Embrace Fintech in ASEAN: A Perception Through Fintech Adoption Index
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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