Embrace Fintech in ASEAN: A Perception Through Fintech Adoption Index
Bibliographic record
Abstract
In this new age of financial technology developments in ASEAN, the financial services industry is evolving quickly. However, consumer intention to embrace financial technology in different financial services remains vague. Hence, this study aims to investigate the consumer Fintech adoption level through constructing a Fintech Adoption Index for ASEAN countries. The empirical findings reveal that Singapore with a mature Fintech development having a relatively high adoption rate, while countries with nascent Fintech development such as Brunei Darussalam, Cambodia, Myanmar and Laos have a relatively low adoption rate as compared to the countries with emerging Fintech development such as Indonesia, Malaysia, Philippines, Thailand and Vietnam. All ASEAN countries show increasing trends in Fintech adoption from 2017 to 2019. From this study, the dimensional and final index scores generated are easy to understand, and this study has successfully simplified the complexity of Fintech adoption level across different sub-sectors for all ten ASEAN countries. In conclusion, the newly constructed Fintech adoption index for ASEAN countries can better illuminate consumer adoption preference toward Fintech development and thus leverage the results for productive financial policy direction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".