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Record W3140920764

Fool Me Once, Shame on You; Fool Me Twice, Shame on Me: The Long-Term Impact of Arthur Andersen’s Demise on Partners’ Audit Quality

2019· article· en· W3140920764 on OpenAlexaff
Feng Guo, Ling Lei Lisic, Jeffrey Pittman, Timothy A. Seidel, Mi Zhou, Ying Zhou

Bibliographic record

VenueScholarsArchive (Brigham Young University) · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAuditDemiseShameQuality auditAccountingJoint auditQuality (philosophy)PsychologyActuarial scienceAudit planAudit evidenceBusinessInternal auditSocial psychologyPolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

Although recent evidence suggests that individual audit partners explain a substantial portion of the variation in audit quality proxies, much less is known about what determines an audit partner’s quality. Psychology and behavioral economics theories hold that an individual’s experiences can have enduring impacts on subsequent behavior. We examine whether auditors’ direct exposure to Arthur Andersen’s collapse has a long-term impact on the quality of their audits. Our evidence implies that audit partners who directly experienced Andersen’s demise impose stricter monitoring evident in their clients exhibiting a lower propensity for misstatements and small profits, and paying higher audit fees. Importantly, these findings reconcile with research in finance and economics implying that firsthand experiences matter more to subsequent behavior than general economic conditions or secondhand or thirdhand experiences. Collectively, the results shed light on one facet of how partners’ audit quality evolves over time. Our findings suggest that major failures associated with the audit firm in which an auditor works can ultimately result in these affected individuals later delivering higher audit quality, which should benefit audit committees in partner selection decisions and audit firms in designing partner assignment policies.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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.

Opus teacher head0.019
GPT teacher head0.260
Teacher spread0.241 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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