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Record W3109918590 · doi:10.1007/s11187-020-00435-8

Women entrepreneurs’ progress in the venturing process: the impact of risk aversion and culture

2020· article· en· W3109918590 on OpenAlexaff
Daniela Gimenez-Jimenez, Linda F. Edelman, Alexandra Dawson, Andrea Calabrò

Bibliographic record

VenueSmall Business Economics · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsConcordia University
FundersTechnische Universität München
KeywordsEntrepreneurshipGlobeNormativeRisk aversion (psychology)Perspective (graphical)Sample (material)MarketingPerceptionUncertainty avoidanceProcess (computing)PsychologyBusinessSociologyDemographic economicsSocial psychologyEconomicsPolitical scienceFinancial economicsMarket economyCollectivism

Abstract

fetched live from OpenAlex

Abstract We explore the gendered impact of risk aversion and country-level culture on nascent student entrepreneurs’ progress in the venturing process. Combining country-level cultural normative variables from the 2004 Global Leadership and Organizational Behavior Effectiveness (GLOBE) survey with data from the 2013/2014 Global University Entrepreneurial Student Spirit Study (GUESSS), our sample consists of 1552 nascent student entrepreneurs from 11 countries. We start with the assumption that perceptions of risk-taking behaviors are not gendered. We then split our sample, finding that, for women, perceptions of risk-taking behaviors are associated with less progress in the venturing process; however, starting a new venture in a socially supportive culture moderates that relationship. For men, neither risk-taking behavior nor country cultural variables are related to their progress in the venturing process. Our study highlights both the importance of country-level contextual variables in entrepreneurship and the need to employ a gendered perspective when studying nascent 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 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)0.002
metaresearch head score (Gemma)0.008
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 score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.215
Teacher spread0.197 · 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

Citations98
Published2020
Admission routes1
Has abstractyes

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