Are intergroup differences between the audit committee and the rest of the board associated with monitoring effectiveness?
Bibliographic record
Abstract
Abstract In contrast to prior research that typically focuses on the characteristics of the audit committee (AC), we investigate how intergroup differences between the AC and the rest of the board (ROB) affect monitoring effectiveness. Drawing on group literature and the similarity attraction paradigm, we hypothesize that high intergroup differences between the AC and the ROB impede communication and information sharing. Poor “fit” between the AC and the ROB can lead to an “us versus them” mentality that reduces trust and hinders knowledge exchange, diminishing monitoring effectiveness. Using a sample of listed US firms, we find that intergroup differences between the AC and the ROB in terms of their respective characteristics are linked to a lower likelihood of reporting an existing or likely material weakness, higher discretionary accruals, and a lower likelihood of a going‐concern opinion among financially distressed firms. These negative effects are most pronounced when the AC and the ROB are very different (i.e., in the upper quartile and decile of the AC‐ROB distance distribution). Additional analyses show a higher probability of a Big R restatement, a lower likelihood of a Big R restatement when a material misstatement likely exists, and a lower likelihood of goodwill impairment when one is expected. Notably, the adverse impact of AC‐ROB dissimilarity is more prominent when the AC is less powerful or lacks group stability. Regulators and companies should be aware that AC composition decisions cannot be made in isolation because large intergroup differences in director profiles between the AC and the ROB reduce monitoring effectiveness.
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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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".