Integration of trust supplier with supply chain capability and application towards supply chain performance: Minimarket competition during the Covid-19 pandemic
Bibliographic record
Abstract
This research aims to analyze the impact of supply chain during the Covid-19 pandemic. The study examines the integration of trust with supply chain capability and application on supply chain performance. The novelty in this research is to find out the effects of supply chain applications on supply chain performance with supply chain capability as a mediator. The research location is in East Java while sampling from mini market in cities and regencies includes Surabaya, Malang, Jember, Sumenep, Madiun and Sidoarjo. The sample size in this study consists of 240 respondents. Respondent criteria are minimarket employees who have worked for at least two years. The technique for analyzing data uses the SEM-PLS program. The results indicate that trust suppliers influence supply chain performance meaning that supplier trust for raw material supply companies must be maintained and both parties must have a good relationship with evidence of commitment and mutual trust. Second, supply chain capability also influences supply chain performance and the ability of suppliers to buyers must be maintained. Third, the relationship between supply chain application variables and supply chain performance is also positive and significant. Therefore, cooperation that can be relied upon must still be maintained together. Fourth, there is the influence of supply chain application on supply chain capability, and based on the test results the effect is very dominant, so that the variable supply chain application is the superiority of the results of this study. Fifth, there is a positive and significant indirect effect of SCA variable on SCP through SCC as mediation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".