A Matter of Appearances: How Does Auditing Expertise Benefit Audit Committees When Selecting Auditors?†‡
Bibliographic record
Abstract
ABSTRACT Literature to date reveals relatively little about the role of expertise in auditor selection beyond basic preferences for Big 4 and industry specialist auditors. We hypothesize that audit committees whose members have no Big 4 auditing experience are likely to struggle when interviewing prospective Big 4 partners, leading such committees to draw on superficial, heuristic cues in lieu of conducting more substantive evaluations. To test this prediction, we obtain independent ratings of the facial attractiveness of audit partners identified from Form AP filings recently mandated by the US PCAOB. Our primary finding is that audit committees with no Big 4–experienced members are more likely to favor partners whose photographs raters view to be highly attractive. We characterize attractiveness as a superficial attribute for auditor selection because we detect no relation between attractiveness and accruals‐ or restatement‐based measures of financial reporting quality for audit committees with one or more Big 4–experienced members. We do find an inverse association between attractiveness and financial reporting quality for committees without this experience, likely reflecting the statistical implication of a selection bias. We conclude that auditing expertise mitigates the influence of superficial considerations in auditor selection, enabling audit committees to fulfill their stewardship role more effectively.
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 | 0.009 | 0.072 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".