Can open audit committee chairs cure the chilling effect of management's presence on auditors' information sharing during audit committee meetings?
Bibliographic record
Abstract
This study experimentally examines how the leadership style of the audit committee (AC) chair (controlling or open) influences the amount of discretionary information that auditors intend to share with the AC, in the context of common meeting formats (i.e., AC meetings with versus without management present, or private meetings between the auditor and AC chair). Participants are highly experienced auditors, including partners and (senior) managers, from Big 4 accounting firms. We predict and find that an open AC chair mitigates the chilling effect of management's presence on the number of discretionary issues shared with the AC. Compared to those who attend AC meetings only, auditors who engage in private meetings with the AC chair before AC meetings plan to disclose fewer discretionary issues to the AC in subsequent AC meetings, but disclose more discretionary issues in total across meetings. AC chair leadership style has no impact on their discretionary information sharing in these private meetings. These results suggest that open AC chairs can mitigate the adverse effect of management's presence on auditors' discretionary information disclosure and have implications for regulators aiming to enhance corporate governance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".