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<p>Artificial intelligence (AI) can play a vital role in facilitating supply chain sustainability. However, the presence of challenges restricts manufacturing firms from fully embracing AI-enabled digital transformation for supply chain sustainability and the extent to which supply chain learning strategies mitigate these and enhance AI adoption is still unclear. Therefore, this study proposes a modelling approach to determine the significance of the challenges to AI adoption for supply chain sustainability and obtain the impact of supply chain learning strategies on mitigating these challenges and enhancing AI adoption. Firstly, the challenges to AI adoption for supply chain sustainability of food firms were identified from a literature review, finalized by experts’ inputs and classified into technological, organizational, human and institutional contexts. As well, five supply chain learning strategies were conceptualized from a literature review and experts’ inputs. Then, aided by data from Nigerian food manufacturing firms, a modeling approach that integrates interval-valued neutrosophic analytical hierarchy process (IVN-AHP), criteria importance through inter-criteria correlation (CRITIC) and combined compromise solution (CoCoSo) was applied to obtain the significance of the challenges to AI adoption for supply chain sustainability and the impact of supply chain learning strategies on mitigating these challenges and enhancing AI adoption. Study results highlight that the ‘human challenges’ are the most significant, followed by the ‘technological challenges’ while ‘transformational leadership’ is the supply chain learning strategy that is most strongly associated with mitigating the AI adoption challenges and fostering AI adoption. Moreover, ‘transformational leadership’ impacts the most on human challenges, while ‘supply chain governance’ impacts the most on technological and institutional challenges. Overall, this study builds on the agenda to enhance AI adoption in food manufacturing firms by relying on the supply chain learning strategies to mitigate the existing challenges and, so, enrich understanding on AI-enabled digitalization for supply chain sustainability.</p>

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The effect of supply chain learning in mitigating the challenges to adopting artificial intelligence for supply chain sustainability

  • 期刊论文

  • 发表日期：

    2026年2月23日

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  • Ifeyinwa Juliet Orji,
  • Chukwuebuka Martinjoe U-Dominic

摘要

Artificial intelligence (AI) can play a vital role in facilitating supply chain sustainability. However, the presence of challenges restricts manufacturing firms from fully embracing AI-enabled digital transformation for supply chain sustainability and the extent to which supply chain learning strategies mitigate these and enhance AI adoption is still unclear. Therefore, this study proposes a modelling approach to determine the significance of the challenges to AI adoption for supply chain sustainability and obtain the impact of supply chain learning strategies on mitigating these challenges and enhancing AI adoption. Firstly, the challenges to AI adoption for supply chain sustainability of food firms were identified from a literature review, finalized by experts’ inputs and classified into technological, organizational, human and institutional contexts. As well, five supply chain learning strategies were conceptualized from a literature review and experts’ inputs. Then, aided by data from Nigerian food manufacturing firms, a modeling approach that integrates interval-valued neutrosophic analytical hierarchy process (IVN-AHP), criteria importance through inter-criteria correlation (CRITIC) and combined compromise solution (CoCoSo) was applied to obtain the significance of the challenges to AI adoption for supply chain sustainability and the impact of supply chain learning strategies on mitigating these challenges and enhancing AI adoption. Study results highlight that the ‘human challenges’ are the most significant, followed by the ‘technological challenges’ while ‘transformational leadership’ is the supply chain learning strategy that is most strongly associated with mitigating the AI adoption challenges and fostering AI adoption. Moreover, ‘transformational leadership’ impacts the most on human challenges, while ‘supply chain governance’ impacts the most on technological and institutional challenges. Overall, this study builds on the agenda to enhance AI adoption in food manufacturing firms by relying on the supply chain learning strategies to mitigate the existing challenges and, so, enrich understanding on AI-enabled digitalization for supply chain sustainability.