Audit Partner Tenure and Internal Control Reporting Quality: U.S. Evidence from the Not‐For‐Profit Sector
Bibliographic record
Abstract
Abstract This study examines the effects of audit partner tenure and audit partner changes on internal control reporting quality for large U.S. not‐for‐profit (NFP) organizations. Regulators contend that audit partners lose their objectivity over successive audits, reducing audit quality. A large body of research has examined this issue, primarily in non‐U.S. jurisdictions, with mixed results. We examine the associations between audit partner tenure and audit partner changes and the incidence of reported internal control deficiencies (ICDs), the quality of internal control reports (following PCAOB audit quality indicators), and the severity of reported ICDs. We find negative associations between audit partner tenure and the incidence of reported ICDs, the quality of internal control reports, and the severity of reported ICDs. Together, these findings indicate that internal control reporting quality deteriorates with audit partner tenure. However, we find no association between audit partner changes and internal control reporting, which is consistent with partners lacking client specific knowledge in their first year with a client. Finally, we find no association between either audit partner tenure or changes and the likelihood of remediation. Our findings contribute large‐sample U.S. evidence on the association between audit partner tenure and internal control reporting quality and provide useful information to government regulators, NFP boards charged with the oversight of the external auditor and internal controls, and NFP stakeholders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".