The effect of supplier integration, manager transformational leadership on supply chain performance
Bibliographic record
Abstract
This study aims to examine the effect of supplier integration on supply chain performance by moderating supply chain transformational leadership styles. The design used in this study is hypothesis testing with two hypotheses and using path analysis. The population of the respondents from this study were 550 manufacturing companies in Jakarta with data collection were performed through online questionnaires and the number of samples that met the criteria for analysis from 150 manufacturing companies represented by leaders in the supply chain management section. The results of this study indicate that supplier integration has a positive effect on supply chain performance, transformational leadership style has a positive effect on supply chain performance and supplier integration has a positive effect on transformational leadership style in Indonesian manufacturing companies. The results of the study can be a reference for decision makers and supply chain management leaders to implement supply chain management strategies in the form of integration with suppliers to improve the company's supply chain performance and to consider the influence of supply chain transformational leadership styles to maintain the sustainability of long-term relationships with suppliers in multinational companies which already has a standard and standard system.
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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 | 0.009 |
| 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 | 0.001 |
| 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".