The Directional Effects of Discussion on Auditors' Moral Reasoning*
Bibliographic record
Abstract
Abstract Auditors' professional judgements are typically made following a discussion of contentious issues with other auditors (Gibbins and Mason 1988). These discussions may be with others at various levels in the hierarchy of the audit firm, with informal discussion with peers often taking place prior to formal discussions with audit supervisors (see, e.g., Solomon 1987). This study uses an experiment, involving 286 public accountants, to consider how discussion with peers may influence auditors' subsequent resolution of realistic audit‐specific moral dilemmas. Auditors were asked to prescriptively discuss how an accountant ideally should resolve a moral dilemma, or to deliberatively discuss how an accountant actually would resolve a moral dilemma. The results showed that auditors have higher moral reasoning scores after prescriptive discussion with peers and lower moral reasoning scores after deliberative discussion with peers. Thus, the study findings point to the significance of discussion of contentious dilemmas with peers and the importance of type of discussion for predicting and explaining auditors' moral reasoning. More specifically, the results indicate that discussion with peers may provide information and/or signal what is important and acceptable to the resolution of a moral dilemma, which facilitates transformation of an auditor's moral reasoning. This suggests the importance of informal mechanisms, such as peer discussion, as part of the social control system in audit firms.
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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 | 0.017 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".