The Effect of Incentive Framing and Descriptive Norms on Internal Whistleblowing
Bibliographic record
Abstract
Abstract Firms are under increasing pressure to implement effective internal whistleblowing systems. While firms can provide incentives to encourage internal whistleblowing, it remains controversial how such incentives should be structured. We examine whether the effectiveness of incentives encouraging internal whistleblowing is a joint function of the framing of such incentives (reward or penalty) and the strength of descriptive norms supporting internal whistleblowing. We predict and find in a lab experiment that penalties lead to a greater increase in internal whistleblowing (compared to rewards) when descriptive norms supporting whistleblowing are stronger. Our study contributes to the previous accounting literature on dishonesty and the role of management control systems design in promoting honesty and ethical norms in organizations. We also contribute to an emerging accounting literature on the links between controls, norms, and individual behavior by distinguishing between descriptive norms and injunctive norms and by highlighting the interplay between these two types of norms. Our results have important implications for organizations considering adopting incentives to encourage internal whistleblowing.
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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 | 0.016 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".