Motivated Perspective Taking: Why Prompting Auditors to Take an Investor's Perspective Makes Them Treat Identified Audit Differences as Less Material*
Bibliographic record
Abstract
ABSTRACT Audit regulators and commentators propose prompting auditors to more fully take an investor's perspective as a remedy to their concern that auditors underreact to material misstatements. By contrast, we predict that prompting auditors in this manner will backfire, making them less (more) heavily weight indicia that misstatements are (not) material. We further predict auditors will apply this asymmetric weighting instrumentally—to a greater degree as needed—to justify management‐preferred conclusions. We test these predictions in two experiments in which in‐charge audit seniors judge the likelihood that identified audit differences are material and choose required adjustment amounts. Between‐participants, we manipulate whether or not auditors are prompted to take an investor's perspective and, within‐participants, whether these audit differences would or would not violate a qualitative criterion—by breaking or not breaking a favorable profitability trend. Study 1 uses a context in which a relatively low degree of motivated perspective taking is needed, as the audit difference is just below tolerable misstatement (TM). Investor‐prompted auditors assess audit differences as less likely to be material than do unprompted auditors, but only when the qualitative criterion is not violated. Study 2 adds a between‐participant manipulation of misstatement tolerability—that is, whether the audit difference is just below or well above TM. Consistent with an instrumental increase in motivated perspective taking, investor‐prompted auditors assess audit differences that simultaneously are less tolerable and violate a qualitative criterion as significantly less likely to be material. Overall, our theory and experimental evidence suggest prompting auditors to take the investor perspective may have unintended consequences.
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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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".