AN ESSENTIAL REVIEW OF INTERNET BANKING SERVICES IN DEVELOPING COUNTRIES
Bibliographic record
Abstract
In the absence of a literature review for an adoption of internet banking in developing countries, this study was conducted to review and summarize the most evaluated articles in the literature. The significance of this work came from three concepts which are to highlight the concepts of research in developing countries, to accentuate the dominant models which have been used effectively in analyzing the constructs in adopting internet banking, and thirdly, to shed some light on the gaps in the potential applications for the system in the future. The Technology Acceptance Model, Theory of Reasoned Action, Theory of Planned Behaviour, Unified Theory of Acceptance and Use of Technology and Self Designed models have been used by most of the researchers in this review with a higher frequency of the Technology Acceptance Model among them. The results from this review are limited to 28 articles which were selected from a total of 110. The sources were from the search engines of Science Direct, Emerald Insight, Growing Science, International Business Information Management Association and Canadian Central of Science and Education and other different types of journal publications. The factors revealed to have important effects on the acceptance and adoption of the system were trust, perceived ease of use, and perceived usefulness, security and privacy and social influences.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".