Green Banking Development: A Case Study of Vietnam
Bibliographic record
Abstract
Purpose: The objectives determine factors affecting green banking development in Vietnam. The authors proposed policy implications that contributed to the green banking development in Vietnam. Theoretical framework: For long-term sustainable economic development, many countries worldwide have chosen to develop a green economy, including the theory of green banking. Design/methodology/approach: The research method of the paper is a combination of qualitative and quantitative research methods. Qualitative research was conducted with a group discussion technique, checked the scales used, and consulted with banking managers on the research issue, thereby building the scales included in the research model and setting up and completing the questionnaire. Quantitative research was carried out from January to February 2023. Processing data by statistical methods, analyzing EFA and CFA, using linear structural model analysis (SEM) to test the fit of models and hypotheses with SPSS 20.0 software and Amos. Findings: The article showed that banking technology substantially impacts green banking development among eight factors. Research, Practical & Social implications: The study has inherited and supplemented the scale in the model and a new set of scales used to evaluate the development of green banks, systematized, increased, and developed more basic theoretical issues about banking green. Originality/value: The paper's originality and value help researchers, managers, and policymakers for Vietnamese commercial banks, in particular, and the banking industry, in general, to apply to contribute to the development of green banking and the green economy in the future.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".