Hijacking the Moral Imperative: How Financial Incentives Can Discourage Whistleblower Reporting
Bibliographic record
Abstract
SUMMARY Recently, policy makers have focused significant attention on the use of financial rewards as a means of encouraging whistleblower reporting, e.g., the Dodd-Frank Act (U.S. House of Representatives 2010). While such incentives are meant to increase the likelihood that fraud will be reported in a timely manner, the psychological theory of motivational crowding calls this proposition into question. Motivational crowding warns that the application of financial rewards (an extrinsic motivator) can unintentionally hijack a person's moral motivation to “do the right thing” (an intrinsic motivator). Applying this theory, we conducted an experiment and found that, in certain contexts, incentive programs can inhibit whistleblower reporting to a greater extent than had no incentives been offered at all. We discuss the implications of our results for auditors, audit committees, regulators, and others charged with corporate governance. Data Availability: Available from the authors upon request.
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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 | 0.014 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".