The Benefit of Mean Auditors: The Influence of Social Interaction and the Dark Triad on Unjustified Auditor Trust
Bibliographic record
Abstract
ABSTRACT Regulators and researchers have expressed concerns that social interaction leads auditors to unjustifiably trust managers, constituting a lack of sufficient professional skepticism. Using both an abstract laboratory experiment and a contextually rich experiment with practicing auditors we predict and find that higher Dark Triad auditors (those with higher levels of the shared core between psychopathy, narcissism, and Machiavellianism) are relatively more resistant to lapses in professional skepticism due to the effects of social interaction. This is likely driven by higher Dark Triad auditors' callousness, lack of empathy, and lack of response to social stimuli. In contrast, while higher social interaction initially increases lower Dark Triad auditors' unjustified trust in managers, this effect reverses in subsequent interactions when lower Dark Triad auditors observe evidence suggesting managers have reported aggressively. These findings add to research on the effect of auditor personality traits, audit‐client social interaction, and the interaction of these two variables, and suggest that practitioners and researchers account for the interplay of Dark Triad traits and social interaction and their effect on professional skepticism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".