The Influence of National IT Policies, Socio-economic Factors, and National Culture on Network Readiness in Africa
Bibliographic record
Abstract
This study examines the influence of national IT policies, socio-economic and cultural factors on the network readiness of African countries. The capability and level of preparation of a nation to participate in and benefit from information and communication technologies (ICT) for socio-development is assessed by the network readiness index. Prior studies have shown that such factors have a significant influence on how a country benefits from its use of ICT products for development. Research on this topic with data from the African continent is rare. This study serves to fill this gap. It is based on data from a cross-section of twenty diverse African countries. The data suggested variability in the use of ICT for developmental purposes among the sampled countries. To that end, Africa should not be viewed as monolithic in such respects. The study showed that all the measures used to operationalize national IT policies, socio-economic and some cultural factors are positively related to the network readiness of the sampled African countries. Importantly, the quality of each country’s educational systems, its transparency (corruption) levels, its ICT regulatory framework, and its cross-cultural dimension of power distance (PDI) were found to have significant relevance to its network readiness. The implications of the study’s findings for research and policy making are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".