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Human-centred AI in industry 5.0: a systematic review

Author

Listed:
  • Mario Passalacqua

    (MAGI - Département de Mathématiques et de Génie Industriel - EPM - École Polytechnique de Montréal, UQÀM - Département de Psychologie - 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

    (Laboratoire de Recherche Magellan - UJML - Université Jean Moulin - Lyon 3 - Université de Lyon - Institut d'Administration des Entreprises (IAE) - Lyon, Institut d'Administration des Entreprises (IAE) - Lyon, UJML - Université Jean Moulin - Lyon 3 - Université de Lyon)

  • Philippe Doyon-Poulin

    (MAGI - Département de Mathématiques et de Génie Industriel - EPM - École Polytechnique de Montréal)

  • Laurène Del-Aguila

    (HEC Montréal - HEC Montréal)

  • Jared Boasen

    (HEC Montréal - HEC Montréal)

  • Pierre-Majorique Léger

    (HEC Montréal - HEC Montréal)

Abstract

Industry 4.0 (I4.0) is a manufacturing paradigm revolutionising production by integrating advanced technologies, like AI, for automation and data integration. However, research in I4.0 overlooks human factors, crucial for designing systems that enhance well-being, trust, motivation, and performance. To address this, international bodies have introduced Industry 5.0, aiming to balance technological advancement with human welfare. To transition towards this vision, an understanding of current human-technology interaction is essential. Through a conceptual model aiming to understand the psychological experience of workers within their environment, we identified the studied human factors, their antecedents, consequences, and methodologies. Additionally, we explored how future research can adopt a human-centred approach in designing and implementing technology. Analysis of 67 articles showed the psychosocial dimension of human factors like AI trust, worker autonomy, motivation, and stress are underrepresented. We observed a significant disconnect between empirical and non-empirical studies in terms of theoretical frameworks, variable selection, data collection methods, and research designs. Our findings highlight the necessity for experimental, theory-driven research in human-AI interaction, using a multi-method approach including perceptual, observational, and psychophysiological measures. Lastly, we discuss the integration of these findings into managerial practice to foster workplaces that are technologically advanced yet remain empathetic to human needs.

Suggested Citation

  • Mario Passalacqua & Robert Pellerin & Florian Magnani & Philippe Doyon-Poulin & Laurène Del-Aguila & Jared Boasen & Pierre-Majorique Léger, 2024. "Human-centred AI in industry 5.0: a systematic review," Post-Print hal-04723054, HAL.
    • Handle: RePEc:hal:journl:hal-04723054
    DOI: 10.1080/00207543.2024.2406021
    Note: View the original document on HAL open archive server: https://hal.science/hal-04723054v1
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    References listed on IDEAS

    as
    1. Andreas Fügener & Jörn Grahl & Alok Gupta & Wolfgang Ketter, 2022. "Cognitive Challenges in Human–Artificial Intelligence Collaboration: Investigating the Path Toward Productive Delegation," Information Systems Research, INFORMS, vol. 33(2), pages 678-696, June.
    2. Thomas De Lombaert & Kris Braekers & René De Koster & Katrien Ramaekers, 2023. "In pursuit of humanised order picking planning: methodological review, literature classification and input from practice," International Journal of Production Research, Taylor & Francis Journals, vol. 61(10), pages 3300-3330, May.
    3. Etieno Enang & Mahdi Bashiri & David Jarvis, 2023. "Exploring the transition from techno centric industry 4.0 towards value centric industry 5.0: a systematic literature review," International Journal of Production Research, Taylor & Francis Journals, vol. 61(22), pages 7866-7902, November.
    4. Vivek Vijayakumar & Fabio Sgarbossa & W. Patrick Neumann & Ahmad Sobhani, 2022. "Framework for incorporating human factors into production and logistics systems," International Journal of Production Research, Taylor & Francis Journals, vol. 60(2), pages 402-419, January.
    5. Grosse, E. H. & Glock, C. H. & Jaber, M. Y. & Neumann, W. P., 2015. "Incorporating human factors in order picking planning models: framework and research opportunities," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 65237, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    6. Eric H. Grosse & Christoph H. Glock & Mohamad Y. Jaber & W. Patrick Neumann, 2015. "Incorporating human factors in order picking planning models: framework and research opportunities," International Journal of Production Research, Taylor & Francis Journals, vol. 53(3), pages 695-717, February.
    7. Monica Molino & Claudio G. Cortese & Chiara Ghislieri, 2021. "Technology Acceptance and Leadership 4.0: A Quali-Quantitative Study," IJERPH, MDPI, vol. 18(20), pages 1-18, October.
    8. Dmitry Ivanov, 2023. "The Industry 5.0 framework: viability-based integration of the resilience, sustainability, and human-centricity perspectives," International Journal of Production Research, Taylor & Francis Journals, vol. 61(5), pages 1683-1695, March.
    9. Mario Passalacqua & Robert Pellerin & Esma Yahia & Florian Magnani & Frédéric Rosin & Laurent Joblot & Pierre-Majorique Léger, 2024. "Practice With Less AI Makes Perfect: Partially Automated AI During Training Leads to Better Worker Motivation, Engagement, and Skill Acquisition," Post-Print hal-04487695, HAL.
    10. Bas van Oudenhoven & Philippe Van de Calseyde & Rob Basten & Evangelia Demerouti, 2023. "Predictive maintenance for industry 5.0: behavioural inquiries from a work system perspective," International Journal of Production Research, Taylor & Francis Journals, vol. 61(22), pages 7846-7865, November.
    11. Winkelhaus, Sven & Grosse, Eric H. & Glock, Christoph H., 2022. "Job satisfaction: An explorative study on work characteristics changes of employees in Intralogistics 4.0," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 134375, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    12. Winkelhaus, S. & Grosse, E. H. & Glock, C. H., 2022. "Job satisfaction: An explorative study on work characteristics changes of employees in Intralogistics 4.0," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 133921, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
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