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Record W2012254544 · doi:10.5539/ibr.v5n3p81

A Qualitative Study on Individual Factors Affecting Iranian Women Entrepreneurs’ Growth Orientation

2012· article· en· W2012254544 on OpenAlexvenueno aff
Zahra Arasti, Shirin Majd Shariat Panahi, Behrouz Zarei, Sima Oliaee Rezaee

Bibliographic record

VenueInternational Business Research · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsWomen entrepreneursEntrepreneurshipEntrepreneurial orientationBusinessDemographic economicsSample (material)Qualitative researchPopulationMarketingIdentity (music)EconomicsSociologyDemographySocial science

Abstract

fetched live from OpenAlex

Despite the impressive growth in the number of firms run by women entrepreneurs, most of these businesses continue to remain small and women-owned firms have not grown as fast as male entrepreneurs. There are many reasons that may help explain the growth limitations in women-owned firms. Amongall, growth orientation is an important factor. A common finding in entrepreneurship literature shows that ventures owned by women tend to be smaller than those by men are. This difference can be due toindividual, organizational and environmental factors.Since half of Iran’s population iswomen who are more willing to have higher education and contribution in the society, they deal with more challenges rather than their male counterparts. So attention to the factors affecting growth orientation of their ventures is the same as deliberate economic development and nationalincome. This is a qualitative study to identify individual factors affecting growth orientation in women’s businesses. Data analysis of 11 semi-structured interviews on a sample of women entrepreneurs indicated individual factors in four groups of “goals and aspirations”, “motives”, “female identity” and “personal characteristics”.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
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 head0.136
GPT teacher head0.417
Teacher spread0.281 · 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.

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

Citations9
Published2012
Admission routes1
Has abstractyes

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