The Influence of Mood on Subordinates’ Ability to Resist Coercive Pressure in Public Accounting
Bibliographic record
Abstract
Abstract This study reports on an experiment conducted to assess the influence of different affective mood states on auditors’ ability to resist obedience pressure to commit or overlook unethical acts in six audit contexts. Obedience pressure from superiors to comply with unethical directives is of particular concern in public accounting, given the hierarchical structure of audit teams and the power imbalance in superior–subordinate relationships. One hundred and seventy audit seniors from two large international public accounting firms participated in an experiment. Three different moods were induced in participants through work‐related trigger events: one positive active mood state (arousal) and two negative passive mood states (fear and insignificance). These mood states were anticipated to influence auditors’ expressed willingness to comply with their superiors’ unethical directives as set forth in our ethical scenarios. Our results indicate that low levels of arousal and high levels of fear and insignificance influenced compliance intentions. Our results also indicate overall high levels of expressed willingness to comply with superiors’ unethical directives. Implications of our findings for understanding the antecedents of unethical conduct within the accounting profession and for future research are discussed.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".