Factors influencing e-government maturity in transition economies and developing countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines the influences of relevant environmental factors on E-government (E-gov) maturity in transition economies and developing countries (TEDC). Countries from Eastern Europe, Sub-Saharan Africa, Latin American and South Asia were selected for the study. Prior research has investigated E-gov growth, development, and diffusion across both the developed and developing worlds. While such a focus is useful for comparative analyses at a global level, it is however argued that more useful information will emerge to enrich insight when research efforts particularly focus attention on issues in emerging parts of the world. Very few researchers have studied the factors influencing E-gov maturity in TEDC and with the approach employed in this present research. Using relevant theoretical frameworks, this research identified and examined the impact of 9 environmental factors of socio, political, economic, and technological dimensions on E-gov maturity in TEDC. A 5-year panel data consisting of 320 observations or data points was used in conjunction with the ordinary least squares (OLS) technique. This research also provided analyses for each of the selected sub-regions to enhance insight. Overall, the results showed that the availability of quality human resource, technological infrastructure, innovative capacity, wealth, rule of law, and transparency levels are important factors that positively impact E-gov maturity in TEDC. The implications of the study's findings for research and policy making are discussed. Future research avenues are also highlighted.
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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 | 0.003 | 0.001 |
| 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 | 0.018 |
| 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 it