The Influence of Audit Committee Chair Characteristics on Financial Reporting Quality
Bibliographic record
Abstract
This study examines the extent to which the characteristics of the audit committee chair enhance the quality of financial reports and reduce the possibility for companies to receive a modified audit opinion (MAO) from an external auditor. We apply logistic regression to investigate the influence of Audit Committee Chair (ACC) characteristics on the FRQ (FRQ), for a sample of 460 firm-year observations (service and industrial company listed) on the Amman stock exchange for the years 2017–2020. This study uses the MAO as a proxy for Financial Reporting Quality (FRQ). The results of this study confirmed that the characteristics of the chair of the audit committee have significant and clear impacts on the quality and efficiency of financial reports, which is in line with previous studies that have addressed this topic. The results also indicated that researchers, academics, regulators, and policymakers should not look just at the characteristics of audit committees as a whole, given that audit committee chairs have effects on financial reports. This study presents its contribution through experimental demonstration of the characteristics of the chair of the audit committee and how these affect the financial reports of companies. It also provides a guide for benefits for working to provide a basis for organizational procedures, especially those related to the impact on corporate boards and internal and external auditing.
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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 | 0.022 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".