Use of E-Banking and Customer E-Engagement in Developing Countries: Case of NFC Bank Cameroon
Bibliographic record
Abstract
Technology-based banking has become essential in developing countries. In these countries, the financial inclusion of populations and the development of banks’ portfolios depend intensely on valuable services like E-banking. This study aims to investigate the influence of some technological features of electronic financial services (Perceived personal information protection, Perceived transaction security) and service factors (Perceived time saving, Service quality, and Perceived cost-saving) on Trust and Use of e-banking. It also studies the impact of Use of E-banking on E-engagement through Usage continuance and Customer satisfaction. We use partial least squares structural equation modeling (PLS-SEM) to test a research model with a sample of 346 customers of NFC Bank in Cameroon. The study reveals that Perceived personal information protection and the service factors (Perceived time saving, Service quality, and Perceived cost-saving) influence Trust. However, Trust in E-banking does not necessarily lead to its use. On the other hand, Use of E-banking influenced by both technological features of electronic financial services (Perceived personal information protection, Perceived transaction security) and service factors (Perceived time saving, Service quality, and Perceived cost saving). The study brings managerial implications for the development of E-banking offers in developing countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.004 | 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".