Customer Loyalty Islamic Banks in Indonesia: Service Quality Which Mediated by Satisfaction and Customer Trust
Bibliographic record
Abstract
Purpose: This study aims to analyze the effect of service quality on loyalty mediated by customer satisfaction and trust. A total of 215 Islamic bank customers in the study area were sampled accidently. Theoretical framework: Theoretically, this study contributes to the gaps in the literature, especially those related to the effect of SQ on Islamic banking loyalty which is mediated by satisfaction and trust simultaneously in one research title. Practically, encouraging Islamic banking management to design SQ programs that integrate the value of satisfaction and trust in the same cultural background to gain customer support and loyalty Design/methodology/approach: There are four latent variables observed; service quality, satisfaction, trust, and loyalty with the number of indicators respectively six, four, three, and four. Data collection was carried out through interviews using a questionnaire. Data were analyzed using Smart-PLS 3.0. The validity test uses factor loading and AVE ( Average Variance Extracted ), the reliability test uses Composite reliability (CR) and Cronbach's Alpha (CA), the direct effect test uses the path analysis matrix, while the indirect effect test uses the specific indirect effects matrix. Findings: The volume of research has been increasing over the past nineteen years, although there are only a few authors, institutions and reference journals contributing to the topic’s consolidation and the countries conducting the most joint research in co- authoring for the largest volume of publications, authors and impact journals. Research, Practical & Social implications: This study concludes that customer loyalty of Islamic banks in Indonesia can be optimized through service quality which is mediated by customer satisfaction and trust. Originality/value: The results of the analysis show that the factor loading value is> 0.6 and the AVE value is> 0.5 which means that the data is valid. CR and CA values > 0.6 which means the data is reliable. The direct effect test shows that service quality, satisfaction and trust have a positive and significant effect (p-value <0.05) on loyalty. Furthermore, service quality has an indirect effect ( specific indirect effects) on customer loyalty through the mediation of satisfaction and trust.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".