The effects of smart human resources 4.0 on employee job effectiveness: The mediating role of employee job satisfaction
Bibliographic record
Abstract
The aim of this study is twofold. First, to explore the effect of smart human resources (HR) 4.0 practices on employee effectiveness. Second, to investigate the mediating role of employee job satisfaction in the relationship between these two latent variables. Distributing a questionnaire to gather data from a sample of HR managers and employees in Ministry of Digital Economy and Entrepreneurship, the results point out that smart HR 4.0 practices as a whole construct represent a significant predictor of employee job effectiveness as measured by employee performance based on their personal, social, methodological, and technical skills. As well, the results revealed that smart HR 4.0 embodies a significant predictor of employee job satisfaction. The results found a significant effect of the latter on employee job effectiveness, a significant mediating role of employee job satisfaction was established. The study provides a theoretical basis for further studies on such effects as well as an empirical ground from which companies could start to boost employee satisfaction and effectiveness through smart HR 4.0 technologies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".