Entrepreneurial Orientation and Performance of Small Business in Vryburg Region North West Province South Africa
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".