Revising Audit Plans to Address Fraud Risk: A Case of “Do as I Advise, Not as I Do”?*
Bibliographic record
Abstract
ABSTRACT Prior research documents that auditors fail to revise audit plans to effectively address identified fraud cues. While auditors may understand what evidence would address such cues, we propose that auditors fail to apply this understanding because they use implemental mindsets when making decisions for themselves (i.e., deciding). However, we also propose that auditors use deliberative mindsets when advising. To test our predictions, we assign auditors to a decider or an advisor role in a realistic case that contains seeded fraud cues and asks them to consider revising last year's plan. We also manipulate whether the case prompts auditors to revise the plan unconventionally. Results indicate decider‐condition auditors use implemental mindsets: Prompted deciders follow the unconventional plan without regard to underlying fraud risk and unprompted deciders stick with the same‐as‐last‐year plan. Advisor‐condition auditors use more deliberative mindsets: In the prompt and no prompt conditions, they identify plans that are strongly linked to their own fraud risk assessments and that better align with experts' recommended plan for effectively addressing the seeded fraud cues. Supplemental analyses suggest deciding and advising auditors both follow the experts' plan when they believe in its potential effectiveness but, after controlling for the influence of perceived effectiveness, deciding auditors follow it to a greater extent simply because they believe the PCAOB wants it. By contrast, advising auditors do not exhibit signs of excessive PCAOB influence. Our findings provide evidence that seeking informal advice (or thinking like an advisor) helps auditors to effectively revise audit plans in response to identified fraud risk—it helps when a prompt is present or not, suggesting it complements rather than merely substitutes for interventions meant to improve auditors' judgment and decision making.
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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 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".