Why Bonuses Promote Deviant Behaviors: A Self‐Determination Theory Perspective
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Bonuses are notoriously used to motivate workers. How effective are they at doing so, and might there be unintended consequences? We investigated the effectiveness of bonuses based on game profitability on the motivation of video game developers by examining specific bonus characteristics that align with advice derived from expectancy theory. We also investigated if bonuses encouraged or discouraged moral engagement and corner‐cutting behavior through their effects on work motivation as conceptualized through self‐determination theory. Company data on bonus characteristics coupled with surveys from 1024 game developers in a video game company indicated that the size of the last received bonus did not influence current work motivation. Uncertain probability of getting the next bonus installment was related to a lack of motivation, while more certain probability was related to higher intrinsic motivation. The probable size of the next bonus was related to lower external regulation and to higher intrinsic motivation, contrary to predictions from most motivation theories. Both probability certainty and bonus size probability were indirectly negatively associated with moral disengagement and corner‐cutting behaviors via decreasing amotivation and external regulation. Overall, results show that the bonus system in this company had limited effects on motivation and on discouraging deviant behaviors and point to how such systems can be improved using advice from theory.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it