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Record W3047314568 · doi:10.5430/ijhe.v9n5p259

Gender Differences in Entrepreneurial Attitudes and Constraints: Do the Constraints Predict University Agriculture Graduates’ Attitudes towards Entrepreneurship?

2020· article· en· W3047314568 on OpenAlexvenueno aff
Som Pal Baliyan, Paseka Andrew Mosia, Pritika Singh Baliyan

Bibliographic record

VenueInternational Journal of Higher Education · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsEntrepreneurshipEmployabilityAgricultureDescriptive statisticsRanking (information retrieval)PsychologyMarketingDemographic economicsEconomic growthEconomicsBusinessGeographyMathematicsFinance

Abstract

fetched live from OpenAlex

This quantitative study analyzed and predicted gender differences of agriculture graduates’ attitudes towards and challenges in entrepreneurship in Botswana. The study adopted a descriptive and correlational survey research design. A valid and reliable questionnaire was used for data collection through a survey of randomly sampled 149 final year agriculture graduate students (n=149). Inferential statistical tools of Independent t-test and Regression analysis were used for data analysis. The findings of the study determined three important attitudinal factors as: entrepreneurship results in economic growth of a country, employability and income generation and, entrepreneurship improves individual and social growth. Three important constraints in entrepreneurship were lack of land, lack of proper infrastructure and, lack of capital. These top attitudinal factors as well as constraints were the same for the male and female graduates despite of their ranking and importance. A gender difference in students’ attitudes towards entrepreneurship was established while no gender difference in the challenges in entrepreneurship was found. Out of fifteen constraints in entrepreneurship under study, only three constraints namely, lack of land, high competition in market and lack of capital, were determined as significant predictors of the graduates’ attitudes towards entrepreneurship. It is recommended that these three factors be made priorities while making policies for entrepreneurship development in the country. Further study is recommended to explore the perceptions of graduates on the possible ways to improve on these three predicting constraints and explore latent constraints predicting graduates’ attitude towards entrepreneurship. Those findings may provide better ideas in planning policies for entrepreneurial development among agriculture graduates in Botswana.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.029
GPT teacher head0.263
Teacher spread0.233 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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