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Record W3125939619 · doi:10.1111/1911-3846.12467

U.S. Auditors' Perceptions of the PCAOB Inspection Process: A Behavioral Examination

2018· article· en· W3125939619 on OpenAlexvenueno aff
Lindsay M. Johnson, Marsha B. Keune, Jennifer Winchel

Bibliographic record

VenueContemporary Accounting Research · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsAuditAccountingEnforcementCompliance (psychology)BusinessQuality auditPerceptionAudit riskListed companyControl (management)PsychologySocial psychologyPolitical scienceEconomicsManagementLaw

Abstract

fetched live from OpenAlex

ABSTRACT This study examines U.S. auditors' observations of the PCAOB inspection process, and its impact on their work, in order to understand the current U.S. regulatory audit climate. Using 20 interviews with experienced auditors, we consider behavioral factors (e.g., perceived power of and trust in the PCAOB) that can impact the level and form of auditor compliance according to theory from the slippery slope framework on audit regulation (Kirchler et al. 2008; Dowling et al. 2018). Our participants described an audit climate with a powerful regulator. They reported that their desire to receive “clean” inspection reports has had a substantial impact on audit procedures and quality control. However, our participants do not appear to have high trust in the PCAOB, as they questioned aspects of the inspection process and its expectations. Accordingly, we conclude that U.S. public company auditors operate in an antagonistic environment in which auditors perceive the PCAOB has high coercive power. In other words, they comply due to fear of enforcement rather than agreement with the PCAOB's views on audit quality. Some auditors also indicated that they consider both the costs and benefits of compliance. Theoretical intuition implies that any future increases to perceived costs relative to perceived benefits of compliance could ultimately decrease the PCAOB's coercive power and reduce U.S. auditor compliance. Our findings have implications for regulators and researchers interested in understanding behavioral factors that may influence regulatory compliance.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.003
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 head0.041
GPT teacher head0.322
Teacher spread0.281 · 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

Citations143
Published2018
Admission routes1
Has abstractyes

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