The effect of high-involvement human resource management practices on supply chain resilience and operational performance
Bibliographic record
Abstract
Supply chain (SC) resilience is an increasingly important topic for practitioners and academics because it is a competitive weapon for firms to cope with SC disruptive risks. This study examines the impact of high-involvement human resource management practices on SC resilience from the ability-motivation-opportunity perspective. It also examines the relationship between the dimensions of SC resilience and operational performance. Based on data collected from 206 Chinese manufacturers, the proposed hypotheses were tested using structural equation modeling. The results indicated that employee participation played the most powerful role in improving supplier, customer, and internal resilience. Moreover, employee skills only facilitate internal and customer resilience but have no significant impact on supplier resilience. By contrast, employee incentives do not influence the dimension of SC resilience. It was also found that both internal and customer resilience have positive effects on operational performance, while supplier resilience has no significant effect. The findings contribute to literature and practice.
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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 | 0.005 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".