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<p>Generative artificial intelligence (GenAI) presents transformative potential for optimising supply chain and logistics operations by enhancing efficiency, supporting sustainability initiatives, and improving stakeholder experiences. Despite these promising prospects, multifaceted and interdependent barriers constrain the integration of GenAI into sustainable supply chains, necessitating a systematic and strategic evaluation. Anchored in the technology-organisation-environment (TOE) framework and Diffusion of Innovation (DOI) theory, this study extends the analytical scope through the incorporation of the supply chain and external contexts. A dual-method approach is employed, leveraging the SAP-LAP (situation–actor–process–learning–action–performance) framework to capture the flexible nature of technological adoption, alongside the Bayesian best–worst method (BWM) to ensure robustness in the prioritisation of critical barriers. Empirical findings highlight that environmental external context (0.2702) emerges as the most significant barrier, followed closely by technological context (0.2689), organisational context (0.2572), and supply chain context (0.2035). These results underscore persistent regulatory ambiguities, limited technological readiness, limited organisational capacities, and inherent complexities within supply chain networks as principal impediments to GenAI adoption. This research offers actionable policy recommendations, including the formulation of clear regulatory frameworks that balance innovation with sustainability compliance and strategic investments in digital infrastructure and literacy.</p>

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Integrating SAP-LAP with a Bayesian Best–Worst Method to Assess Generative AI Adoption in Sustainable Supply Chains

  • 期刊论文

  • 发表日期：

    2026年3月17日

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  • Sourabh Kumar,
  • Aalok Kumar,
  • Shamita Garg

摘要

Generative artificial intelligence (GenAI) presents transformative potential for optimising supply chain and logistics operations by enhancing efficiency, supporting sustainability initiatives, and improving stakeholder experiences. Despite these promising prospects, multifaceted and interdependent barriers constrain the integration of GenAI into sustainable supply chains, necessitating a systematic and strategic evaluation. Anchored in the technology-organisation-environment (TOE) framework and Diffusion of Innovation (DOI) theory, this study extends the analytical scope through the incorporation of the supply chain and external contexts. A dual-method approach is employed, leveraging the SAP-LAP (situation–actor–process–learning–action–performance) framework to capture the flexible nature of technological adoption, alongside the Bayesian best–worst method (BWM) to ensure robustness in the prioritisation of critical barriers. Empirical findings highlight that environmental external context (0.2702) emerges as the most significant barrier, followed closely by technological context (0.2689), organisational context (0.2572), and supply chain context (0.2035). These results underscore persistent regulatory ambiguities, limited technological readiness, limited organisational capacities, and inherent complexities within supply chain networks as principal impediments to GenAI adoption. This research offers actionable policy recommendations, including the formulation of clear regulatory frameworks that balance innovation with sustainability compliance and strategic investments in digital infrastructure and literacy.