Does gender and ethnic diversity among audit partners influence office‐level audit personnel retention and audit quality?
Bibliographic record
Abstract
Abstract Motivated by prior literature on organizational identification and 23 semistructured interviews with a variety of US audit partners and directors, we examine whether the gender and ethnic diversity of an office's audit partners influences the retention of the office's audit professionals and the quality of the audits conducted by the office. Using hand‐collected data on US audit partners, we find that greater levels of (or changes in) diversity in office audit partners' gender and ethnicity are associated with lower (reduced) turnover among office audit professionals and higher (increased) office‐level audit quality. We conduct a path analysis based on the most common mechanisms highlighted in our interviews to provide further insight into the audit quality results. The results indicate partial mediation through increased retention, greater gender and ethnic diversity among office audit personnel, client continuity, and increased efficiency. Further tests reveal that the association with audit quality is incremental to, and distinct from, the effect of individual engagement partner characteristics and does not reflect client screening. The findings underscore the importance of gender and ethnic diversity among office audit partners to organizational outcomes and provide important practical implications for audit firms.
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 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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".