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Record W3046439278 · doi:10.5267/j.msl.2020.7.023

Women’s entrepreneurship and local wisdom: The role of sustainable subjective wellbeing

2020· article· en· W3046439278 on OpenAlexvenueno aff
I Gusti Ayu Purnamawati, Made Suyana Utama, I Wayan Suartana, A.A.I.N. Marhaeni

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsEntrepreneurshipPsychologySocial psychologySociologyBusiness

Abstract

fetched live from OpenAlex

The research objective is to analyze the effect of women's empowerment, bricolage, and entrepreneurial orientation, in improving business performance and life satisfaction perceived by women entrepreneurs through subjective well-being. The sampling technique used is non-probability sampling and data collection methods through non-behavioral observation, structured interviews, and in-depth interviews. The research data were sourced from primary data through questionnaires and interviews and used a sample of 113 weaving entrepreneurs in Bali Province. Data analysis techniques with descriptive statistics and Structural Equations Model using Partial Least Square. The results showed that: women's empowerment and entrepreneurial orientation influences business networks. Entrepreneurial orientation, business networks and bricolage have a significant effect on business performance. Empowering women has no significant effect on business performance. But overall, women's empowerment, entrepreneurial orientation, business networks and business performance have a significant effect on subjective well-being. Bricolage strengthens the role of women's empowerment in the business performance of weaving owners. The positive influence of women's empowerment and entrepreneurial orientation through business networks on business performance and subjective well-being. Business performance mediates the influence of women's empowerment, entrepreneurial orientation, and business networks on subjective well-being.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.307
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.008
GPT teacher head0.192
Teacher spread0.184 · 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
Published2020
Admission routes1
Has abstractyes

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