Adoption of Digital Technology and Financial Knowledge: Strategies for Achieving Sustainable Performance of MSMEs
Bibliographic record
Abstract
Micro, small and medium enterprises (MSMEs) contribute significantly to Indonesia’s economic growth. In an increasingly digitalised era, MSMEs face challenges and opportunities that affect their performance. Technology adoption will have an impact on operational efficiency and ease of transactions, providing added value for consumers. Meanwhile, good financial management depends on the level of financial literacy and inclusion of MSME players. This study aims to examine the factors that influence the sustainable performance of MSMEs from the aspects of technology adoption and financial knowledge. The independent variables include automation, digital payments, financial inclusion and financial literacy, and the dependent variable is MSME performance. This study uses primary data in the form of questionnaires, and data processing uses SEM-PLS. Statistical test results show that the variables of business automation and financial literacy have a positive effect, while the variables of digital payments and financial inclusion have no effect. The results of the study show that financial literacy is an important key to MSME performance and the importance of business automation that affects efficiency through technology. The results of this study are expected to provide useful recommendations for MSME actors and policymakers in formulating strategies to improve the competitiveness of MSMEs.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".