Relationship between Internal Marketing and Service Quality with Customers' Satisfaction
Bibliographic record
Abstract
This study aims at investigating the relationship between internal marketing and service quality to customers'satisfaction in Jordan commercial banks, through answering the following questions: Do commercial banks inJordan apply the appropriate concept of internal marketing? What are the possible procedures that can be appliedin the banking sector? Is there any relationship between bank services quality and customer satisfaction?In order to achieve the study objectives, two questionnaires were designed and distributed over two samples ofJordan bank employees and customers totaling (231) and (384) respectively. The questionnaires were collectedand analyzed by using the SPSS. The study's conclusions are as follows:-Study's sample attitudes were positive towards internal marketing (service culture, human resourcesdevelopment, motives system and rewards) totaling (0.5693) more than virtual mean-Study's sample evaluations were positive towards internal marketing procedures from employees perspectivessince its mean is more than the virtual mean (3), totaling (0.6935).-Study's sample attitudes were positive towards banking service quality represented by (tangibility, reliability,responsiveness, assurance, empathy), since evaluations before benefiting from the banking service were (3.566)i.e. (0.566) more than the virtual mean, while after benefiting, the evaluations were (0.778) more that the virtualmean.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".