Financial performance and service quality of Saudi Arabia banks: An analytical approach
Bibliographic record
Abstract
Service quality plays an important role in the enhancement of the satisfaction level of customers or consumers and the financial performance of business organizations. Customers expect excellence in service quality and the gap between the expectations and availability of the services determines the level of the service quality of the business organizations. The primary objective of the study is to measure the service quality of Saudi Arabian banks. An online questionnaire containing SERVQUAL dimensions was administered, and responses were analyzed by applying the F test two sample variances, and rank correlation. Financial information extracted from the selected Saudi Arabian banks for the period 2018 to 2022 and ROA (return on assets) and ROE (return on equity) were calculated to get the financial performance. The combined study of rank analysis of financial variables and SERQUAL variables indicates that financial performance is positively and moderately governed by the Tangibility, Assurance, and Empathy dimensions of service. Overall, the expectations of the bank clients are higher than the availability of services in Saudi Arabian banks. There is a need to improve the Reliability and Responsiveness to enhance the level of service quality in Saudi Arabian banks.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".