人工知能統合とビッグデータ分析がサステナビリティ報告監査品質に与える影響：ESG開示と業務複雑性を調整変数として
The Effect of Artificial Intelligence Integration and Big Data Analytics on Sustainability reporting Audit Quality with ESG Disclosure and Operational Complexity as Moderating Variables (原題)
Inez Permatasari, Noer Sasongko
🤖 gxceed AI 要約
日本語
インドネシアIDX上場エネルギー企業46社・138観測（2022〜2024年）を対象に、AIとビッグデータ分析（BDA）がサステナビリティ報告監査品質（SRAQ）に与える影響を、ESG開示と業務複雑性の調整効果とともに検証した。BDAはSRAQに有意な正の影響を与える一方、AIは有意な影響を示さず、ESG開示・業務複雑性の調整効果も確認されなかった。技術的成熟度の不足からAI活用が限定的である一方、BDAはサステナビリティ監査で実効的に活用されている実態を示唆する。
English
Using 138 firm-year observations from 46 Indonesian energy firms listed on IDX (2022–2024), this study tests how AI and big data analytics (BDA) affect sustainability reporting audit quality (SRAQ), moderated by ESG disclosure and operational complexity. BDA significantly improves SRAQ, while AI shows no significant effect, and neither moderator is supported. The authors attribute this to limited AI readiness in Indonesian energy firms versus more mature BDA use in sustainability assurance.
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
SSBJ基準・保証業務の整備が進む日本では、デジタル監査技術がサステナビリティ保証の品質をどう左右するかは実務的関心が高い。新興国でのAI導入ギャップは、日本企業の保証対応や監査法人のデジタル化投資を考える上での比較材料になる。
In the global GX context
As ISSB/CSRD assurance requirements tighten globally, evidence on whether digital audit tools actually raise sustainability assurance quality is scarce. This paper offers emerging-market empirical data showing BDA outperforms AI in assurance practice, useful for regulators weighing assurance-tech expectations.
👥 読者別の含意
🔬研究者:AI・BDAとサステナビリティ保証品質の関係を調整変数付きで検証した、新興国初の実証枠組みとして参照価値がある。
🏢実務担当者:サステナビリティ保証の品質向上にはBDA活用とESGデータ統合・デジタル監査能力の強化が有効という示唆が得られる。
🏛政策担当者:サステナビリティ保証制度の設計において、デジタル監査技術の導入実態と技術的成熟度の差を考慮すべき点を示す。
📄 Abstract（原文）
This study investigates the effects of Artificial Intelligence (AI) and Big Data Analytics (BDA) on Sustainability Reporting Audit Quality (SRAQ) with ESG Disclosure (ESGD) and Operational Complexity (OC) as moderating variables. This research addresses the limited empirical evidence regarding the effectiveness of digital audit technologies in sustainability assurance practices, particularly within Indonesian energy sector companies. The research applied a quantitative associative method based on secondary data derived from the annual and sustainability reports of energy companies listed on the IDX for the 2022–2024 period. The purposive sampling technique resulted in 46 companies with 138 observations. The data were analyzed using SPSS version 25 by applying descriptive statistical analysis, classical assumption testing, and Moderated Regression Analysis (MRA). The findings indicate that Big Data Analytics significantly affects Sustainability Reporting Audit Quality, while Artificial Intelligence does not significantly affect Sustainability Reporting Audit Quality. In addition, ESG Disclosure and Operational Complexity are unable to moderate the relationships between AI, BDA, and Sustainability Reporting Audit Quality. These findings suggest that AI implementation in Indonesian energy companies remains relatively limited due to technological readiness and sustainability assurance challenges, whereas BDA has been more effectively utilized in sustainability auditing practices. Theoretically, this study extends to the sustainability auditing literature through the integration of digital audit technologies, ESG disclosure, and operational complexity within a single research framework based on Stakeholder Theory and Contingency Theory. Practically, the findings imply that companies and auditors should strengthen ESG data integration, technological readiness, and digital audit competencies to improve sustainability assurance practices.
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- semanticscholar https://journal.yrpipku.com/index.php/ijedr/article/download/11179/6395first seen 2026-10-23 05:55:16 · last seen 2026-11-06 05:23:58
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