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Record W2594104084 · doi:10.1108/ijebr-12-2015-0297

Belief patterns of entrepreneurship: exploring cross-cultural logics

2017· article· en· W2594104084 on OpenAlexaff
Dave Valliere

Bibliographic record

VenueInternational Journal of Entrepreneurial Behaviour & Research · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsHofstede's cultural dimensions theoryEntrepreneurshipTheory of planned behaviorOriginalityValue (mathematics)Bridge (graph theory)SensemakingSociologyDimension (graph theory)Mechanism (biology)Perspective (graphical)Social psychologyPsychologyPositive economicsEpistemologyPolitical scienceEconomicsPublic relationsControl (management)ManagementComputer scienceCreativity

Abstract

fetched live from OpenAlex

Purpose Under the theory of planned behaviour (TPB), subjective norms are important antecedents of entrepreneurial intent. But little is known about the forces that shape these. Hofstede’s national culture has implicated, but the conceptual distance between it and subjective norms is wide. The purpose of this paper is to explore an intermediate level to propose a mechanism by which national cultures give rise to individual beliefs about entrepreneurship. Design/methodology/approach Uses Q methodology with data from seven countries to discover patterns of beliefs in diverse cultures. Hierarchical clustering characterises an intermediate-level mechanism. Findings In each country, a small number of patterns emerge, two of which are found in every country studied – despite the large cultural differences. Drawing on the institutional logics perspective, a model of individual sensemaking is developed to bridge between monolithic national culture and idiosyncratic subjective norms of individuals, and to explain the commonality of belief patterns observed. Several propositions are suggested for testing the model. Originality/value Reports cultural attitudes towards entrepreneurship at a more granular level than previous research, and thereby discovers the existence of cross-cultural patterns. Proposes a novel model that connects macro forces of national culture with individual precursors of TPB through cultural entrepreneurship.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0020.004
Open science0.0030.001
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 head0.197
GPT teacher head0.421
Teacher spread0.224 · 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

Citations25
Published2017
Admission routes1
Has abstractyes

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