Strategy to Strengthen Unumart's Performance through the Role of Digital Payment Optimization in the First Quarter of 2025
Bibliographic record
Abstract
Digital transformation emphasizes digitalization as a strategy for enhancing efficiency and competitiveness. The shift to tech-based payment systems, like QR Codes and e-wallets, simplifies transactions, boosting operational efficiency and potential sales for businesses. This study aims to empirically test the effect of Digital Payments (QRIS) on UNU Mart Performance (Sales). This study is a causal research with quantitative methods. The examples obtained through purposive sampling techniques, from 90 data only 70 can be processed during the first 3 months of 2025, using primary data from the UNU Mart cashier application. In this study, SPSS statistical software version 24 was used. The results of the study showed that the variables taken, namely QRIS on Sales, were proven to have a positive and significant effect. In addition, seen from the multiple correlation, more than 50% of QRIS variables affect Sales.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".