The differential impacts of customer commitment dimensions on loyalty in the banking sector in Jordan: Moderating the effect of e-service quality
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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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 | 0.003 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".