Fintech and Financial Health in Vietnam during the COVID-19 Pandemic: In-Depth Descriptive Analysis
Bibliographic record
Abstract
The growing popularity of smartphones and the proliferation of technology have accelerated the development of the digital payment industry. Fintech enables customers to access financial services more efficiently and faster than traditional business, especially during the COVID-19 pandemic due to health protocols, including restrictions on physical contact. This study investigates financial literacy, fintech adoption, and the impact of the COVID-19 crisis on the financial health of consumers in Vietnam. The relatively higher level of the unbanked population in Vietnam and the lower level of adult financial literacy compared with the ASEAN region motivated this study. Based on judgment sampling, participants were approached using the mall intercept technique, and those familiar with fintech were selected for the research interview. Thirty participants were interviewed and were given a survey form to be filled online using their mobile phones. Data analysis was conducted using IBM SPSS software version 23. Perceived ease of use, perceived usefulness, trust, brand image, government support, user innovativeness, and attitude are found to be significantly correlated with fintech adoption in Vietnam, while financial literacy was found to be not significantly correlated with fintech adoption. Furthermore, further analysis using multiple linear regression revealed user innovativeness and attitude have a positive impact towards fintech adoption, and in contrast, financial literacy showed significant negative impact on fintech. This inverse relationship could indicate that in Vietnam, fintech may play a role of bringing financial inclusion where people with lower financial literacy are able to use technology for financial transactions, which was previously inaccessible to them. This could also mean that Vietnamese with higher financial literacy do not see fintech as an important tool for their financial transactions, as they may already have strong access to traditional financial facilities. This research contributes to knowledge in the field of Fintech adoption in Vietnam at the time of the COVID-19 outbreak. To foster greater financial inclusivity and access for the Vietnamese consumers, policy makers could promote the development of fintech business infrastructure and regulatory sandboxes to foster fintech startups.
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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 | 0.001 | 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) | 0.002 | 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".