Employing Quantile Regression for Influences of Human Resource Management on Employee Performance
Bibliographic record
Abstract
The current study has employed the regression of quantile to explore the impacts of human resource management practices on employee performance at enterprises in business. The research data was collected in Vietnam as a developing economy. The empirical results offer a quite comprehensive picture of the causal linkages from the practices of human resource management to employee performance in emerging economies. These complex linkages have been explored at different quantiles of the conditional distribution of employee performance. The findings reveal that at different points of the conditional mean of employee performance, the effects of human resource management practices are different. The current work is helpful to researchers and business directors, especially in emerging countries like Vienam, by providing them with a more comprehensive picture of the multifaceted links from the practices of human resource management to employee performance. Accordingly, they are able to make better business decisions on the implementation of suitable human resource management. Finally, their enterprises can achieve better employee performance, which in turn leads to superior firm performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".