Independence Threats, Litigation Risk, and the Auditor's Decision Process*
Bibliographic record
Abstract
Abstract This study examines the effect of independence threats and litigation risk on auditors' evaluation of information and subsequent reporting choices. Using a Web‐based experiment, I tracked auditors' information gathering and evaluation leading to a going‐concern reporting decision. Specifically, 48 audit managers assessed client survival likelihood, gathered additional information, and suggested audit report choices. I found that auditors facing high independence threats (fear of losing the client) evaluated information as more indicative of a surviving client and were more likely to suggest an unmodified audit report, consistent with client preferences. In contrast, auditors facing high litigation risk evaluated information as more indicative of a failing client and were more likely to suggest a modified audit report. In addition, the association between risk and report choice was fully mediated by final information evaluation. This suggests that it is unlikely that different reporting choices resulted from a conscious choice bias, but rather that motivated reasoning during evidence evaluation plays a key role in the effect of risk in auditor decision making.
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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 | 0.034 | 0.203 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".