The Sustainability Revolution: How Generative AI Powers Ethical and Transparent Global Supply Chains

Abstract

This study examines how generative artificial intelligence (GenAI) can strengthen transparency and sustainability in global supply chains. Specifically, it distinguishes between (i) GenAI-enabled sustainability reporting (i.e., automated generation of auditable narrative disclosures from multi-tier supply chain data) and (ii) predictive sustainability analytics (i.e., forecasting tools that anticipate environmental and social risks and support proactive interventions). We propose that both capabilities positively influence sustainable supply chain practices and that these effects are contingent on key boundary conditions: supplier collaboration and regulatory pressure. Drawing on stakeholder theory, the resource-based view and sociotechnical systems theory, the paper develops a conceptual model and associated hypotheses and outlines a quantitative research design using survey data from supply chain and sustainability professionals. The proposed model has been analysed using partial least squares structural equation modelling (PLS-SEM), including interaction (moderation) effects. The study contributes by clarifying the distinct transparency and proactivity pathways through which GenAI can enable accountable, data-driven and sustainable supply chain management. The findings show that predictive sustainability analytics is the most significant predictor of the sustainable supply chain practices, followed by GenAI-enabled sustainability reporting, while regulatory pressure and supplier collaboration play significant moderating roles and their combined effect is also significant. Enriched with the theoretical framework, the study offers meaningful implications for supply chain practitioners, policymakers and decision-makers.