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Record W3016359158 · doi:10.5539/ies.v13n5p94

Barriers Affecting the Passion and Entrepreneurial Intention of University of the ITSON

2020· article· en· W3016359158 on OpenAlexvenueno aff
Luis Enrique Valdez-Juárez, Domingo García Pérez de Lema, Elva Alicia Ramos-Escobar

Bibliographic record

VenueInternational Education Studies · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsPassionCreativityEntrepreneurshipStructural equation modelingPsychologySocial cognitive theoryTheory of planned behaviorSocial entrepreneurshipSociologyManagementSocial psychologyPedagogyMathematics educationPolitical scienceMathematics

Abstract

fetched live from OpenAlex

The purpose of this article is to analyze the internal and external barriers experienced by university students of the Technological Institute of Sonora (ITSON) for the development of entrepreneurship. The study is focused on a sample of 733 students from the areas of engineering, administrative sciences, and social sciences. The field work was carried out during the months of May to September 2018. The controlled statistical technique for data analysis was the structural equation model (SEM) with the support of SMARTPLS software version 3.2.8. The results have revealed that internal creativity barriers are the ones that most negatively impact the entrepreneurial passion of ITSON university students. This research contributes to the development of entrepreneurship literature from the area of psychology and business sciences through planned behavior theory, cognitive theory, and self-determination.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.031
GPT teacher head0.270
Teacher spread0.239 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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