Customer satisfaction as a mediation between micro banking image, customer relationship and customer loyalty
Bibliographic record
Abstract
The purpose of this article is to build a consumer loyalty model by considering consumer satisfaction as a mediating variable between the image of micro banking and consumer relations with consumer loyalty. Design/methodology/approach of this article is a research on micro banking customers. The survey was conducted on 100 micro banking customers. The research shows that company image positively influences customer satisfaction and customer loyalty. Customer relationship positively influences micro banking company image, customer satisfac-tion and customer loyalty. In addition, customer satisfaction influences customer loyalty. Moreover, customer satisfaction cannot be used as a mediation variable between micro banking company image and relationship with customer. Practical implications of this research is that consumer loyalty could be enhanced by strengthening the image of micro banking companies, strengthening consumer relations and maintaining customer satisfaction. This research is important to identify the image of micro banking and consumer relations and their relationship with customer satisfaction and consumer loyalty, since the strength of micro-enterprises lies in the ability to build image and proximity to consumers. This is important be-cause of the limited ability of micro banking companies to advertise heavily on various adver-tising media.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.009 | 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".