The differential impacts of customer commitment dimensions on loyalty in the banking sector in Jordan: Moderating the effect of e-service quality
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
The current research scrutinizes the relationship between the three model commitment components (affective, normative, and calculative commitment) and their various influences on customer loyalty. This is particularly in the banking sector setting in Jordan. A self-reported questionnaire was distributed to collect primary data for analysis. 333 completed questionnaires were analyzed via using PLS software to extract the effect of e-service quality on the relationship between customer commitment and loyalty. The results of this study demonstrate that the affective type of commitment has a positive impact on customer loyalty followed by normative commitment and lately by calculative commitment. Moreover, the results show that the influence of the dimensions of customer’s commitment on loyalty is moderated by e-service quality. This study indicates that affective commitment elements (self-identification, sense of belonging and emotional attitudinal components) are essential for customers when they deal with their bank. On the other hand, the cost associated with leaving has shown to have the weakest impact on customer loyalty. Companies must know that customers may switch even though the cost associated with leaving is high.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 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