Improving Complex Audit Judgments: A Framework and Evidence*†
Bibliographic record
Abstract
ABSTRACT Regulators and researchers provide evidence that auditors' judgment quality is problematic in complex audit tasks. We introduce a framework for improving auditor judgment in these tasks. The framework builds on dual‐process theory to recognize that high‐quality judgment in complex tasks requires that auditors (i) possess the knowledge needed for the task, (ii) recognize the need for analytical (versus heuristic) processing, and (iii) have sufficient cognitive capacity to complete the analytical processing. Based on the framework, we predict that auditors' need for cognition (NFC), a characteristic theoretically linked to recognizing the need for analytical processing, is associated with higher quality complex judgments. Analysis of 11 studies supports this assertion. We demonstrate the usefulness of the framework by predicting and finding that priming auditors with an accuracy goal improves judgments, particularly for lower NFC auditors, who are less likely to spontaneously engage in analytical processing. The framework facilitates systematic development of interventions to improve auditor judgment by highlighting that solutions should address the specific conditions causing judgment problems.
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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 | 0.041 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 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".