The mediating role of technology and logistic integration in the relationship between supply chain capability and supply chain operational performance
Bibliographic record
Abstract
The purpose of this paper is to explore the link between supply chain capability and supply chain operational performance.In addition, the current study investigates the mediating role of technology integration and logistic integration between supply chain capability and supply chain operational performance.The firms in Tin industry of Indonesia are chosen as the sample of the study.To achieve the objectives of the current study, structural equation modeling is used using smart PLS.Data is collected through mail and telephonic survey.The responses are collected through the postal and electronic mail, questionnaire survey.According to the direct results, it is shown that all hypotheses were meaningful (α = 5%).The mediation effect of technology integration and logistic integration in the relationship between Supply Chain Capability and Supply Chain Operational Performance (SCOP) was examined.The results of mediation show that for logistic integration mediation hypothesis, the results was meaningful (α = 5%), whereas for the technology integration the results was not significant.The results of the study are useful for policymakers, practitioners, operation managers in understanding the link between human resource management and operational management.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".