How does variable pay relate to pay satisfaction among Canadian workers?
Bibliographic record
Abstract
Purpose This study aims to determine the effect of individual and group variable pay plans on pay satisfaction among Canadian workers from six occupational groups. Design/methodology/approach Theoretical foundations rest on the discrepancy model of pay satisfaction and equity theory. Canadian national data from theWorkplace and Employee Survey(WES) were used to test the hypotheses. Findings The results show that individual and group variable pay plans act differently on workers’ pay satisfaction. For individual pay plans, being eligible for a variable pay plan, and thereby having one's performance rewarded, has no effect on pay satisfaction. Workers on variable pay plans are more satisfied with their pay only when they receive performance‐dependent payouts. In short, they want to be rewarded not only for performance but also for effort. For group pay plans, not receiving payouts has no negative effect on pay satisfaction. In contrast, receiving payouts creates pay dissatisfaction. Individual and group plans have a distinct effect on pay satisfaction by occupational group. Practical implications Managers can make informed decisions regarding the adoption of variable pay plans and their implementation. Originality/value This study sheds light on the link between variable pay and pay satisfaction. It improves our understanding of the mechanism by which variable pay affects pay satisfaction: the effort – performance – pay link (i.e. risk and perceived fairness of the allocation).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".