The effect of digital marketing, digital finance and digital payment on finance performance of Indonesian SMEs
Why this work is in the frame
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Bibliographic record
Abstract
The purpose of this study is to analyze the effect of digital finance, digital marketing and digital payment variables on finance performance. This study uses quantitative methods and data analysis techniques is performed based on Structural Equation Modeling using SmartPLS 3.0 software. The method of selecting the sample using the snowball sampling methods. Online questionnaires were sent to 190 SMEs respondents in the province of Banten Indonesia and evaluated the returned questionnaires. The results of data analysis show that the digital finance had a positive and significant effect on the finance performance, the digital payment had a positive and significant effect on the finance performance and the digital marketing had a positive and significant effect on the finance performance. The findings of this research can provide benefits for MSME actors in developing their business to improve business performance, by paying attention to aspects of MSME digitization and financial literacy of MSME entrepreneurs. Keep in mind, the important role of information technology in business activities requires entrepreneurs to improve their digital literacy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it