Linking employee engagement towards individual work performance through human resource management practice: From high potential employee’s perspectives
Bibliographic record
Abstract
Most previous studies of employee engagement and performance have been rooted in either Social Cognitive Career Theory (SCCT) or the Social Exchange Theory (SET), giving often inconclusive outcomes.In fact, there are only a few researchers in social science focusing on the level of high potential employee's engagement towards individual work performance through human resource management practices.The purpose of this study is to investigate the relationship between employee engagement and individual work performance with mediation role of human resource management (HRM) practices for selected manufacturing organizations in Malaysia.Two hundred and fifty-two usable questionnaires are collected to empirically test the hypotheses using IBM SPSS software and Smart Partial Least Square (SmartPLS) version 3. The results suggest that high potential employee engagement positively and significantly influence on individuals' work performance.Additionally, human resource management practices play significant role in mediating the relationship between employee engagement and individual work performance among high potential employees.Therefore, these results should provide insight towards managing high potential employees, especially in the manufacturing sector.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".