The Impact of Mobile Banking on Enhancing Customers’ E-Satisfaction: An Empirical Study on Commercial Banks in Jordan
Bibliographic record
Abstract
Recently, banking services begin via mobile appear on a large scale as has become one of the latest services offered by commercial banks in Jordan. So to make banking transactions via mobile, the researcher links banking service via mobile to effect on customer E-satisfaction, therefore, the customers are getting banking service on their own without the need for assistance by the bank employee. The Importance of the subject of mobile banking service and the importance of focusing on the service provided by the banks adopted, this study use seven dimensions that are very important to provide this service and they are: reliability, flexibility, privacy, accessibility, ease of navigation, efficiency, safety, where the aim of this study is to measure the impact of using banking services via mobile to effect on customer e-satisfaction. The study sample consisted of 360 customers from 400 who use banking services via mobile in the following banks: Jordan Ahli Bank, Union Bank, HSBC Bank, Capital Bank and has been tested hypotheses through simple regression, the results indicated that there is an effect of use mobile banking services to reach customer e-satisfaction. The results showed that there is a statically significant impact of the overall dimensions of mobile banking service on customer E-satisfaction and after performing a simple regression that Privacy and accessibility are more influential comparing of the rest of the mobile banking dimensions. The researcher recommended that the bank should give more time and effort to activate and develop mobile banking services to do many different banking transactions in order to reach a customer E-satisfaction.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".