Export Barriers Food Product Micro Small and Medium Enterprises Based Leonidou Model in Greater Malang: Literature Study
Bibliographic record
Abstract
There are 762,857 MSMEs in Greater Malang region and 60% of them are in the food sector. They have various types of innovative food products that has the potential to be exported. However, the condition of MSMEs is generally unstable because there are a lot of domestic competition with similar products that is a threat to the survival of MSMEs. Export is an opportunity to overcome this problem. The opportunity for MSME to exports their product are widely open because Indonesia has free trade agreements (FTA) with countries abroad. However, MSMEs in Greater Malang region cannot seize this opportunity. The purpose of the research is to find solutions to export barriers faced by MSMEs in Greater Malang region food products, it is hoped that overcoming these export barriers will help the MSMEs to carry out export. This study uses the Leonidou export barrier classification model, because it is a systematic and comprehensive model. The model can be expanded into several modifiers of the reality of export barriers, including: 1) previous reviewers, 2) referral sources, 3) guidance government sources MSMEs. 4) source from MSMEs. The process of identifying export barriers to MSMEs in Greater Malang region using the inquiry form is a modification, then a validity test is carried out to measure accuracy and reliability to measure the robustness of the research instrument. The data then processed using the SEM-PLS method which is a complex multivariable analysis (many constructs and indicators) simultaneously to build and test the structural equation enabler model and measurement model. The result is a model of Malang food MSME export barriers. Further, this research results are expected to be used practically by the MSMEs to develop their business towards exports. It is also expected to be utilized by the Indonesian policy makers to develop an Internationalization strategy that is more suitable for MSMEs.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".