Impacts of COVID-19 on Digital Financial Transformation: Insights from Consumer Behaviors in Vietnam
Bibliographic record
Abstract
The usage of e-commerce platforms and digital financial services in Vietnam surged dramatically during COVID-19. An important question for the financial service industry is whether and how the surge will translate into long-term industry reconfigurations. Current literature on digital financial services, which has typically focused on changes driven by corporate stakeholders and investors, provides little room to consider the role of customers. Our proposed chapter fills this gap by integrating conceptual and empirical ideas from service quality research, focusing on customer perceptions and behaviors. Using empirical survey data, we examine whether and how customer behavioral changes during COVID-19 impact digital financial transformation in Vietnam. We further combine survey findings with three case studies of digital transformation to evaluate whether customer behavioral changes are in sync with relevant corporate transformation strategies. By connecting two sources of changes, our work will help investors, managers, and policymakers envision long-term industry reconfigurations in the post–COVID-19 era.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".