The effects of customer relationship management, service quality and relationship marketing on customer retention: The mediation role of bank customer retention in Indonesia
Bibliographic record
Abstract
This study aims to examine customer retention (CR) from the aspect of customer satisfaction with customer relationship management (CRM), service quality and marketing relations (RM). State-owned bank customers selected the research population in all branch offices in the Madura region, and data were collected through a Likert scale model questionnaire. The results of the path analysis using the structural analysis model (SEM) show that there is an influence of CRM on customer satisfaction; there is an effect of service quality on customer satisfaction; there is an effect of RM on customer satisfaction; CRM through customer satisfaction affects CR; service quality through customer satisfaction affects CR; RM through customer satisfaction affects CR; there is an effect of customer satisfaction on CR on customers. Then the simultaneous test shows that simultaneously RM, service quality, and RM impact customer satisfaction, and the value of coefficient of determination (R-Square) explains that CRM, service quality, and RM can effectively contribute to customer satisfaction. Simultaneously, CRM, service quality, and RM affect CR. CRM, service quality, and RM affect CR mediated by customer satisfaction. CRM, service quality, and RM, through customer satisfaction, can effectively contribute to CR to customers of state-owned bank Regional Offices.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".