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Record W4360776538 · doi:10.5267/j.ijdns.2023.3.016

The impact of brand image on public university links in the context of autonomy: A case study in Vietnam

2023· article· en· W4360776538 on OpenAlexvenueno aff
Quang Bach Tran, Thi Thuy Quynh Nguyen, Thi Hoang Mai Tran, Thi Quynh Lien Duong, Thi Hanh Duyen Nguyen, Thi Bich Thuy Nguyen, Nhu Nguyen, Thi Lien Trinh

Bibliographic record

VenueInternational Journal of Data and Network Science · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Identity and Reputation
Canadian institutionsnot available
Fundersnot available
KeywordsAutonomyContext (archaeology)Structural equation modelingExploratory researchExploratory factor analysisPublic relationsPolitical scienceSociologySocial scienceGeographyMathematics

Abstract

fetched live from OpenAlex

This study aims to examine the impact of brand image on public university links in the context of autonomy in Vietnam. Using quantitative research methods through exploratory factor analysis (EFA) and Structural Equation Modeling (SEM), the survey data included 631 samples of managers, experts, and scientists at public universities divided by different disciplines. The results of the study showed that the brand image has both a direct and indirect impact on public university links in the context of autonomy through intermediate elements of trust and commitment in the relationship between universities. In addition, trust has also been shown to have a direct impact on commitment in the relationship between public universities in Vietnam in the context of autonomy. In the context of research in Vietnam, the findings of this study have shown both theoretical and practical contributions and will be an important basis for further research.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.055
GPT teacher head0.324
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2023
Admission routes1
Has abstractyes

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