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

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.

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Publication Details

Journal
Annals of Operations Research
Published
2025-02-28
DOI
https://doi.org/10.1007/s10479-025-06534-7
Citations
22
Primary Topic
Supply Chain Resilience and Risk Management
Type
article
Field-Weighted Citation Impact
48.98
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article

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

Emilia Vann Yaroson, Mélanie Roux, Amélie Abadie
22 citations
Annals of Operations Research
Supply Chain Resilience and Risk Management
48.98
article

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

Emilia Vann Yaroson, Mélanie Roux, Amélie Abadie
article en
22 citations

Abstract

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.

Annals of Operations ResearchVol. 354(1)
TBS Education (FR), University of Sheffield (GB), University of Hassan II Casablanca (MA)
Openalex Percentile: Top 0%
Supply Chain Resilience and Risk Management
48.98
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