The Power of Workplace Rewards: Using Self-Determination Theory to Understand Why Reward Satisfaction Matters for Workers Around the World
Bibliographic record
Abstract
How can workplace rewards promote employee well-being and engagement? To answer these questions, we utilized self-determination theory to examine whether reward satisfaction predicted employee well-being, job satisfaction, intrinsic motivation and affective commitment, as well as valuable organizational outcomes, such as workplace contribution and loyalty. Specifically, we investigated the role of three universal psychological needs—autonomy, competence and relatedness—in explaining whether and why reward satisfaction matters for employees’ well-being. We tested our model in a large, cross-sectional study with full-time employees working for multinational corporations in six main world regions: Asia, Europe, India, Latin America, North America and Oceania ( N = 5,852). Consistent with our theorizing, we found cross-cultural evidence that reward satisfaction promoted greater employee functioning through psychological need satisfaction, contributing to better organizational outcomes. Critically, our results were consistent regardless of geographic location. As such, this study provides some of the strongest evidence to date for the power of understanding psychological mechanisms in the workplace: Regardless of the actual rewards that employees received, how workplace rewards made employees feel significantly predicted their optimal functioning.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".