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Record W4416449298 · doi:10.1016/j.aos.2025.101618

Can open audit committee chairs cure the chilling effect of management's presence on auditors' information sharing during audit committee meetings?

2025· article· en· W4416449298 on OpenAlexafffund
Lukas J. Helikum, Karim Jamal, Hun‐Tong Tan, Xiao Li

Bibliographic record

VenueAccounting Organizations and Society · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsMacEwan UniversityUniversity of Alberta
FundersNanyang Technological UniversityUniversity of Alberta
KeywordsAudit committeeAuditContext (archaeology)Chief audit executiveInformation sharingJoint audit

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.003
GPT teacher head0.200
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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