Customer Satisfaction with Electronic Banking Services in the Saudi Banking Sector
Bibliographic record
Abstract
<p class="a">This research aims to examine the extent of customer satisfaction with electronic banking (e-banking) services in the Saudi banking sector and to address issues with quality of services by focusing on the following: (i) ease of use; (ii) information security and reliability and its role in influencing customer adoption of electronic services; (iii) the mechanisms of monitoring and control over these services. The research employed analytic and descriptive methodology, collecting primary data through a survey. It examined various aspects of electronic services provided by banks in Saudi Arabia to shed more light on these services and customer expectations, while also taking into account modern studies in this field as secondary data. The results show that Saudi banks have succeeded in attaining significant customer satisfaction by improving their electronic services, facilitating electronic transactions, improving processing performance and enhancing the specifications of electronic services. In addition, they have achieved effective communication with their customers as well as the speed of applications. However, there is an absence of awareness and guidance for customers about the e-banking system. The results of this research lead to some recommendations for improving the electronic services provided by banks in Saudi Arabia to enhance customer satisfaction.</p>
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".