Why Bonuses Promote Deviant Behaviors: A Self‐Determination Theory Perspective
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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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 | 0.005 | 0.016 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".