Applying self‐determination theory to understand the motivational impact of cash rewards: New evidence from lab experiments
Bibliographic record
Abstract
We investigated, based on self-determination theory (SDT), the impact of the functional meaning of monetary rewards on individuals' motivation and performance and further tested the role of the psychological needs as the underlying mechanism. In two experimental studies, we show that when presented in an autonomy-supportive way, rewards lead participants to experience greater intrinsic motivation, which leads them to perform better, than when monetary rewards are presented in a controlling way. This is mediated by greater psychological need satisfaction, indicating that through greater feelings of competence, autonomy, and relatedness, individuals experience greater intrinsic motivation for the task at hand. Our findings suggest that rewards can have a distinct effect on individuals' motivation and performance depending on whether they take on an autonomy-supportive or controlling meaning, thus providing empirical evidence for the theoretical and practical implications of SDT's concept of functional meaning of rewards. By highlighting the importance of this concept, this research contributes to our understanding of the effectiveness of such rewards in the workplace, suggesting that they can foster employee motivation and performance if organisations present them to employees in an autonomy-supportive way to convey an informational meaning and positively contribute to their psychological need stisfaction.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".