Cross-Border Banking Services and Determinants of Bank Selection from Corporate Customer’s Perspective: Evidence from Vietnam
Bibliographic record
Abstract
This paper focuses on assessment of cross-border services in the Vietnam banking system in the context of international integration from customer’s perspective. The authors used Exploratory Factor Analysis (EFA) in SPSS from survey of 153 corporate clients of Vietnamese banks in 2018 to evaluate the current situation of offering cross-border services as well as competitiveness of banking system in Vietnam. Research results show that from customers’ perspective three key factors relating to marketing policy, infrastructure and financial capacity of banks are the most important factors in selection of using cross-border banking services in Vietnam. Survey results show that competitiveness of Vietnamese banks is reaching medium level but much lower than the expectation of customers. Therefore, the authors propose to Vietnam banking system some recommendations including: (i) Improving the services’ quality, especially in terms of technology and depth of cross-border banking services; (ii) Focusing more on customer care activities by developing more useful applications on smartphones, tablets and computers, create the linkage among banks and end-users, develop digital marketing instead of traditional marketing methods; (iii) Maintaining domestic market as well as finding new markets abroad, especially in the ASEAN countries, in order to gradually increase the diversification as well as the quality of cross-border financial and banking services.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".