Factors Affecting the Competitive Capacity of Commercial Banks: A Critical Analysis in an Emerging Economy
Bibliographic record
Abstract
This research was conducted to investigate the factors influencing the commercial bank’s competitive capacity in an emerging country. Data were collected from the domestic-owned commercial banks and foreign-owned commercial banks listed on Vietnam’s Stock Exchange over the period of nine years from 2010 to 2018. Three statistic approaches were employed to address econometrics issues and to improve the accuracy of the regression coefficients: Pooled Ordinary Least Square (Pooled OLS), Random Effects Model (REM), and Fixed Effects Model (FEM). To correct the diagnostics and endogeneity in the model, the study uses Generalized Least Square (GLS) and Generalized Method of Moments (GMM). In order to account for the degree of competitive capacity we use Lerner index. Results demonstrate that the impact of bank-specific characteristics on market power in banks is statistically significant, and there are substantial distinguishments of economic consideration among these factors. In addition, a bank with a higher level of competitive capacity in the previous year will outstandingly generate competitive capacity in the current year. Another possibility, a greater level foreign investment into the banks in the host country could further encourage competitive capacity in the banking system. Finally, economic growth rate has no impact on competitive capacity at a significant level of 5% while a positive effect from inflation on bank’s market power could be found.
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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 | 0.002 | 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 | 0.000 |
| 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".