Effect of High-Performance Human Resource Practice in Colleges and Universities in China on Teachers’ Turnover Intention: A Moderated Mediation Model
Bibliographic record
Abstract
Previous studies have demonstrated that high-performance human resource practice (HPHRP) can reduce employees’ turnover intention (TI). However, only a few studies have investigated this relationship in the context of Chinese culture. Using a sample of 740 teachers from five colleges and universities in Hebei Province, China, this study assessed the effect of HPHRP in colleges and universities on teachers’ TI in the context of Chinese culture. Furthermore, the mediating effects of teachers’ organizational commitment (OC) and the moderating effects of teachers’ organizational justice (OJ) were investigated. The results revealed that HPHRP in colleges and universities had a significant negative effect on teachers’ TI. Teachers’ OC played a partial mediating effect in the relationship between HPHRP in colleges and universities and teachers’ TI. In addition, the moderation analysis indicated that HPHRP in colleges and universities enhanced OC for teachers with high levels of OJ. This study contributed to a better understanding of the role of HPHRP in colleges and universities in reducing teachers’ TI, suggesting that HPHRP implementation and improving teachers’ OC and OJ can reduce teachers’ TI.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".