Mediating mechanism of customer satisfaction on customer relationship management implementation and customer loyalty among consolidated banks
Bibliographic record
Abstract
The aim of this study is to investigate the mediating mechanism of customer satisfaction (CS) on customer relationship management (CRM) and customer loyalty (CL) among Nigerian consolidated banks. This paper used a survey research design, and the study unit of analysis consists of selected customers among Nigerian consolidated banks. This study used a purposive sampling technique whereby structured questionnaires were used to collect data from 750 customers of the 5 focused banks in Kano State, Nigeria. Partial least square–structural equation modelling (PLS-SEM) was used to evaluate the study hypotheses. The outcome of the study revealed that CRM has a significant effect on CL while CS partially mediates CRM and CL relationship. This paper provides substantial results to practitioners to realize the role of developing a CRM strategy in the Nigerian banking industry. In line with that, the management of the banks should build sound CRM components such as process fit, customer information quality and information system support to deliver sound services in order to operate and compete in the banking ecosystem effectively. This paper has made a substantial contribution to the body of knowledge in the CS, CL, and CRM literature by operationalizing it within the Nigerian banking industry.
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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 | 0.002 | 0.006 |
| 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) | 0.006 | 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".