How the Integration of Payment Systems Through QRIS Accelerates Economic and Financial Cooperation in the ASEAN Region
Bibliographic record
Abstract
This study aims to analyze the opportunities, challenges, strengths, and weaknesses of payment systems, as well as the strategies for implementing regional payment connectivity through QRIS as an alternative for integrating payment systems in the ASEAN region.The research employs an exploratory mixed-methods approach, utilizing SWOT analysis and linear regression.Data sources include secondary data, in-depth interviews, and focus group discussions with 85 respondents from Bank Indonesia, local governments, academics, ASEAN tourists, and MSME actors across Batam, Yogyakarta, and West Nusa Tenggara.The findings indicate that QRIS has a positive impact on the economies of ASEAN countries.As part of the ASEAN financial system, QRIS demonstrates strong characteristics due to its well-balanced strengths and weaknesses in trade transactions.As a result, opportunities and threats have become the government's focus in enhancing trade interactions across the region.Furthermore, QRIS serves as a viable alternative for integrating payment systems in ASEAN, particularly in the trade, MSME, and tourism sectors.To maximize its potential, policymakers should enhance cross-border regulatory frameworks, promote financial literacy among MSMEs, and strengthen digital infrastructure to support seamless transactions across ASEAN countries.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".