Generative AI for socially responsible and resilient supply chains: Mapping drivers, challenges, risks, practices, and performance measures

Abstract

This study explores the integration of generative artificial intelligence (GAI) in fostering socially responsible and resilient supply chains (SRRSCs), addressing drivers, challenges, risks, practices, and performance measures. Through PRISMA guidelines, and bibliometric analysis of 49 peer-reviewed studies, we map the intellectual landscape of GAI applications in socially responsible and resilient supply chain management. Key findings reveal that GAI enhances transparency, decision-making, and risk management by leveraging tools such as large language models (LLMs), enabling real-time analytics, scenario planning, and ethical compliance. However, challenges such as high costs, data quality issues, and ethical concerns, particularly for SMEs, impede adoption. Risks include over-reliance on AI, cybersecurity vulnerabilities, and potential misalignment with human values. This study identifies several practices as pivotal for sustainability and resilience. Performance measures, including supply chain transparency index and ESG scores, provide metrics to balance efficiency and social responsibility. The fragmented research landscape, marked by limited cross-citation, underscores the need for integrative frameworks. Policy recommendations advocate for adaptive regulations, public-private partnerships, and ethical AI standards to address disparities and foster equitable adoption. New research opportunities and propositions provide several avenues for researchers. This study offers a cohesive framework to guide researchers, practitioners and policymakers in leveraging GAI for SRRSCs.