U.S. Auditors' Perceptions of the PCAOB Inspection Process: A Behavioral Examination
Bibliographic record
Abstract
ABSTRACT This study examines U.S. auditors' observations of the PCAOB inspection process, and its impact on their work, in order to understand the current U.S. regulatory audit climate. Using 20 interviews with experienced auditors, we consider behavioral factors (e.g., perceived power of and trust in the PCAOB) that can impact the level and form of auditor compliance according to theory from the slippery slope framework on audit regulation (Kirchler et al. 2008; Dowling et al. 2018). Our participants described an audit climate with a powerful regulator. They reported that their desire to receive “clean” inspection reports has had a substantial impact on audit procedures and quality control. However, our participants do not appear to have high trust in the PCAOB, as they questioned aspects of the inspection process and its expectations. Accordingly, we conclude that U.S. public company auditors operate in an antagonistic environment in which auditors perceive the PCAOB has high coercive power. In other words, they comply due to fear of enforcement rather than agreement with the PCAOB's views on audit quality. Some auditors also indicated that they consider both the costs and benefits of compliance. Theoretical intuition implies that any future increases to perceived costs relative to perceived benefits of compliance could ultimately decrease the PCAOB's coercive power and reduce U.S. auditor compliance. Our findings have implications for regulators and researchers interested in understanding behavioral factors that may influence regulatory compliance.
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".