Does an Audit Judgment Rule Increase or Decrease Auditors' Use of Innovative Audit Procedures?
Bibliographic record
Abstract
ABSTRACT The current audit environment encourages auditors to conduct defensive auditing procedures in lieu of using new, innovative, and potentially more effective audit procedures, due to concerns these procedures may be second‐guessed in litigation or by audit inspectors such as the PCAOB. As a result, auditors may prefer traditional “generally accepted” procedures over innovative procedures that are potentially more effective. We test recent proposals that an Audit Judgment Rule (AJR) encourages the use of innovative, and potentially more effective, audit procedures analogous to the similar Business Judgment Rule that affords legal protections to corporate directors. Under an AJR, litigators or audit inspectors could not second‐guess auditor judgments, even if they perceive that alternate judgments would have ordinarily been reached, provided the auditor's judgment was made in good faith and in a rigorous manner. However, the AJR's requirements that auditors must defend the rigor of their innovative judgments could potentially backfire and lead auditors to select more traditional procedures. Under the framework of goal activation theory, we conduct an experiment with audit managers and seniors and find that an AJR makes auditors less likely to select innovative audit procedures, particularly when audit risk is high. They do so despite believing the innovative procedures to be more effective than the traditional procedures. Findings from a supplementary experiment with experienced auditors further suggest that national office affirmation of the reasonableness of the procedures does not help overcome this effect. Overall, our findings suggest that an AJR may have the unintended consequence of further increasing auditors' focus on more traditional, and potentially less effective, audit procedures.
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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 | 0.020 | 0.193 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".