The effects and potential benefits of audit committee oversight in a strategic setting
Bibliographic record
Abstract
Abstract Since the passage of the Sarbanes‐Oxley Act of 2002, many notable frauds have been tied to ineffective audit committee (AC) oversight. As a result, AC oversight is of continuing interest, and regulators continue to debate this issue, garnering a growing body of research focused on the role played by the AC. But little theoretical research exists to guide analytical and empirical researchers investigating AC oversight. The purpose of this study is to provide theoretical guidance by examining AC oversight in a strategic setting. We focus on the AC's role in overseeing internal controls (ICs) and the impact of whether the AC relies on management in designing the controls. We characterize how the nature of control risk changes and how IC strength is associated with the amount of managerial fraud, expected probability of fraud detection (which, on average, equates to audit effort), and audit quality (assessed as 1 − audit risk) across two settings defined by the degree of AC oversight. As one example that highlights the need for theoretical guidance, we consider the literature's presumption that IC strength is negatively associated with audit effort. We find that this association may be positive or negative as IC changes, where the association varies with the degree of direct AC oversight and the change in payoff parameters.
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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 | 0.026 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".