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Record W2127377765 · doi:10.2308/accr.2010.85.2.573

Auditor Independence in a Private Firm and Low Litigation Risk Setting

2010· article· en· W2127377765 on OpenAlexaff
Ole‐Kristian Hope, John Christian Langli

Bibliographic record

VenueThe Accounting Review · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAuditor independenceReputationIndependence (probability theory)BusinessAuditAccountingCompromiseLitigation risk analysisNorwegianSample (material)Inherent risk (accounting)Actuarial scienceExternal auditorInternal auditJoint auditPolitical scienceLawStatistics

Abstract

fetched live from OpenAlex

ABSTRACT: We examine the issue of auditor independence in a unique setting. Specifically, we test for auditor independence impairment among (1) private client firms, for which the risk of auditor reputation loss is lower than for publicly traded firms, and (2) in a low litigation environment (i.e., Norway) that further reduces the expected costs to the auditor associated with independence impairment. We have thus chosen a setting that gives independence impairment its best chance of being detected if it exists. Using a large sample of private Norwegian firms, we analyze whether auditors who receive higher fees are less likely to issue modified opinions. Despite the low litigation risk and the reduced reputation risk, our empirical results provide no evidence that auditors compromise their independence through fee dependence. These results are robust to controlling for the expected portion of fees, to different sample specifications, to the use of both levels and changes specifications, and to a number of sensitivity analyses.

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)0.020
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 score0.545
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.005
GPT teacher head0.216
Teacher spread0.211 · 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

Citations58
Published2010
Admission routes1
Has abstractyes

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