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Record W2542382452 · doi:10.17722/ijme.v7i3.859

Barriers to the Use of Electronic Government as Perceived by Citizens at the Municipal Level in México

2016· article· en· W2542382452 on OpenAlexvenueno aff
Carlos Luis López-Sisniega, María del Carmen Gutiérrez Diéz, Ana María Arras Vota, José Luis Bordas-Beltrán

Bibliographic record

VenueInternational Journal of Management Excellence · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicE-Government and Public Services
Canadian institutionsnot available
Fundersnot available
KeywordsTechnology acceptance modelBusinessPaymentGovernment (linguistics)Web presenceThe InternetPerceptionMarketingLiteracyStructural equation modelingPublic relationsUsabilityPsychologyEconomic growthEconomicsFinancePolitical science

Abstract

fetched live from OpenAlex

The benefits of e-government services depend on the number of citizens who take advantage of them. The purpose of this quantitative, correlational research study was to determine barriers to e-government use as perceived by citizens at the municipal level in Mexico. The technology acceptance model (TAM), the diffusion of innovations (DOI) theory, and models of web trust formed the theoretical framework of the study. Several hypotheses tested the relation of demographic variables, TAM, DOI, and web trust constructs to the intention of using e-government services of 149 taxpayers of the city of Chihuahua, Mexico, who did not to use the e-government services for payment provided by the government of their municipality. The findings of this study show that trust in the Internet, trust in government, perceptions of convenience, perceptions of compatibility, access to the Internet, perceptions of ease of use, and perceptions of relative advantages are related to the intention to use e-government services. Conversely, awareness of the existence of e-government services, income level, family structure, age, literacy level, computer literacy level, gender, and possession of bankcards are not individually related to the intention to use e-government services of those persons who made face-to-face payments at the treasury office.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score0.999

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.288
Teacher spread0.256 · 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.

Study designNot applicable
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

Citations1
Published2016
Admission routes1
Has abstractyes

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