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Safeguarding worker psychosocial well-being in the age of AI: The critical role of decision control

Article dans une revue avec comité de lecture
Auteur
ccPASSALACQUA, Mario
360045 Université du Québec à Montréal = University of Québec in Montréal [UQAM]
ccPELLERIN, Robert
53681 Département de Mathématiques et de Génie Industriel [MAGI]
57241 École Polytechnique de Montréal [EPM]
ccMAGNANI, Florian
302032 Institut d'Administration des Entreprises (IAE) - Lyon
ccJOBLOT, Laurent
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
ccROSIN, Frédéric
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
ccYAHIA, Esma
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
ccLÉGER, Pierre-Majorique
304765 HEC Montréal [HEC Montréal]

URI
http://hdl.handle.net/10985/26948
DOI
10.1016/j.ijhcs.2025.103649
Date
2025-11
Journal
International Journal of Human-Computer Studies

Résumé

Advancements in artificial intelligence (AI) have ushered in the era of the fourth industrial revolution, transforming workplace dynamics with AI's enhanced decision-making capabilities. While AI has been shown to reduce worker mental workload, improve performance, and enhance physical safety, it also has the potential to negatively impact psychosocial factors, such as work meaningfulness, worker autonomy, and motivation, among others. These factors are crucial as they impact employee retention, well-being, and organizational performance. Yet, the impact of automating decision-making aspects of work on the psychosocial dimension of human-AI interaction remains largely unknown due to the lack of empirical evidence. To address this gap, our study conducted an experiment with 102 participants in a laboratory designed to replicate a manufacturing line. We manipulated the level of AI decision support—characterized by the AI's decision-making control—to observe its effects on worker psychosocial factors through a blend of perceptual, physiological, and observational measures. Our aim was to discern the differential impacts of fully versus partially automated AI decision support on workers' perceptions of job meaningfulness, autonomy, competence, motivation, engagement, and performance on an error-detection task. The results of this study suggest the presence of a critical boundary in automation for psychosocial factors, demonstrating that while some automation of decision selection can nurture work meaningfulness, worker autonomy, competence, self-determined motivation, and engagement, there is a pivotal point beyond which these benefits can decline. Thus, balancing AI assistance with human control is vital to protect psychosocial well‑being. Practically, industry and operations managers should keep employees involved in decision making by adopting partial, confirm‑or‑override AI systems that sustain motivation and engagement, boosting retention and productivity.

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