Audit Committee Member Investigation of Significant Accounting Decisions
Bibliographic record
Abstract
SUMMARY: In the post-Enron environment, audit committee (AC) members are under increased scrutiny to demonstrate effectiveness in resolving significant accounting issues. However, prior research suggests that AC members are not involved in material auditor-client negotiations and that they are often not adequately informed of the issue resolution process. Therefore, AC members may not be effective in their oversight of the financial reporting process unless an accounting decision is clearly aggressive or adequate information about the decision is provided. In this study, I examine AC members’ investigation of accounting decisions when they are (or not) adequately informed of the negotiation process that led to the decision and when the decision results in an aggressive (versus conservative) financial reporting outcome. The hypotheses are developed from social psychology and research on corporate governance practice suggesting that AC members investigate accounting decisions to reduce discomfort in the financial reporting process by asking probing questions of the auditors and management. The results indicate that negotiation knowledge increases AC discomfort but has no effect on AC investigation, perhaps because potential questions were adequately addressed by the available information. I also find that AC members investigate more extensively as accounting decisions become increasingly aggressive and AC members with accounting experience are particularly thorough in their investigations when accounting decisions are aggressive. The results of this research have important implications to practice and future research.
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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 | 0.024 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".