The Effects of Corporate Governance Experience and Financial Reporting and Audit Knowledge on Audit Committee Members' Judgments
Bibliographic record
Abstract
Interest in audit committees as part of overall corporate governance has increased dramatically in recent years, with a specific emphasis on member independence, experience, and knowledge. This paper reports the results of a study investigating whether audit committee members' corporate governance experience and financial reporting and audit knowledge affect their judgments in auditor-corporate management conflict situations. A sample of 68 audit committee members completed an accounting policy dispute case and several knowledge and ability tests. The results indicate that, as expected, greater independent director experience and greater audit knowledge was associated with higher audit committee member support for an auditor who advocated a substance over form approach in the dispute with client management. Conversely, concurrent experience as a board director and a senior member of management was associated with increased support for management. Collectively, these findings have a number of implications for practice and research. The results provide justification for calls that audit committees be composed completely of independent directors. The results also support auditor concerns that varying knowledge levels lead to systematic differences in audit committee member judgments in disputes between auditors and management.
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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 | 0.009 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".