Human Resource Practices, Job Satisfaction and Perceived Discrimination(s) at the Workplace
Bibliographic record
Abstract
This research contributes to the debate in the human resources management (HRM) literature by examining the impact of some HRM practices on workers’ overall job satisfaction and the determinants of workers’ perception of discrimination. The novelty of our study consists in the deepening of the relation between HRM practices and the employees’ perception of discrimination in workplace: a largely unexplored topic, until now. Our aim is to add value to existing literature by assessing the synergy effect between perception of discrimination and HRM practices on workers’ job satisfaction, performing a probit regression analysis of a selection of variables drawn from the sixth wave of European Working Condition Survey data, collected in 2015. We also provide a comparison of different types of discrimination, examining the moderating effect of the perception of discrimination on the relationship between HRM practices and employees’ job satisfaction, assuming that the strength of the above relation is weaker for discriminated workers. Our findings highlight that HRM practices we analysed (except for autonomy of the work-group and job-intensity) have a positive impact on workers’ satisfaction and reduce the perception of discrimination. Moreover, we find that the perception of every kind of discrimination have a negative impact on workers’ job satisfaction. Our results also suggest that the perception of discrimination has a moderator role in the relation between HRM practices and job satisfaction. Policy implications are finally discussed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".