Big data analytics and green finance as drivers of sustainable ESG performance
ビッグデータ分析とグリーンファイナンスが持続可能なESGパフォーマンスを促進する (AI 翻訳)
S. Khan, Mohd. Abass Bhat, C. Tiwari, Abhinav Pal, Aastha Behl
🤖 gxceed AI 要約
日本語
本研究は、インドの調達専門家290名を対象に、ビッグデータ分析能力(BDAC)とグリーンファイナンス(GF)がESGパフォーマンスに与える影響を調査。結果、BDACはESG全次元、特に環境パフォーマンスとグリーンパフォーマンスに有意な正の影響を与える。GFは独立してESGを向上するが、BDACとの相互作用はグリーンパフォーマンスに対してのみ有意であり、相乗効果を示す。理論的にはRBV、NRBV、資源依存理論を統合し、新興国調達分野での実証的知見を提供。
English
This study surveys 290 Indian procurement experts to examine how Big Data Analytical Capabilities (BDAC) and Green Finance (GF) drive ESG performance. Results show BDAC significantly improves all ESG dimensions, especially environmental and green performance. GF independently enhances ESG but only moderates the BDAC-green performance link. The study extends RBV, NRBV, and resource-dependence theory, offering empirical evidence from an emerging economy context.
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
本論文はインドの調達専門家を対象としていますが、ビッグデータとグリーンファイナンスの統合的活用がESGパフォーマンスを強化するメカニズムは、日本企業にも示唆を与えます。特に、環境パフォーマンス向上におけるデータ活用とグリーンファイナンスの相乗効果は、日本のサプライチェーン管理やESG投資戦略に応用可能です。
In the global GX context
This study contributes to global GX discourse by empirically linking big data analytics and green finance to ESG performance in an emerging market context. It highlights the synergistic role of technological and financial resources, which is relevant for firms adopting ISSB or CSRD-aligned sustainability strategies. The findings also support the growing interest in data-driven green finance mechanisms worldwide.
👥 読者別の含意
🔬研究者:The paper integrates RBV, NRBV, and resource-dependence theory to show how big data and green finance jointly affect ESG, offering a novel framework for further study.
🏢実務担当者:Firms should invest in both big data analytics and green finance instruments to enhance ESG performance, especially in procurement and supply chain operations.
🏛政策担当者:Policymakers can promote ESG outcomes by supporting data infrastructure and providing access to green finance, fostering a data-driven sustainable procurement ecosystem.
📄 Abstract(原文)
A quantitative research design was adopted using a Web-based survey targeting 290 Indian procurement experts across public and private sectors. The data were analyzed using SmartPLS 4.0, applying the Partial Least Squares–Structural Equation Modeling technique to test measurement reliability, structural relationships and moderating effects. This study investigates the role of Big Data Analytical Capabilities (BDAC) in enhancing firms’ environmental, social and governance (ESG) performance, with a particular emphasis on the moderating effect of Green Finance (GF). It aims to uncover how data-driven capabilities and sustainable financial mechanisms jointly promote ESG outcomes among Indian procurement professionals. The results reveal that BDAC significantly and positively influences all ESG dimensions, particularly Environmental Performance (EP) and Green Performance (GP). Although GF independently improves ESG outcomes, its effect is weaker than BDAC’s. Importantly, GF significantly moderates the relationship between BDAC and GP, but not with EP or Social Performance, highlighting a synergistic role in advancing environmentally driven outcomes. The findings underscore that firms can strengthen ESG performance by simultaneously investing in big data analytics and green financing instruments. Managers and policymakers should encourage data-driven sustainability monitoring and facilitate access to green funds to support environmentally responsible procurement and corporate governance practices. This study extends the Resource-Based View Natural Resource-Based View and Resource-Dependence Theory by integrating technological and financial resources as complementary drivers of sustainability. To the best of the authors’ knowledge, this study is among the first to empirically examine this interaction in the context of Indian procurement experts, a critical yet understudied domain.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1108/jstpm-10-2025-0471first seen 2026-05-15 20:56:16
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