INTEGRATING ARTIFICIAL INTELLIGENCE, BIG DATA, AND FINTECH INNOVATIONS IN SUSTAINABILITY REPORTING: A QUANTITATIVE ANALYSIS OF ESG DISCLOSURE AND CORPORATE TRANSPARENCY
人工知能、ビッグデータ、フィンテックの革新を統合したサステナビリティ報告：ESG開示と企業透明性の定量分析 (AI 翻訳)
A. Sunitha, K. Srinivas, T.Radhika, B.Chandrakala Naik, P. Sandya Rani
🤖 gxceed AI 要約
日本語
本論文は、AI、ビッグデータ、フィンテックの採用がサステナビリティ報告の品質と企業透明性に与える影響を、インド・UAE・英国の312名の専門家を対象にPLS-SEMで分析。3つのデジタル技術はいずれも報告品質と透明性に有意な正の影響を与え、透明性はデジタル技術とESGパフォーマンスの関係を媒介する。また、企業規模はAIとESG成果の関係を調整する。
English
This study empirically analyzes the impact of AI, big data, and fintech adoption on sustainability reporting quality and corporate transparency using PLS-SEM on a sample of 312 professionals from India, UAE, and UK. All three digital constructs positively affect reporting quality and transparency, with transparency mediating the link to ESG performance. Firm size moderates the AI-ESG relationship.
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
日本企業がSSBJ基準に対応する中、AIやフィンテックを活用したESG開示の高度化に示唆を与える。特に、透明性の媒介効果は、開示の質向上が投資家評価に繋がる可能性を示唆しており、日本の有報・統合報告書への応用が期待される。
In the global GX context
This paper provides quantitative evidence that AI, big data, and fintech can enhance ESG disclosure quality, directly relevant to global frameworks like IFRS S1/S2 and CSRD. The mediating role of corporate transparency underscores the importance of holistic digital adoption for improving ESG outcomes, which can guide global policymakers and firms in technology investment decisions.
👥 読者別の含意
🔬研究者:Scholars in digital finance and ESG governance will find a validated structural model and cross-country empirical evidence on technology-driven disclosure improvements.
🏢実務担当者:Corporate sustainability teams can use these findings to justify investments in AI and fintech for more transparent and higher-quality ESG reporting.
🏛政策担当者:Regulators can note the positive impact of digital adoption on disclosure quality, supporting policies that encourage technology integration in sustainability reporting frameworks.
📄 Abstract（原文）
The convergence of artificial intelligence (AI), big data analytics, and financial technology (FinTech) with environmental, social, and governance (ESG) reporting is one of the most impactful processes in the modern corporate governance. Although these three forces of digital have increasingly been the focus of scholarly and regulatory attention, their combined and interactive impacts on ESG disclosure quality and corporate transparency are yet to be explored empirically. This paper proposes and validates a theoretically based structural design that looks into the impact of AI and business analytics adoption, big data potential, and FinTech innovation on the quality of sustainability reporting, corporate transparency, and ESG performance in a cross-sectional sample of 312 sustainability and finance professionals that are based in firms located in India, the United Arab Emirates, and the United Kingdom. The study is based on Resource-Based View (RBV), Stakeholder Theory, and Signaling Theory and uses Partial Least Squares Structural Equation Modelling (PLS-SEM) which shows that all three digital constructs have valuable positive impacts on the quality of sustainability reporting and corporate transparency. Corporate transparency is also established as a strong mediator between the digital technology adoption and the ESG performance. The introduction of firm size as a modulating condition of the relationship between the adoption of AI and ESG results. Its results are part of an emerging body of interdisciplinary work related to digital finance, management information systems, and sustainability governance providing practical implications to corporate managers and regulators, as well as developers of FinTech products working within emerging ESG requirements such as GRI, IFRS S1/S2, and the EU Corporate Sustainability Reporting Directive (CSRD).
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.20488987first seen 2026-06-03 04:54:46
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