Linking HR practices to employee engagement: A mediated-moderated model of self-efficacy and supervisory support
Bibliographic record
Abstract
This study aims to investigate the role of human resource practices (HRP) in enhancing employee engagement (EE), focusing on the mediating effect of self-efficacy (SE) and the moderating influence of supervisory support (SS). Anchored in Social Exchange Theory (SET) and the Job Demands-Resources (JD-R) model, the study seeks to explore the mechanisms through which HRP contribute to a more engaged workforce within the Chinese organisational context. A quantitative research design was adopted using a structured questionnaire distributed to employees working in various Chinese companies. Data were collected from 412 respondents and analysed using SmartPLS 4 to examine the direct, mediating, and moderating relationships among the variables. The results confirm that HRP has a significant positive impact on EE. Furthermore, SE significantly mediates the relationship between HRP and engagement, while SS strengthens the positive association between SE and engagement. Additionally, a moderated mediation effect was observed, indicating that the indirect impact of HRP on engagement through SE is more substantial when SS is high. This study advances the understanding of EE by introducing a moderated mediation framework that highlights the synergistic roles of HRP, psychological empowerment, and leadership support. Practical and theoretical implications are presented for organisations seeking to develop sustainable engagement strategies.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".