Quality of Website Services at Government Banks, NationalPrivate Banks and Local Government Banks in Indonesia:Customer Perspective Approach
Bibliographic record
Abstract
The purpose of this research is to analyze the customer's characteristics of private commercial banks and national governments in using e-banking, and to analyze the existence of differences in the use of e-banking through six variables, namely accessibility, interaction, adequacy of information, usefulness of content, lifestyle and personality. The results of this research show that there is a clear difference between the customers of local government bank, government bank and national private bank in measuring the quality of service of the bank's website in Bekasi City. Three factors discriminant analysis and two factor discriminant analysis is used to analyze the respondents of local government bank (DKI Bank), government bank (Mandiri Bank), private bank (BCA Bank) and respondents of BJB Bank and Mandiri Bank. The result of three factors discriminant analysis shows that there is a difference between the customers of local government bank, government bank and private bank. Meanwhile, the result of two factors discriminant analysis shows that there is no difference between the customer of government bank and the customer from local government bank in measuring the service quality website.
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 | 0.001 | 0.002 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".