Digitalizing the Municipality and Factors Affecting the Acceptance of E-municipality: An Empirical Analysis
Bibliographic record
Abstract
<p>Over the years, rapid developments in information and communications technologies have brought about an increase in the amount of time users spend online. Users who consistently interact online also resort to receiving goods and services that are supplied via these networks. This type of user behavior has also expedited the supply of services delivered by municipalities. In this regard, the purpose of the current study is to determine the factors affecting the adaptation of e-municipality services and their extent, by exclusively observing the leading e-municipality service provider. In order to achieve the purpose of the study, field research was conducted among 302 participants in the city of Yalova using the convenience-sampling technique.</p><p>The findings indicate that competence in the use of technology, perceived ease of use, perceived awareness and information quality positively influence the acceptance of e-municipality services whereas factors such as perceived trust and usability of resources have no significant impact.</p>
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How this classification was reachedexpand
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".