Determinants of eGovernment maturity in the transition economies of central and eastern Europe
Bibliographic record
Abstract
Our research focuses on the possible determinants of eGovernment (E-gov) maturity in the Transition Economies of Central and Eastern Europe (TEECE). E-gov maturity, in this research, refers to the growth levels in a country's online services and its citizens' online participation in governance. Our study of the extant literature indicated that few have discussed the determinants of E-gov maturity in TEECE. Studies from differing parts of the world are needed for theory development. Building on a prior framework, we used the contingency theory and the resource-based view perspective to guide our discourse. In particular, we examined the relationships between macro-environmental factors such as national wealth, technological infrastructure, rule of law, and so forth on E-gov maturity. A 5-year panel data of 16 TECEE selected from two main groupings was used for analysis in conjunction with structural equation modeling technique; the data consisted of 80 observations or data points. The data analysis underscored the relevance of such factors as technological infrastructure, rule of law, and human capital development as possible determinants of E-gov maturity in TEECE. National wealth was found to be an enabler in the research conceptualization. The implications of our 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 | 0.003 |
| 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 | 0.001 |
| 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".