The Impact of Risk and the Potential for Loss on Managers' Demand for Audit Quality*
Bibliographic record
Abstract
ABSTRACT This study uses experimental economic markets to investigate the impact of risk and the potential for loss on managers' demand for audit quality. We posit that these two important contextual factors influence managers' audit quality preferences. We study these factors because they are ubiquitous to companies, and we focus on their influence on managers because managers continue to play a significant role in the auditor hiring process and we know relatively little about their auditor preferences. We predict that risk, the potential for loss, and their interaction will each decrease manager demand for high audit quality due to a desire to achieve greater reporting flexibility. Experimental results are consistent with our predictions; specifically, increased risk, the potential for loss, and to a lesser extent their interaction, significantly reduce managers' likelihood of hiring the best available auditor in the market. Path analysis indicates that this reduction in audit quality demand leads to increases in misreporting. Finally, we observe investors overpaying for assets to a greater extent when managers hire lower‐quality auditors. Our results show that the contextual factors of risk and the potential for loss, which are ubiquitous to companies, can reduce demand for audit quality, which can increase misreporting behavior and ultimately harm investors.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".