Developing model of logistics capability, supply chain policy on logistics integration and competitive advantage of SMEs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it