E-Commerce Challenges and Policy Considerations in Nigeria
Bibliographic record
Abstract
Electronic commerce (or e-commerce) is the popular term for doing business electronically. According to Haag, Cummings, and McCubbrey (1998), for businesses, electronic commerce includes performing transactions with customers over the Internet for purposes such as home shopping, home banking, and electronic cash use; performing transactions with other organizations through the use of electronic data interchange (EDI); gathering information relating to consumer market research and competitors; and distributing information to prospective customers through interactive advertising, sales, and marketing efforts. Benefits of e-commerce to companies include a wider potential market (i.e., global access); lowering of transaction costs; increase in the speed of transactions; improved economies of scale; minimization of human intervention in business processes; and unlimited access to product information for customers (Sesan, 2000; Wood, 2003). While a few developing countries such as Costa Rica are making inroads into electronic commerce (Travica, 2002), many others are slow in its adoption. For example, a study, which rated 42 developing countries on their “e-readiness,” found that Taiwan and Estonia had emerged as leaders among developing countries in the ability to conduct e-commerce, whereas Russia, much of the Middle East, and Africa were lagging behind (Anonymous, 2000). One of the countries included in the study but that rated poorly in its e-commerce efforts is Nigeria. In this articl, we shall be discussing the challenges being faced by the country as it grapples with the adoption 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 | 0.000 | 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".