How do lead auditor instructions influence component auditors' evidence collection decisions? The joint influence of construal interpretations and responsibility
Bibliographic record
Abstract
Abstract Regulators have raised concerns about the quality of component auditors' work. Of particular concern is that component auditors often do not adequately perform procedures and gather enough quality evidence. This failure is likely caused by component auditors' different interpretations of lead auditor instructions and by their lack of responsibility. Our interview findings suggest that component auditors tend to interpret lead auditor instructions concretely because they often receive detailed instructions from lead auditors. We propose that a responsibility prompt reminding component auditors to be aware of their obligations to the group audit engagement can improve their evidence collection. In two experiments, we find that our proposed responsibility prompt can effectively improve component auditors' evidence collection decisions and that this finding holds across different cultural settings. Our third experiment provides evidence that a responsibility prompt improves component auditors' evidence collection when provided to auditors who receive instructions that prime low‐level (but not high‐level) construals. Overall, our findings suggest that prompting component auditors to internalize the responsibility of a group audit engagement is a viable way to improve the quality of group audits.
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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 | 0.038 | 0.244 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".