Policy Framework to Improve MSME Competitiveness and Financial Performance with Indonesia’s Asta Cita Vision Goals
Bibliographic record
Abstract
Micro, small, and medium enterprises (MSMEs) are recognized as the cornerstone of Indonesia’s economy, especially in the agriculture, fisheries, and tourism sectors. Given Asta Cita’s ambitious vision for the country, which emphasizes inclusive and sustainable development, MSMEs are under increasing pressure to improve their competitiveness and financial performance. This research aims to develop and empirically evaluate a comprehensive policy framework that identifies digitalization, sustainable development, and innovation as the primary catalysts for MSME progress, with government support as a mediating variable, grounded in dynamic capabilities and institutional theories. A quantitative methodology was used to collect primary data from 435 MSME respondents in North Sulawesi, which was then analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings show that digitalization, sustainable practices, and innovation have a substantial, positive impact on the financial performance of MSMEs. However, government support cannot mediate the influence of digitalization, sustainable development, and innovation on improving economic performance. This shows that internal organizational competencies are more important than external interventions in achieving financial success. The results of this study underscore the need for MSMEs to prioritize technology integration, incorporate sustainability into their business frameworks, and continue innovating to maintain resilience and competitiveness.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".