Nigeria SMEs Participation in Electronic Economy: Problems and the Way Forward
Bibliographic record
Abstract
For both developing and developed countries, small and medium scale firms play important roles in the process of industrialization and economic growth. Apart from increasing per capita income and output, Small and Medium Scale Enterprises (SMEs) create employment opportunities, enhance regional economic balance through industrial dispersal and generally promote effective resource utilization considered critical to engineering economic development and growth. The development of SMEs is seen as accelerating the achievement of wider economic and socio-economic objectives, including poverty alleviation. Unleashing its influence on the way traditional business is conducted hitherto is the phenomenon of electronic commerce (e-commerce). The Internet through its reduction in distance-related costs is seen by many as a potential source of economic revitalization. This Internet economy has continued to grow at an unprecedented rate. Thus, this paper focused on the extent of Nigerian’s SMEs participation in this Internet economy. This will be achieved by considering the conceptual framework of Internet/electronic economy, some applications of Internet/electronic economy, Nigeria SME participation in electronic economy, the benefits and limitations of electronic economy in general, the problems hindering Nigeria SMEs’ participation in electronic economy and finally pointing out the way forward.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 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".