The Effects of Multitasking on Auditors’ Judgment Quality
Bibliographic record
Abstract
Abstract Auditors must frequently multitask in order to complete their work efficiently. However, the potential impact of multitasking on auditors’ judgment quality is poorly understood. Using Ego Depletion Theory and a laboratory experiment, we predict and find that auditors become less able to identify seeded errors after multitasking, and that this effect is most prominent in the identification of conceptual, rather than mechanical, errors. These negative consequences of multitasking are mitigated when auditors are exposed to an intervention based on a theoretical countermeasure of replenishing depleted self‐control resources, in that multitasking auditors identify more seeded errors with the intervention than without. Given that multitasking is a pervasive feature of the current audit environment, these findings have direct implications for audit practice. Beyond identifying multitasking as a cause of impaired performance in auditing, this study's results provide initial evidence that such negative effects can be mitigated, resulting in improved audit quality and, by extension, improved financial statement 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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".