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Record W2019619756 · doi:10.1111/1911-3846.12042

Future Nonaudit Service Fees and Audit Quality

2013· article· en· W2019619756 on OpenAlexvenueno aff
Monika Causholli, Dennis J. Chambers, Jeff L. Payne

Bibliographic record

VenueContemporary Accounting Research · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
FundersKennesaw State UniversityUniversity of Kentucky
KeywordsBusinessAuditQuality auditAuditor independenceRevenueAccountingEarningsAccrualIncentiveJoint auditInternal auditEconomics

Abstract

fetched live from OpenAlex

Prior to the Sarbanes–Oxley Act of 2002, audit partners experienced economic pressure to grow revenue from the sale of nonaudit services to their audit clients. To an auditor who is highly rewarded for revenue generation and growth, nonaudit services may represent a particularly strengthened economic bond with the client. Prior research shows that, in general, nonaudit service fees received in the current period do not impair audit quality. We examine a different setting. We propose that auditor independence can become impaired, and audit quality compromised, when clients that currently purchase relatively low amounts of nonaudit services, increase their purchases of nonaudit services from the auditor in the subsequent period. We test our prediction in the context of earnings management as a proxy for audit quality, measured by (a) performance‐adjusted discretionary accruals and (b) classification shifting of core expenses. Our results indicate that prior to the Sarbanes‐Oxley Act, rewards to the auditor in the form of future additional nonaudit service fees from current‐year high fee‐growth‐opportunity clients adversely affects audit quality. This effect is particularly strong among companies with powerful incentives to manage earnings. Our findings indicate that regulators should consider the multiperiod nature of the client–auditor relationship when contemplating policies that restrict nonaudit services, as well as the overall environment in which audit partners operate. This might include partner compensation arrangements that put pressure on audit partners to focus on increasing revenue at the expense of audit quality.

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)0.004
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.005
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.005

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 head0.045
GPT teacher head0.303
Teacher spread0.258 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations113
Published2013
Admission routes1
Has abstractyes

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