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Record W4380354748 · doi:10.1108/mbe-02-2022-0034

Factors affecting the performance of micro-level women entrepreneurs: a comparative study between UAE and India

2023· article· en· W4380354748 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMeasuring Business Excellence · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsOriginalityAffect (linguistics)EntrepreneurshipStructural equation modelingBusinessMarketingEntrepreneurial orientationConceptual modelCompetitive advantageWomen entrepreneursPsychologyCreativityComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

Purpose The study investigates the impact of various factors that affect the business performance of micro-level women entrepreneurs in the UAE and India. Design/methodology/approach A conceptual model including the factors that impact the performance of micro-level women entrepreneurs is proposed. The proposed model was validated with data collected through a structured questionnaire based on a cross-sectional survey conducted in the UAE and India. The collected data was analyzed using the structural equations modeling approach. Findings Findings revealed that factors such as competitive aggressiveness, incubation, innovativeness, market orientation and risk-taking propensity have a positive impact on business performance and growth in both countries. Factors like training, learning and finance orientation did not affect business performance. Originality/value Gender plays an essential and influential role in developing countries with regard to entrepreneurship. This research attempts to uncover the often-neglected area of women 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.

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 score0.012
Threshold uncertainty score0.785

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.109
GPT teacher head0.261
Teacher spread0.151 · 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