Electronic Services Management in Local Governance – Evidence from a Transitional Economy
Bibliographic record
Abstract
The purpose of this study is to measure the impact of electronic services management on local government. Thus, it aims to provide empirical evidence on the factors that influence citizens’ willingness to use e-services. This study analyses the role of factors such as: awareness of electronic services, poor infrastructure and technical problems (quality of electronic services) in using e-services. The study was conducted based on data collected through a self-administered questionnaire from 130 citizens of the Republic of Kosovo, in the Gjilan region, and from three managers who manage e-services in the municipality. Whereas, to analyze these data was used SPSS as statistical software. The results show that factors such as accessibility at any time, reduction of waiting time and quality of information are the most important factors that increase the importance and willingness of using e-services. In addition, the use of e-services is positively related to the management of electronic services, awareness about electronic services and the quality of electronic services offered. The study contributes theoretically and empirically, to the knowledge about the management of electronic services on local government. This work becomes relevant for policymakers to understand in depth the specific challenges faced by users of electronic services.
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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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".