Innovation and supply chain orientation concerns toward job creation law in micro, small, and medium enterprises export-oriented products
Bibliographic record
Abstract
The supply chain of MSME products in the context of export penetration is very important, but MSME actors have not been able to meet all export needs. This study aims to: 1) analyze the supply chain of MSMEs in the context of export penetration in the era of the covid-19 pandemic, and 2) examine the potential of MSMEs to develop in terms of corporate legality, industrial design, and brand registration, as well as the use of digital marketing. This research method was quantitative research with survey approach and normative empirical study. The population of this study was 134 MSMEs, samples from 63 MSMEs in Central Java (Brebes), West Java (Bogor), and the Special Region of Yogyakarta (Bantul). Quantitative data analysis used Structural Equation Modeling (SEM) approach and analyzed with Smart PLS 3.3 software. The results of the study show: 1) the supply chain needs of MSMEs in penetrating the export market in the era of the covid-19 pandemic are very difficult; 2) MSMEs must be able to meet all export needs to create a balanced supply chain, 3) in order to prepare export-oriented MSMEs, they must first motivate and educate, establish policies that support their legality and export 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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".