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Record W2913643380 · doi:10.6000/1929-7092.2019.08.07

Entrepreneurial Orientation and Performance of Small Business in Vryburg Region North West Province South Africa

2019· article· en· W2913643380 on OpenAlexvenueno aff
Olabanji Oni, Edem Korku Agbobli, Chux Gervase Iwu

Bibliographic record

VenueJournal of Reviews on Global Economics · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessOrientation (vector space)Entrepreneurial orientationEconomic geographyGeographyEntrepreneurshipGeometryFinanceMathematics

Abstract

fetched live from OpenAlex

Small businesses play a significant role in job creation, economic growth and development, innovation, competitiveness and poverty alleviation that eventually improve business performance. The purpose of this study was to assess the association between Entrepreneurial orientation (EO) (innovativeness, risk taking, proactiveness and autonomy) and performance of small business in Vryburg area North West Province South Africa(SA). This quantitative study utilised questionnaire for data collection in a survey. The population were small business owners/managers in North West Province South Africa. Simple random sampling method was utilised to obtain participants for the study. The study utilised descriptive and inferential statistics. The result shows that only three attributes (innovativeness, risk taking and proactiveness) influence business performance while no association was found between autonomy and business performance. Additionally, positive relationship exists between the overall EO and the performance of small business. Empirically, the study contributes to the literature on EO and advance recommendations to improve the EO of small business in South Africa. The study recommends that policy makers, owners and managers of small business strategize on enterprise development and better business performance of small business in Vryburg area North West Province South Africa.

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)0.000
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 score0.058
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.023
GPT teacher head0.209
Teacher spread0.187 · 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

Citations34
Published2019
Admission routes1
Has abstractyes

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