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Record W3125406116 · doi:10.1111/1911-3846.12129

What is the Relationship Between Audit Partner Busyness and Audit Quality?

2015· article· en· W3125406116 on OpenAlexvenueno aff
John Goodwin, Donghui Wu

Bibliographic record

VenueContemporary Accounting Research · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
FundersHong Kong Polytechnic University
KeywordsAuditQuality auditAccountingQuality (philosophy)Audit evidenceBusinessJoint auditPortfolioActuarial scienceInternal auditFinance

Abstract

fetched live from OpenAlex

Abstract Contemporaneous studies generally find a negative relationship between audit partner busyness ( APB ), measured as the number of clients in an audit partner's portfolio, and audit quality. Their argument is that a busy partner does not devote sufficient time to properly audit his average client. Contrary to these studies, we argue that when busyness is optimally chosen by the partner, in equilibrium, there is no causal relationship between APB and audit quality. Using Australian data for the 1999–2010 period, we show that APB is not reliably linked to audit quality, consistent with this equilibrium theory. We argue that causality can be ascribed to the APB ‐audit quality relationship when accounting scandals exogenously shocked the Australian audit market during the 2002–04 period and APB likely deviated from optimum levels. Supporting this disequilibrium view, we find that higher APB reduces a partner's propensity to issue first‐time going‐concern opinions during this period. Our evidence highlights the importance of the equilibrium condition in testing empirical associations between audit outcomes and endogenous auditor attributes, and shows that the detrimental effect of APB on audit quality is not as pervasive as contemporaneous studies suggest.

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)0.013
metaresearch head score (Gemma)0.035
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0040.008
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.183
GPT teacher head0.372
Teacher spread0.189 · 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

Citations214
Published2015
Admission routes1
Has abstractyes

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