Industry and Firm-level Determinants of Employment Relations in China: A Two-level Analysis
Bibliographic record
Abstract
Factors influencing the adoption of human resource management (HRM) policies and practices are nested in multilevel context of firm and industry. Institutional theory focuses on environmental pressure and suggests that organizations’ choice of HRM systems is partly attributable to mimetic isomorphism. Drawing on different theoretical perspectives, this study examines the multilevel environmental and organizational contingencies as determinants of HRM systems and tests their effects on the use of short-term labor contracts, contract duration, training and employee involvement programs, using a survey of 313 manufacturing plants in China. Our analysis using a hierarchical linear model shows that while most of the variance in HRM systems occurred at the firm level, approximately 5 to 7 percent of the total variance in the four HRM policies are explained by industry level factors. Particularly, findings suggest that international competitive pressure, capital intensity, firm size, unionization and ownership type have significant effects on manufactures’ use of labor contracts. However, for training programs, only capital intensity and firm size are significant positive predictors; for employee involvement programs, only firm size and ownership are significant determinants.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".