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Record W2742436347 · doi:10.1504/ijev.2016.10006912

What Can Universities Do to Promote Entrepreneurial Intent? An Empirical Investigation

2016· article· en· W2742436347 on OpenAlexaffabout
Dave Valliere, Jeffrey Overall, Steven A. Gedeon

Bibliographic record

VenueInternational Journal of Entrepreneurial Venturing · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsNipissing UniversityToronto Metropolitan University
Fundersnot available
KeywordsEntrepreneurshipStructural equation modelingPsychologyPsychosocialSocial cognitive theorySocial psychologyEntrepreneurial orientationTheory of planned behaviorEmpirical researchMarketingManagementBusinessEconomics

Abstract

fetched live from OpenAlex

Our university has promoted entrepreneurship extensively through networking events, business plan competitions, funding sources, degree programs, and on-campus incubators. But do these efforts work? We integrate and extend intentions-based models and the psychosocial cognitive model to develop a model of entrepreneurial intent and behaviour. We test our theory using partial least squares structural equation modelling on survey data collected from 334 undergraduate business students in Canada. We find that the belief constructs, namely: subjective norms toward entrepreneurship, prevalence of entrepreneurship on social milieu, and goal-orientation are found to positively influence the attitude constructs of the: 1) desirability of an entrepreneurial career; 2) perceived feasibility of an entrepreneurial career. These attitudes, in turn, positively impact entrepreneurial intent, which subsequently positively influences entrepreneurial behaviour. We, thus, found support for the hypotheses that university support for these psychosocial influences has a positive effect on student entrepreneurial intent and behaviour. Practical implications are discussed and future directions are suggested.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.467
Threshold uncertainty score0.805

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.004
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 head0.025
GPT teacher head0.273
Teacher spread0.248 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

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