Investigating the Relationship Between ESG Disclosure Performance and Audit Fees in the Presence of Institutional Ownership: Evidence from Malaysian Listed Firms
Bibliographic record
Abstract
Based on the Malaysian market, this study investigates the connection between ESG (environmental, social, and governance) disclosure performance and audit fees and examines whether institutional ownership moderates this relationship. The sample of this study comprises 323 firm-year observations collected from 49 Malaysian publicly listed companies covering 2012 to 2020. Panel data regression is employed to test the hypotheses. The findings indicate a significant positive relationship between ESG disclosure performance and audit fees, suggesting that auditors perceive ESG reporting as increasing audit complexity and risk. Further, institutional ownership strengthens this positive relationship, indicating that sophisticated investors’ monitoring roles lead to more thorough auditing of ESG disclosures. Our primary contribution is resolving mixed findings in prior literature by identifying institutional ownership as a key moderating variable. The findings offer critical insights for Malaysian regulators in designing the ESG verification framework and help companies and investors better understand audit cost drivers. This study highlights the real-world impact of institutional shareholders on corporate governance and raises market awareness of how auditors respond to sustainability disclosures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".