Enhancing Customer Retention through Electronic Service Delivery Channels in the Nigerian Banking Industry
Bibliographic record
Abstract
Successful customer retention consists of more than just giving the customer what they expect. Electronic service delivery channels have become a means through which banks achieve their objectives of business renewal and providing effective and efficient services. The main objective of this study is to examine the effect of electronic service delivery channels on customer retention in the Nigerian banking industry. The study made use of a sample of 235 employees from some selected banks in Asaba Metropolis in Delta State, Nigeria. Cross sectional survey research design method was adopted, and the statistical tools used comprised simple percentage, correlation and multiple regression analysis. Findings showed that point of sales service exhibited the relatively highest positive effect on customer retention. It was also revealed that online banking service, point of sales service and mobile banking have significant relationships with customer retention. The study concluded that the demand for POS technology seems currently high, however banks are now taping into this opportunity by making POS available at all times, so as to reduce queuing time in the bank and give customers convenience and control. It is therefore recommended that banks should collaborate with internet service providers because it will enable banks to better control quality of service as well as enhance user’s accessibility.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".