Drivers of E-Government Maturity in Two Developing Regions: Focus on Latin America and Sub-Saharan Africa
Bibliographic record
Abstract
This research focuses on the determinants of e-government (E-gov) maturity in two comparable regions of the world i.e. Latin America and Sub-Saharan Africa (LA&SSA). E-gov maturity refers to the growth levels in a country’s online services and its citizens’ online participation in governance. To date, few researchers have focused on the determinants of E-gov maturity in LA&SSA. Given the challenges faced by LA&SSA with regard to the implementations and deployment of technological innovations including E-gov, research such as this current one is needed to enrich insight in such contexts. Building on a prior framework and the modernization theory, the impacts of macro-environmental factors of political, economic, social, and technological dimensions on E-gov maturity in LA&SSA are examined. A 5-year panel data consisting of 160 observations or data points was used for analysis in conjunction with structural equation modeling. The data analysis underscored the pertinence of some of the factors on E-gov maturity in LA&SSA. The implications of the study’s findings for research and policy making are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".