To Hide Behind the Mask of Mandates: Disguised Opinion Shopping Under Mandatory Audit Firm Rotation and Retention in Korea
Bibliographic record
Abstract
This study investigates whether audit tenure mandates—designed to curb managerial discretion—may unintentionally enable disguised opinion shopping. Specifically, it examines whether firms benefit from observed mandates that align with their unobservable preferences, despite appearing to comply with mandatory audit firm rotation or retention rules. A counterfactual framework is developed to estimate firms’ preference for switching or retention in the absence of regulation, allowing identification of strategic alignment under constraint. Empirical analysis using Korean data from 2000 to 2009 reveals that firms classified as disguised opinion shoppers are more likely to receive unmodified audit opinions and exhibit lower audit quality, as indicated by higher discretionary accruals and more frequent reporting irregularities. These effects are concentrated under mandatory retention and not observed under rotation, suggesting that forced auditor turnover weakens firms’ ability to secure favorable outcomes. Additional evidence shows that these firms are more likely to retain the same auditor after mandates expire, consistent with a reward-for-accommodation mechanism. Thus, this study not only provides empirical evidence that opinion shopping can persist under auditor tenure mandates, but also introduces a novel method for identifying such behavior when traditional signals—such as voluntary dismissals—are unavailable. These findings inform ongoing regulatory debates on the effectiveness of tenure-based reforms.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".