Include, care, reward and enhance to build a well-being HRM system based on perceived effectiveness and fairness of HRM practices
Bibliographic record
Abstract
Purpose One of the criticisms that can be addressed to the existing HRM literature is that performance is often the primary target, leaving well-being as a secondary consideration. This study aims to put employee well-being at the center of HRM concerns. By focusing on needs-supply fit and social exchange theories, our approach focuses on employees’ perceptions of the effectiveness and fairness of HRM practices. Design/methodology/approach Based on a sample of 740 workers collected via an electronic survey, HRM practices were grouped into bundles using factor analysis to form an HRM system. The impact of the HRM system and its bundles on employee well-being and job performance was analyzed using structural equation modeling (SEM). The mediating role of well-being was tested with Stata’s medsem package. Findings The HRM system and its bundles (Include, Care, Reward and Enhance) derived from the perceived effectiveness and fairness of HRM practices have a positive direct effect on employee well-being and a positive indirect effect on job performance through the mediating role of well-being. However, the bundles have no direct effect on job performance, highlighting the importance of integrating employee well-being into HRM concerns. Originality/value These findings reveal that when employees consider HRM practices to be fair and effective, it promotes their well-being, which has a positive impact on their job performance.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".