Influence of Demographic Factors on Users' Adoption of Electronic Banking in Ethiopia
Bibliographic record
Abstract
Purpose: Regardless of the fact that the influence of demographic factors on users' adoption or usage behavior of e-banking channels for banking transactions have been intensively examined by studies carried out mostly in the developed countries, this area is not well studied in the developing countries especially in Ethiopia. Therefore, the main objective of this study is to investigate the influence of demographic factors on users' adoption of e-banking systems in Ethiopia from the current users' perspective. Research Methodology: Descriptive type of research was applied for this study. A total of 600 users' of e-banking services were used as a sample survey from those commercial banks that are using e-banking systems as a means of banking service provision in Addis Ababa, Ethiopia. A well-structured and randomly administered questionnaire was used to collect the relevant information from those customers who are using at least one form of e-banking systems. Interview was also used to collect supporting data from e-banking department managers of each respective bank. Data gathered from customers were analyzed using independent sample T-test and one way analysis of variance (ANOVA). The entire statistical tests were conducted using SPSS version 21. Findings: The findings of this study imply that except for gender, the remaining demographic variables such as age, income, educational level and occupational status have no significant influence on users' e-banking usage behavior which implies that those users' who are in different age, income, educational status and occupation category have similar e-banking adoption or usage behavior. Implication: The findings from this study (particularly from gender perspective) suggest that commercial banks in Ethiopia should create more awareness to their e-banking users' (especially to females) in order to develop better e-banking usage practice.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".