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Record W2601758333 · doi:10.5539/res.v9n2p74

Proposition and Test of a Quality Assessment Extension WebQual Model in Brazil

2017· article· en· W2601758333 on OpenAlexvenueno aff
Nivia Elaine Haddad Rezende, Luiz Rodrigo Cunha Moura, Fernanda Carla Wasner Vasconcelos, Nina Rosa da Silveira Cunha

Bibliographic record

VenueReview of European Studies · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyNomological networkDiscriminant validityVariance (accounting)Construct validitySocial psychologyCriterion validityConstruct (python library)StatisticsApplied psychologyComputer scienceStructural equation modelingMathematicsPsychometricsDevelopmental psychology

Abstract

fetched live from OpenAlex

This study has a purpose to test the extended WebQual Model from the inclusion of the construct familiarity has nomological validity, besides to verify the explained variance to satisfaction and intention to reuse are higher in the extended model than the original model. The quality evaluation WebQual model presents a measurement process from twelve constructs: information fit-to-task, tailored information, on-line completeness, relative advantage, easy of understanding, intuitive operations, trust, response time, visual appeal, innovativeness, emotional appeal and consistent image. Constructs familiarity, satisfaction and intention to reuse by users were included in this model. Data were collected by electronic means, and at the end, 456 questionnaires were obtained from the Rede Habitar portal and 240 questionnaires from Rede Imvista portal, totalizing 696 questionnaires. Obtained results indicate that the analyzed portals quality evaluation is reasonable, which all one-dimensional indices, reliability, and convergent validity showed suitable results. In the discriminant validity, only two correlations did not show proper values. Nomological validity was achieved and the relationship between the extended model constructs were shown suitable. In explained variance terms, the construct familiarity insertion increased the indicator’s value for satisfaction, however it should also be considered the lack of discriminant validity between familiarity and satisfaction.

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)0.006
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.822

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.368
GPT teacher head0.541
Teacher spread0.173 · 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 designObservational
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

Citations9
Published2017
Admission routes1
Has abstractyes

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