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、フィンテックの持続可能性報告への統合：ESG開示と企業の透明性に関する定量分析 (AI 翻訳)
A. Sunitha, K. Srinivas, T.Radhika, B.Chandrakala Naik, P. Sandya Rani
🤖 gxceed AI 要約
日本語
本研究は、AI、ビッグデータ、フィンテックの採用がESG報告の質と企業の透明性に与える影響を、インド、UAE、英国の312名の専門家データを用いてPLS-SEMで分析。その結果、すべてのデジタル構築物が持続可能性報告の質と透明性に正の影響を与え、透明性がESGパフォーマンスへの媒介効果を持つことを示した。企業規模がAIとESG結果の関係を調整することも明らかにした。
English
This study examines the impact of AI, big data, and FinTech adoption on ESG disclosure quality and corporate transparency using survey data from 312 professionals in India, UAE, and UK. PLS-SEM results show positive effects of these digital constructs on reporting quality and transparency, with transparency mediating the link to ESG performance. Firm size moderates the AI-ESG relationship. The findings offer implications for regulators and firms under GRI, IFRS S1/S2, and CSRD.
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、AIやビッグデータの活用によるESG報告の質向上を示しており、日本のSSBJや有報での開示充実に示唆を与える。ただし、日本固有の政策やデータに基づくものではないため、直接的な応用には注意が必要。
In the global GX context
This paper contributes to the global discourse on digitalization of ESG disclosure, relevant to the evolving frameworks like ISSB, CSRD, and SEC climate rules. It provides empirical evidence that AI, big data, and FinTech can enhance transparency and ESG performance, which is crucial for investors and regulators worldwide.
👥 読者別の含意
🔬研究者:This paper offers a validated model integrating RBV, stakeholder, and signaling theories to understand digital technology's role in ESG disclosure, paving way for cross-country comparative studies.
🏢実務担当者:Corporate sustainability teams can leverage the findings to justify investments in AI and data analytics for improving ESG reporting transparency and performance.
🏛政策担当者:The results underscore the importance of digital infrastructure in mandatory ESG reporting frameworks and suggest that firm size should be considered in regulatory design.
📄 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.20488988first seen 2026-06-03 04:54:33
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