Micro, small, and medium enterprises (MSMEs): The emerging market analysis
Bibliographic record
Abstract
This study aims to analyse the factors affecting the Micro, Small, and Medium Enterprises in the province of South Sumatra, Indonesia. Data of 100 MSMEs were collected through questionnaires in the 15 regencies/cities in South Sumatra. The statistical analysis used was Structural Equation Modelling (SEM) processed through AMOS. The results evidence that the external factors of capital support, business partners, and infrastructure directly have no direct effects but indirectly affect the performance of MSMEs in South Sumatra. Also, the availability of resources and environmental conditions; and the capability of business owners and employees indirectly affect the performance of MSMEs in South Sumatra. Lastly, the use of technology and research impact the performance of MSMEs in South Sumatra directly and indirectly through the availability of resources and environmental conditions and business owners and employees' capability. Theoretically, this study expands the MSMEs literature by discussing factors (i.e., external and internal) affecting MSMEs' performance holistically. Practically, this study is beneficial for the government, practitioners, and policymakers.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".