THE EFFECT OF SUPPLY CHAIN COLLABORATION ON SUPPLY CHAIN PERFORMANCE THROUGH PRODUCTION TECHNOLOGY, NEW PRODUCT DEVELOPMENT, AND PRODUCT KNOWLEDGE
Bibliographic record
Abstract
Manufacturing companies are trying hard to anticipate a disrupted supply chain. Internal changes are encouraged to adapt to external conditions. Partnerships with external parties through supply chain collaboration are needed to improve supply chain performance and increase competitiveness. This research examines the effect of supply chain collaboration on supply chain performance by adopting new product development, product knowledge, and production technology. The study surveyed 148 manufacturing companies at managerial level using questionnaires. Data processing using SmartPLS software version 4.0. The results show that supply chain collaboration positively influences production technology, product knowledge, new product development, and supply chain performance. Production technology positively impacts product knowledge, new product development, and supply chain performance. The results also show that supply chain performance is influenced by product knowledge and new product development. In addition, production technology, new product development, and product knowledge mediate the indirect influence of supply chain collaboration on supply chain performance. This study contributes to enriching supply chain management theory. The practical contribution is to enlighten the company's managerial to run supply chain collaboration in generating performance and competitiveness.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".