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Human-artificial intelligence collaboration in supply chain outcomes: the mediating role of responsible artificial intelligence

Author

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  • Emilia Vann Yaroson

    (University of Sheffield)

  • Amélie Abadie

    (Toulouse Business School)

  • Mélanie Roux

    (Toulouse Business School)

Abstract

Human-artificial intelligence collaboration (CAIT) presents considerable opportunities for optimising supply chain outcomes. Nonetheless, it poses numerous ethical, technological, and organisational obstacles that could impede its efficacy. This study contends that responsible AI (RAI) systems can function as a conduit between CAIT and supply chain outcomes to tackle these challenges. Accordingly, we leveraged the resource-based view (RBV) and socio-technical system (STS) theoretical lenses to analyse the mediating role of RAI in the relationship between CAIT and two supply chain outcomes (supply chain wellbeing (SCWB) and sustainable business performance (SBP)). The suggested model was evaluated using PLS-SEM on survey data from 301 supply chain managers in the UK. Our analysed data revealed a statistically insignificant relationship between CAIT and supply chain outcomes (SCWB and SBP). However, the mediating role of RAI was confirmed. The findings suggest that CAIT is merely a component of a supply chain's capacity to produce intrinsic resources, rather than a universal solution. To harness the dividends of human-AI collaboration involves designing boundaries, aligning CAIT to supply chain goals and integrating ethical and transparent strategies. Our findings contribute to the discourse on AI use in supply chain literature by showing that CAIT can influence supply chain outcomes by bridging ethical, operational and technological gaps while fostering trust and efficiency.

Suggested Citation

  • Emilia Vann Yaroson & Amélie Abadie & Mélanie Roux, 2025. "Human-artificial intelligence collaboration in supply chain outcomes: the mediating role of responsible artificial intelligence," Annals of Operations Research, Springer, vol. 354(1), pages 35-69, November.
    • Handle: RePEc:spr:annopr:v:354:y:2025:i:1:d:10.1007_s10479-025-06534-7
    DOI: 10.1007/s10479-025-06534-7
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