E-readiness and Security in E-commerce: A Three-Dimensional Approach
Bibliographic record
Abstract
E-commerce is globalizing. It is no possible to a country to emerge without it. That is why the vast majority of the world's countries adopt it. The purpose of this paper is to highlight the factors that impact e-readiness and e-commerce security. To achieve this objective, data have been collected on a sample of 40 cameroonian companies based in Douala and Yaoundé. The number of companies by business sector respects the proportions from the census conducted by the National Institute of Statistics (NIS) in 2016. The collected data are then analyzed using statistical software such as STATA to highlight the weight of each dimension. As results, E-readiness depends on legal factors, economic factors and technological factors. So, E-readiness is based on three pillars: legal, economic and technological. None of the three is to be overlooked in the development of e-commerce.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".