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Record W4410898168 · doi:10.5267/j.dsl.2025.3.005

Determinants of student satisfaction based on website information quality

2025· article· en· W4410898168 on OpenAlexvenueno aff
Roberto Líder Churampi-Cangalaya, Miguel Fernando Inga-Ávila, Enrique Mendoza Caballero, Victor Oscar Moyano Mustto, Madelyn Apardo Quispe, Janneth Del Pilar Nuñez Velasquez, Efraín Núñez Villazana

Bibliographic record

VenueDecision Science Letters · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
Fundersnot available
KeywordsQuality (philosophy)Information qualityBusinessPsychologyMarketingKnowledge managementComputer scienceInformation systemPolitical science

Abstract

fetched live from OpenAlex

Websites have become the digital showcase for companies, organizations, and people in a globalized world. These platforms are essential for communication, e-commerce, education, and entertainment. The objective of this study is to analyze the relationship between the quality of information on websites and user satisfaction in public higher education in Tarma. This is basic research with a quantitative and correlational approach, carried out with a sample of 428 students of the professional careers of Administration, Nursing, and Agroindustrial Engineering enrolled in 2024 at the Universidad Nacional Autónoma Altoandina de Tarma, located in the Department of Junín. The data were processed and modeled using structural equations based on PLS. The results show a Spearman's Rho correlation coefficient of 0.852 and a significance level of 0.000, which shows a high positive correlation between the variables studied. Likewise, the general hypothesis is confirmed, which establishes a significant relationship between usability, information quality, service interaction quality and user satisfaction of the university website.

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)0.007
metaresearch head score (Gemma)0.004
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 score0.193
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.082
GPT teacher head0.456
Teacher spread0.374 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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