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Record W2588004512 · doi:10.5539/ibr.v10n3p148

Entrepreneurs, Strategy, Venturing Process and Industry Environment: A Configurational Approach to New Venture Emergence

2017· article· en· W2588004512 on OpenAlexvenueno aff
Qian Ye

Bibliographic record

VenueInternational Business Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Comparative Analysis Research
Canadian institutionsnot available
Fundersnot available
KeywordsQualitative comparative analysisCausality (physics)Independence (probability theory)Variance (accounting)Dimension (graph theory)Process (computing)Complement (music)BusinessIndustrial organizationMarketingEconomicsEconometricsComputer scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

This study applied qualitative comparative analysis (QCA) as a viable method for new venture emergence. Through the inclusion of four-dimension variables from individuals, founding strategy, startup process, and environment, the study illuminates the importance of factor independence, factor interdependence, and factor complex causality in understanding new venture emergence. This study demonstrates the benefits of QCA approach to entrepreneurship research, a complement to the conventional variance-based approach. Limitations of QCA and future research directions are provided.

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)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.003
Science and technology studies0.0030.009
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.237
GPT teacher head0.509
Teacher spread0.272 · 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 designQualitative
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

Citations6
Published2017
Admission routes1
Has abstractyes

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