How Does Intrinsic Motivation Improve Auditor Judgment in Complex Audit Tasks?
Bibliographic record
Abstract
ABSTRACT Intrinsic motivation is generally thought to be positively associated with performance on a variety of tasks. However, there is only sparse experimental evidence supporting this idea and we know little about the specific mechanisms behind any effect. We develop theory about how auditors’ intrinsic motivation for their jobs can improve their judgments about complex accounting estimates. We experimentally test whether a prompt to make auditors’ intrinsic motivation for their jobs salient improves the specific information processing behaviors necessary for high‐quality judgments in complex audit tasks. It does: Prompted auditors attend to a broader set of information, process information more deeply, and request more relevant additional evidence. Supplemental analyses show that these processing behaviors mediate between salient intrinsic motivation and an improved ability to identify a biased complex estimate. Our theory and analyses indicate that auditors’ intrinsic motivation for their work provides unique value for improving judgment quality, particularly in the context of performing complex audit tasks. Our study supports the view that high‐quality cognitive processing can improve auditors’ professional skepticism by providing a foundation for skeptical judgments.
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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 | 0.003 | 0.030 |
| 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) | 0.006 | 0.001 |
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".