Determinants of satisfaction and loyalty of e-banking users during the COVID-19 pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research aims to analyze factors affecting e-banking user satisfaction and e-banking user loyalty during Covid-19 pandemic with e-banking service quality that consists of reliability, privacy and security, design on application or website, and customer service and assistance as the independent variables, while the dependent variables in this study are e-banking user satisfaction and e-banking user loyalty. This research was viewed based on an e-banking user perspective. This research used nonprobability sampling and purposive sampling. This research is a quantitative study using primary data based on a questionnaire distributed online to 110 e-banking users like respondents. The hypothesis testing in this study used the SPSS analysis tool through the IBM SPSS Statistics Version 22 application. The result illustrates that e-banking service quality, reliability, and design on application and website influence both e-banking user satisfaction and e-banking user loyalty. Meanwhile, privacy and security only influence e-banking user loyalty, not on e-banking user satisfaction. Furthermore, customer service and assistance have no effect on both e-banking user satisfaction and e-banking user loyalty during the Covid-19 pandemic.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it