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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".