3 interviews with metro inspection workers in China, we employed fuzzy-set qualitative comparative analysis (fsQCA) to identify configurational pathways. The findings demonstrate asymmetric effects: challenge technostressors such as techno-mastery and techno-relatedness foster job engagement and productivity, whereas their absence amplifies the detrimental impact of hindrance technostressors, leading to job burnout and low engagement. Furthermore, the interdependent patterns of complementarity and contingency illustrate how AI-induced technostressors interact to either intensify or mitigate technostress outcomes. By theorizing the dual roles and interdependencies of technostressors in human-AI collaboration, this study extends the transactional model of technostress and informs the design of resilient AI-enabled workplaces.
Recommended Citation
Qi, Yimeng and Du, Rong, "Unraveling Technostress in Human-AI Collaboration: A
Configurational Study of Metro Inspection Systems" (2025). ACIS 2025 Proceedings. 259.
https://aisel.aisnet.org/acis2025/259