Analysis the Influence of Non-Cash Payment to Output (Case Study: Selected ASEAN Countries)
Bibliographic record
Abstract
This study aims to analyze the effect of non-cash payments to output, and the case study is in 3 selected ASEAN countries such as Indonesia, Malaysia and Vietnam. This study will analyze how the effect of non-cash payments consisting of credit card transactions and electronic money transactions on output in Indonesia, Malaysia and Vietnam. The data analysis technique used in this research is a quantitative method with panel data by using Stata 14.2 version software program. The data used are credit card transactions, electronic money transactions and GDP per capita for the 1st to 4th quarter of 2018-2021 period in the 3 selected ASEAN countries. Based on the results of this study, credit card transactions have a negative effect on GDP per capita in Indonesia, Malaysia and Vietnam and electronic money transactions have a positive effect on GDP per capita in Indonesia, Malaysia and Vietnam. \n \nKeywords: Credit Card, Electronic Money, GDP per capita
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".