Do Stronger <scp>Wise‐Thinking</scp> Dispositions Facilitate Auditors' Objective Evaluation of Evidence When Assessing and Addressing Fraud Risk?*
Bibliographic record
Abstract
ABSTRACT The objective evaluation of evidence is imperative for audit effectiveness and the proper exercise of professional skepticism. However, numerous studies suggest that auditors fail to evaluate evidence objectively when assessing or addressing the risk of material misstatement due to fraud. We develop theory to predict that auditors do evaluate evidence objectively but only when they have stronger wise‐thinking dispositions (WTDs), a construct that is new to the audit literature. We define WTDs as the tendency of individuals to naturally engage in the balanced revision of beliefs and doubts about target phenomena by thinking openly and reflectively about evidence. We report prediction‐consistent results from two experiments that measure the strength of participants' WTDs and manipulate whether the underlying evidence is less or more indicative of fraud. The experimental results also document that auditors vary considerably in WTD strength and collectively demonstrate the reproducibility of audit judgment‐quality benefits of stronger WTDs. We further validate the WTD construct in auditing using confirmatory bi‐factor analyses to show that it has one higher‐order general factor along with several subfactors. Overall, our theory and results advance the literature by identifying WTDs as a determinant of auditors' ability to objectively evaluate evidence. In addition, our findings have implications for standard setters and audit firms as quality control standards and audit working paper review processes might benefit from revisions that take into account that auditors do not objectively evaluate evidence unless they have stronger WTDs.
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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 | 0.026 | 0.167 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".