The effect of digital marketing, digital finance and digital payment on finance performance of Indonesian SMEs
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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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".