MétaCan
Menu
Back to cohort
Record W4390548627 · doi:10.55482/jcim.2023.33579

Determinants of Entrepreneurial Intention Among Students: Perceived Role of the Academic and Socio- Institutional Environment

2023· article· en· W4390548627 on OpenAlexaffvenue
Léandre Gbaguidi, Sèvèho Timothée Ignace Amidjogbe, Alexis Abodohoui

Bibliographic record

VenueJournal of Comparative International Management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsSnowball samplingEntrepreneurshipLegitimacyGovernment (linguistics)Sample (material)PsychologyBusinessSociologyMarketingPublic relationsPolitical sciencePolitics

Abstract

fetched live from OpenAlex

This article aims to analyze the influence of the academic and socio-institutional environment on the entrepreneurial intention of students in Benin. Using the snowball sampling technique, data collection was carried out based on a questionnaire distributed to a sample of 325 students from public and private universities in Benin. The estimation of the ordered logistic regression model with STATA 13 shows that the entrepreneurial intention of students in Benin is characterized by entrepreneurial education, innovation, and risk propensity. Moreover, unlike government support, variables related to perceived cultural norms and social legitimacy of entrepreneurship negatively moderate the effect of entrepreneurship education on students’ entrepreneurial intention in Benin. As a contribution to the literature, this paper shows the crucial role of students’ education in the acquisition of entrepreneurial skills that enhance entrepreneurial capacity and lead them to develop skills that help them start businesses.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.291
Teacher spread0.266 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Comparative International ManagementSame topicEntrepreneurship Studies and InfluencesFrench-language works237,207