Determinants of eGovernment maturity in the transition economies of central and eastern Europe
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
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.
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.
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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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