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

Mario Passalacqua (), Robert Pellerin, Florian Magnani (), Laurent Joblot (), Frédéric Rosin (), Esma Yahia () and Pierre-Majorique Léger
Additional contact information
Mario Passalacqua: UQAM - Université du Québec à Montréal = University of Québec in Montréal
Robert Pellerin: MAGI - Département de Mathématiques et de Génie Industriel - EPM - École Polytechnique de Montréal
Florian Magnani: MAGELLAN - Laboratoire de Recherche Magellan - UJML - Université Jean Moulin - Lyon 3 - Université de Lyon - Institut d'Administration des Entreprises (IAE) - Lyon
Laurent Joblot: LISPEN - Laboratoire d’Ingénierie des Systèmes Physiques et Numériques - Arts et Métiers Sciences et Technologies
Frédéric Rosin: LISPEN - Laboratoire d’Ingénierie des Systèmes Physiques et Numériques - Arts et Métiers Sciences et Technologies
Esma Yahia: LISPEN - Laboratoire d’Ingénierie des Systèmes Physiques et Numériques - Arts et Métiers Sciences et Technologies
Pierre-Majorique Léger: HEC Montréal - HEC Montréal

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Abstract: 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.

Keywords: Human-centred AI; Motivation; Engagement; Psychosocial; Industry 4.0; Industry 5.0; Human-centered AI (search for similar items in EconPapers)
Date: 2025-11
Note: View the original document on HAL open archive server: https://hal.science/hal-05345071v1
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Published in International Journal of Human-Computer Studies, 2025, 205, pp.103649. ⟨10.1016/j.ijhcs.2025.103649⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05345071

DOI: 10.1016/j.ijhcs.2025.103649

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