The Sustainability Revolution: How Generative AI Powers Ethical and Transparent Global Supply Chains
持続可能性革命：生成AIが倫理的で透明なグローバルサプライチェーンを強化する方法 (AI 翻訳)
R. M. Ellahi, J. Qureshi, M. H. Farooqui, Syed Faisal Abbas Shah, Fizzah Ayub
🤖 gxceed AI 要約
日本語
本研究は、生成AIがサプライチェーンの持続可能性を強化する2つの経路（報告自動化と予測分析）を提案し、PLS-SEM実証分析により予測分析が最も強い予測因子であることを示した。規制圧力とサプライヤー協力が重要な調整効果を持つ。
English
This study proposes two pathways for generative AI to enhance supply chain sustainability: automated reporting and predictive analytics. PLS-SEM analysis shows predictive analytics is the strongest predictor, with regulatory pressure and supplier collaboration as significant moderators.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本のサプライチェーンはSSBJ対応やScope3開示が急務であり、生成AIによる報告自動化とリスク予測は実務負担軽減と開示品質向上に直結する。経済産業省のデータ連携基盤構想とも親和性が高い。
In the global GX context
This paper provides empirical validation of distinct AI pathways for supply chain sustainability, relevant to global mandatory disclosure regimes (CSRD, ISSB, SEC). It offers clear guidance on prioritizing AI investments under varying regulatory and collaborative conditions.
👥 読者別の含意
🔬研究者:Confirms a two-pathway model with moderators, offering a theoretical framework for future studies on AI in sustainable supply chains.
🏢実務担当者:Suggests prioritizing predictive analytics over reporting automation for greatest impact on sustainable practices, considering collaborative and regulatory contexts.
🏛政策担当者:Indicates that regulatory pressure strengthens the effectiveness of GenAI in driving supply chain sustainability, supporting policies that mandate transparency.
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1002/csr.70691first seen 2026-07-18 07:31:00
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