Developing model of logistics capability, supply chain policy on logistics integration and competitive advantage of SMEs
Bibliographic record
Abstract
This study aims to analyze the influence of supply chain policies, logistical capabilities, on logistical integration and competitive advantage in SMEs in Indonesia. The measurement method uses structural equation modeling (SEM) analysis using SmartPLS 4.0 software to analyze the influence of supply chain policies, logistical capabilities, on logistics integration and competitive advantage. The research data was obtained from distributing online questionnaires via social media. The questionnaire was designed using a Likert scale of 7. The respondents used in this study were SMEs owners who were determined through simple random sampling. The online questionnaire was distributed to 490 UKM owners. The stages of data analysis are validity test, reliability test and significance test or hypothesis test. Based on the results of data processing carried out, it was found that supply chain policy has a positive effect on logistical integration, logistics capability has a positive effect on logistics integration, supply chain policy has a positive effect on competitive advantage, logistics capability has a positive effect on competitive advantage, logistics integration has a positive effect on competitive advantage competitive. The novelty of this research is the relationship model of logistics capability and supply chain policy on logistics integration and competitive advantage in SMEs organizations. The theoretical implication of this research is to support previous theories that logistics capability and supply chain policy play a role in encouraging increased logistics integration and encouraging increased competitive advantage in SMEs organizations. The practical implication of this research is the management of SMEs to implement logistics capability and create and implement supply chain policies to encourage increased logistics integration so that it will increase competitive advantage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".