Auditors' Responses to Workload Imbalance and the Impact on Audit Quality*
Bibliographic record
Abstract
ABSTRACT Using detailed data for fieldwork hours and audit hours by rank from audit engagements in Korea, we examine whether audits conducted under workload imbalance, proxied by busy‐season audits, impair audit quality, and how auditors adjust staff assignments for busy‐season audits. We generally find that busy‐season audits are associated with lower audit quality, and that audit firms reduce the involvement of senior auditors during busy‐season audits. In addition, the greater the involvement of senior auditors and junior auditors, the lesser the deterioration in audit quality. Finally, although there is no increase in interim audits in response to workload imbalance during busy seasons, increasing interim audits can mitigate the negative impact of busy‐season audits on audit quality. Our results are relevant to auditors and regulators, who have expressed concerns about the adverse effects of workload imbalance on audit quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".