Coping Ability and Employment Growth in African Immigrant- Owned Small Businesses in Southern Africa
Bibliographic record
Abstract
Despite the plethora of challenges faced by immigrant-owned businesses, there are still some that are performing well and contributing to employment growth in their respective host nations. Unfortunately, research tends to be skewed towards the examination of these challenges, while scant attention is paid to critical antecedents of the coping ability of immigrant entrepreneurs and employment growth in their businesses. This empirical quantitative study, is a cross-country survey spanning South Africa, Mozambique and Swaziland. It aims to establish the extent to which the independent variables of financial bootstrapping, access to business services and business location play contributory roles in the coping ability of African immigrant entrepreneurs. It also explores the possibility of a relationship between these independent variables and employment growth. The findings reveal that all of the independent variables were considered as important contributors to the coping ability of African immigrant entrepreneurs though financial bootstrapping was ranked highest. However, regression analysis results indicate that a statistically significant relationship was only evident for the hypothesized relationship between access to business services and employment growth. This finding has important practical implications for stakeholders who are committed to supporting African immigrant entrepreneurship endeavours in the Southern Africa region.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".