Money talks? The motivational mechanisms of base pay on well‐being and work performance
Bibliographic record
Abstract
Whether compensation serves as a motivational resource for employees is still a debated subject. It has been suggested that the effect of pay on motivation could be contingent on the satisfaction of the basic psychological needs as outlined by self-determination theory. The current research explored the role of amount of base pay as well as pay fairness discrepancy in relation to basic psychological need satisfaction, autonomous work motivation and, in turn, psychological well-being (i.e., vigor and emotional exhaustion), turnover intentions, and work performance. Managerial need support was also included in the model, so its mechanisms and its effects on the outcomes could be measured against those of pay. Using a combination of archival data and employees' self-reports from 593 Norwegian workers, results revealed that amount of base pay did not predict satisfaction of any of the basic needs, pay fairness discrepancy negatively predicted satisfaction of the needs for autonomy and relatedness and positively predicted satisfaction of the need for competence, whereas managerial need support significantly predicted satisfaction of all three needs. While there was no significant direct relation from amount of base pay to any of the outcomes, results showed a significant direct relation from managerial need support to psychological well-being (positive to vigor and negative to emotional exhaustion) and turnover intentions (negative). There was also a significant direct positive relation from pay fairness discrepancy to turnover intentions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".