Eliciting deliberative and implemental mindsets in audit planning
Bibliographic record
Abstract
Abstract There is concern that rather than critically deliberating specific circumstances, auditors focus on selecting and documenting defensible audit positions. Currently, subordinate auditors perform tasks mindful that they will be accountable for their work both inside (e.g., partners) and outside (e.g., PCAOB) the firm and adopt “implementation intentions” based on previous review experiences to guide their performance. In the context of fraud‐detection planning, we consider an alternative approach in which subordinate auditors work under contingent reward agreements under which they will be compensated for effective fraud‐detection plans. Lacking an anticipated course of action, they invoke a “deliberative mindset” in order to create a task strategy. In an experiment, auditors completed a fraud‐detection planning task under contingent rewards, accountability, or anonymity. We find that auditors operating under contingent rewards used deliberative mindsets. They were better able to identify potential fraud, select more effective procedures, and plan more hours for effective procedures. Auditors under accountability completed the planning task based on implementation intentions. They focused on broadly increasing audit hours across procedures, including allocating significantly more hours to less effective procedures. Mediation analysis shows that improved planning performance resulted from the use of deliberative mindsets and not implementation intentions.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".