Performance and adoption of a PWA-based omnichannel application for MSMEs: Insights from SinariUMKM
Bibliographic record
Abstract
The rapid growth of social media use among micro, small, and medium enterprises (MSMEs) in Indonesia has created new opportunities for digital marketing. Yet, fragmented platform management remains a significant challenge. While previous studies highlight the efficiency of cross-platform integration, few have provided practical solutions tailored to MSMEs with limited technical and financial resources. The objective of this study is to develop and evaluate “SinariUMKM”, a progressive web application (PWA)-based omnichannel system designed to simplify campaign management, broadcasting, and reporting across multiple social media platforms. Using a research and development framework combined with rapid prototyping, the application was calibrated and tested in numerous stages. Black-box functional tests demonstrated that all modules, campaign, broadcasting, and reporting, performed reliably, achieving an average loading time of 3s and a transition speed of less than 1 second between pages. Survey validation involving 117 MSMEs showed high acceptance, with mean scores of 4.62 for smartphone usability, 4.62 for promotional usefulness, and 4.52 for alignment with current business trends. However, technical reliability scored slightly lower at 3.55. These findings confirm that “SinariUMKM” effectively addresses key marketing needs while remaining lightweight and affordable. The study contributes a scalable model for supporting MSME digital branding and highlights the importance of improving technical stability for sustainable adoption.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".