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 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 | 0.029 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".