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Cultivating An Entrepreneurial Mindset: A Quasi-Experimental Study of Entrepreneurial Education

2025· article· en· W4416005929 on OpenAlexaff
Nicole Larson, Houston Peschl, Matthew J. W. McLarnon, Zahra Jamshidi

Bibliographic record

VenueAcademy of Management Proceedings · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsMount Royal UniversityUniversity of Calgary
Fundersnot available
KeywordsMindsetExperiential learningEntrepreneurshipTeamworkEntrepreneurship educationEntrepreneurial educationExperiential education

Abstract

fetched live from OpenAlex

Entrepreneurship education goes beyond preparing students for new venture creation by developing an entrepreneurial mindset that acts as a guiding cognitive framework essential for navigating the complexities of the modern workplace. This study examines the impact of two pedagogical approaches to entrepreneurship education (i.e., competence-based vs. supply-demand) on an entrepreneurial mindset (i.e., failing forward, tolerance for ambiguity, responsiveness to feedback, and teamwork attitude). A quasi-experimental design involving over 1,300 undergraduate students was used to assess changes in entrepreneurial mindset and entrepreneurial intentions. The findings reveal that the competence-based model, grounded in constructivist and experiential learning principles, leads to significant improvements in failing forward, responding to feedback, and teamwork attitudes, but not tolerance for ambiguity. Interestingly, entrepreneurial intentions showed a decrease over time in both courses, but students that were able to develop responsiveness to feedback, were buffered against this effect. Additionally, online course delivery formats showed advantages over traditional face-to-face methods in enhancing several aspects of the entrepreneurial mindset. Keywords: Entrepreneurship Education, Entrepreneurial Mindset, Entrepreneurial Intention, Experiential Learning, Online Learning

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.001
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 head0.019
GPT teacher head0.294
Teacher spread0.274 · 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.

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
Published2025
Admission routes1
Has abstractyes

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