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Record W2781956081 · doi:10.1177/104225870202600402

Are Entrepreneurial Cognitions Universal? Assessing Entrepreneurial Cognitions across Cultures

2002· article· en· W2781956081 on OpenAlexaff
Ronald K. Mitchell, J. Brock Smith, Eric A. Morse, Kristie W. Seawright, Ana María Peredo, Brian McKenzie

Bibliographic record

VenueEntrepreneurship Theory and Practice · 2002
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsWestern UniversityUniversity of Victoria
Fundersnot available
KeywordsCognitionEntrepreneurshipPsychologySet (abstract data type)ArchetypeNeed for cognitionSocial psychologyBusiness

Abstract

fetched live from OpenAlex

In this study we examine three research questions concerned with entrepreneurial cognition and culture: (1) Do entrepreneurs have cognitions distinct from those of other business people? (2) To what extent are entrepreneurial cognitions universal? and (3) To what extent do entrepreneurial cognitions differ by national culture? These questions were investigated in an exploratory study using data collected from 990 respondents in eleven countries. We find, in answer to question one, that individuals who possess “professional entrepreneurial cognitions” do indeed have cognitions that are distinct from business non-entrepreneurs. In answer to question two, we report further confirmation of a universal culture of entrepreneurship. And in answer to question three, we find (a) observed differences on eight of the ten proposed cognition constructs, and (b) that the pattern of country representation within an empirically developed set of entrepreneurial archetypes does indeed differ among countries. Our results suggest increasing credibility for the cognitive explanation of entrepreneurial phenomena in the cross-cultural setting.

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)0.004
metaresearch head score (Gemma)0.016
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 score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.306
Teacher spread0.259 · 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

Citations411
Published2002
Admission routes1
Has abstractyes

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