Innovation network for micro, small and medium enterprises in Indonesi
Bibliographic record
Abstract
This study aims to develop an innovation network model to help MSMEs recover from the downturn due to the pandemic. The basic model used is an innovation network consisting of associations, suppliers, customers and government. The basic model was developed by adding capital and digitalization variables to suit the needs. Data processing uses a structural equation model and the sample is MSMEs in West Java province. This study is the first on the innovation network model for MSMEs affected by the pandemic and the largest number of MSME respondents. The results conclude that according to MSMEs in Indonesia, suppliers, customers, government, associations, capital, and digitalization have an effect on the innovation network. This study concludes that associations, suppliers, customers, government, capital and digitalization all have a positive effect on the innovation network. This finding is the first in the innovation network model for MSMEs that accommodates the needs of the industry to recover from the pandemic situation and adds new literature on industrial innovation networks. While previous studies have a lot of similar literature, all focus on certain types of businesses and on normal economic conditions. This study is different from similar studies because it was conducted on MSMEs in all business sectors and economic conditions in crisis due to the pandemic. These results can be used as a reference in decision making to increase the growth of MSMEs with limited resources.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".