Auditing with data and analytics: External reviewers' judgments of audit quality and effort
Bibliographic record
Abstract
Abstract Audit firms hesitate to take full advantage of data and analytics (D&A) audit approaches because they lack certainty about how external reviewers evaluate those approaches. We propose that external reviewers use an effort heuristic when evaluating audit quality, judging less effortful audit procedures as lower quality, which could shape how external reviewers evaluate D&A audit procedures. We conduct two experiments in which experienced external reviewers evaluate one set of audit procedures (D&A or traditional) within an engagement review, while holding constant the procedures' level of assurance. Our first experiment provides evidence that external reviewers rely on an effort heuristic when evaluating D&A audit procedures—they perceive D&A audit procedures as lower in quality than traditional audit procedures because they perceive them to be less effortful. Our second experiment confirms these results and evaluates a theory‐based intervention that reduces reviewers' reliance on the effort heuristic, causing them to judge quality similarly across D&A and traditional 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 | 0.090 | 0.454 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".