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Record W2108626185 · doi:10.1177/0148558x14544503

The Effect of Audit Experience on Audit Fees and Audit Quality

2014· article· en· W2108626185 on OpenAlex
Steven F. Cahan, Jerry Sun

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Accounting Auditing & Finance · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsAuditAccountingQuality auditAudit evidenceBusinessJoint auditAccrualAudit planInformation technology auditWalk-through testChief audit executiveInternal auditPerformance auditCertificationEconomicsManagementEarnings

Abstract

fetched live from OpenAlex

Prior research on audit experience focuses on behavioral studies that are conducted by running experiments. Although these studies provide evidence on the role of experience in completing specific audit tasks, they do not shed light on how experience affects a complete audit engagement. We conduct an archival study to examine the effect of audit experience on audit fees and audit quality. Using unique data from China, where the signees of the audit report can be identified and linked with a government database containing personal information about certified public accountants, we find that experience is positively associated with audit fees and negatively associated with absolute discretionary accruals. Furthermore, we extend the research on personal characteristics of audit partners by considering the incremental effects of gender, education, engagement tenure, industry specialization, and client importance after controlling for overall audit experience. Overall, our results suggest that the auditors’ personal characteristics may serve as a signal of the level of care that will be exercised during the audit process. Our results also have implications for China’s recently announced regulation that would require localization of Big 4 offices in China.

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.

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)0.006
metaresearch head score (Gemma)0.043
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.000
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 head0.008
GPT teacher head0.240
Teacher spread0.233 · 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