Using Mentorship and Codes of Conduct to Support Individuals’ Internal Motivation to Report Honestly
Bibliographic record
Abstract
Despite extensive investment in organizational controls to prevent dishonesty, fraud statistics continue to rise. We take a different approach, one designed to promote honesty. Building on Self-Determination Theory (Ryan & Deci, 2000) and set in the context of reporting accounting information, we examine how codes of conduct and mentor relationships can be used to leverage employees’ internal motivation to report honestly, in two experiments with managers. Experiment 1, set in a solo-reporting setting, manipulated code autonomy support (high/low) and mentor relatedness support (high/low), measured participants’ trait-like internal motivation to report honestly and assessed their effects on honest reporting behavior. We found that individuals with low motivation to report honestly were more likely to report honestly when codes convey low (versus high) autonomy support. Experiment 2, set in a group-misreporting setting, manipulated code autonomy support (high/low), measured participants’ internal motivation to report honestly, and assessed their effects on refusal to join group misreporting. We found that individuals with high motivation to report honestly were more likely to refuse to join others to misreport when codes convey high (versus low) autonomy support. Our results suggest managers tailor these anti-fraud controls to fraud risk and employees’ motivation.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".