Are evaluations of audit quality influenced by management’s intentions and outcomes?
Bibliographic record
Abstract
One major regulatory device for improving audit quality is to require auditors to assess the ‘Tone at the Top’ (that is, the integrity and especially top management’s intentions and attitude towards earnings management), but prior audit research suggests that auditors are quite poor at assessing the knowledge, preferences and intentions of others. In this study, we report results of two experiments in which a material misstatement occurs intentionally (fraud) or inadvertently (error). Shareholders suffer a loss (or no adverse consequence). Experienced auditors (Certified Public Accountants [CPAs]) and a control group of university students assess the appropriateness of the auditor’s conduct and specify a penalty. Experiment 1 results show that CPAs are not influenced by management’s intentions or outcomes. Students are responsive to outcomes but not to management’s intentions. In Experiment 2, we re-ran Experiment 1 using a within-subjects design to make management’s intentions more salient. Experiment 2 results indicate that, this time, both CPAs and students respond to intention but in a manner opposite of that prescribed by professional standards. Students also respond to outcomes, though CPAs do not.
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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 | 0.010 | 0.062 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".