Technology Aspects of E-Government Readiness in Developing Countries: A Review of the Literature
Bibliographic record
Abstract
The rapid global growth of the Internet and information technology has inspired many governments to transform their traditional services into electronic ones. Many governments are now developing, implementing and improving their strategies to transform government services using information and communication technologies (ICTs). E-Government, as it is known, has become a popular focus of government efforts in many developed countries and, more recently, in several developing countries. Further, e-government services have become a significant and active means for interaction among government, citizens and businesses. E-government comprises several dimensions, one of the main ones being e-government readiness. To put technology to effective use, a government must be “ready”. E-government readiness helps a government to measure its stages of readiness, identify its gaps, and then redesign its government strategy. One of the aspects of e- government readiness is that of technological readiness, which plays an important role in implementing an effective and efficient e- government project. This paper explores the gaps in current knowledge relating to the technological aspects of e-government readiness through the conduct of a literature review. In particular, the review focuses on the models and frameworks that have been developed to assess e-government readiness.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".