Client‐specific litigation risk and audit quality differentiation
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine whether client‐specific litigation risk affects the audit quality differentiation between Big N and non‐Big N auditors. Specifically, the authors examine whether higher quality audits of Big N auditors relative to non‐Big auditors is more pronounced for clients with high litigation risk than for clients with low litigation risk. Design/methodology/approach The authors develop the hypothesis based on auditors' potential monetary and reputational losses, collect the data of US listed companies from the Compustat and CRSP databases, and conduct regression analyses. Findings The authors find that the higher effectiveness of Big N auditors over non‐Big N auditors in constraining earning management is greater for high litigation risk clients than for low litigation risk clients, suggesting that clients' high litigation risk can force big auditors to perform more effectively. Originality/value This paper contributes to the literature by providing novel evidence on the effect of client‐specific litigation risk on the audit quality differentiation between Big N and non‐Big N auditors. The authors' findings complement the extant research on the relationship between the audit quality differentiation and country‐level litigation risk.
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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 | 0.009 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".