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Record W2885356347 · doi:10.1111/1911-3846.12448

Spillover Effects of Internal Control Weakness Disclosures: The Role of Audit Committees and Board Connections

2018· article· en· W2885356347 on OpenAlexvenueno aff
Shijun Cheng, Robert H. Felix, Raffi Indjejikian

Bibliographic record

VenueContemporary Accounting Research · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
FundersUniversity of Michigan
KeywordsAccountingAuditBusinessSpillover effectControl (management)WeaknessAudit committeeEconomicsManagement

Abstract

fetched live from OpenAlex

ABSTRACT We find that firms are less likely to report an internal control material weakness (as mandated by the Sarbanes‐Oxley Act) in a given year if one of their audit committee members is concurrently on the board of a firm that disclosed a material weakness within the prior three years. We find a similar spillover effect for financial restatement disclosures. The spillover from material weakness disclosures is evident only if a shared director has more experience with the disclosing firm or can channel more information about the disclosed material weakness. Our findings suggest that prior director experiences outside the firm influence the work of audit committees inside the firm. One rationale is that a director's prior experience with an adverse disclosure helps diffuse important insights and serves as a catalyst for improvements in a firm's internal control and financial reporting practices. An alternative explanation, which we cannot dismiss, holds that a director's prior experience helps a firm to underreport material weaknesses and financial restatements without any attendant improvements in the underlying practices.

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)0.003
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.013
GPT teacher head0.263
Teacher spread0.250 · 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

Citations51
Published2018
Admission routes1
Has abstractyes

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