Measuring the Degree of Internationalization for Taiwanese Banking Industry: Scoring Measurement by Principal Component Analysis
Bibliographic record
Abstract
This paper mainly focuses on researching and measuring the effects concerning the degree of internationalization (DOI) within the Taiwanese banking industry, with particular application of the scoring measurement by principal component analysis (PCA). The empirical results indicate that there exits an increasing trend for the DOI of Taiwanese banking industry as measured by FATA (each bank’s overseas assets over its own total assets), FETE (each bank’s overseas equities over its own total equities), FSTS (each bank’s overseas sales over its own total sales) and FBTB (each bank’s overseas facilities and operating units over its own total operating units). Under the Box-cox transformation, the DOI scoring index as percentage level (%) from 0 to 100 shows that the relevance of the variables are mostly above 50, indicating that the DOI of the observed Taiwanese banks are quite strong. Thus, the results support the existence and importance of the DOI for current Taiwanese financial institutions, while presenting complimentary and more realistic information regarding the degree of internationalization for Taiwanese banks. At the same time, providing substantial groundwork and adding value to future international management research and investigations that could help representatives of government and management decision makers to achieve better investment decision-making for business strategies and prompt international expansion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".