Auditors’ Response to Assessments of High Control Risk: Further Insights
Bibliographic record
Abstract
Abstract Auditing standards prescribe a risk‐based approach where auditors assess the risk of material misstatement and then design and perform audit procedures to reduce audit risk to an appropriately low level. Prior research suggests that auditors are responsive to high control‐risk assessment (CRA), but that this response is, perhaps, only partially effective at reducing audit risk, with relatively little insight into where and why this occurs. By refining analyses to more detailed levels of the audit, I extend this research by providing further insight into auditors’ response to high CRA. I examine and find that audit fees are significantly higher for high CRA in revenue relative to high CRA in other accounts, suggesting that auditor effort in response to high CRA is more pronounced in audit areas of particular interest and concern to investors and regulators. Despite this, I find evidence suggesting that revenue is the only audit area examined where auditor effort in response to high CRA does not attenuate the likelihood of misstatement. Finally, because auditors face time constraints, I examine whether increased effort in response to high CRA in certain audit areas diverts auditors’ attention from other areas with lower risk, thus contributing to the overall association between misstatements and internal control deficiencies documented in prior research. I find a greater likelihood of misstatement in non‐core operating accounts with lower CRA as audit effort increases in response to high CRA in revenue, consistent with the explanation that high CRA in revenue may divert auditors’ attention from other areas of the audit with lower CRA.
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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 | 0.009 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".