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Record W4224947061 · doi:10.18280/ijsdp.170226

Electronic Services Management in Local Governance – Evidence from a Transitional Economy

2022· article· en· W4224947061 on OpenAlexvenueno aff
Naim Mustafa, Adrian Bajrami, Xhavit Islami

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicE-Government and Public Services
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessE-servicesServices computingGovernment (linguistics)Quality (philosophy)Work (physics)Corporate governanceMarketingKnowledge managementFinanceComputer scienceEngineering

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.261
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

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