Sustaining Family Businesses through Business Incubation: An Africa-Focused Review
Bibliographic record
Abstract
The influence of business incubation systems on family businesses in African economies has not been thoroughly investigated despite the potential contribution of family businesses to Africa’s economic expansion and the attainment of development goals outlined in the Africa Development Agenda 2063 and the Sustainable Development Goals. Therefore, this study investigates the potential benefits that family businesses in Africa can derive from engaging in business incubation. This study utilised an integrative literature review methodology to investigate the research question. Twenty-three peer-reviewed articles were systematically selected from the Scopus, Web of Science, and Google Scholar databases using the following combination of phrases: “family business” and either “business incubation” or “business incubator”. The findings suggest ways to create a mutually beneficial relationship between family businesses and business incubators to improve long-term sustainability, promote collaboration, facilitate knowledge transfer, and foster an entrepreneurial ecosystem. It also recognises challenges, such as cultural alignment in family businesses. Business incubators in Africa can improve the sustainability of family businesses, such as during the succession, by offering support, resources, and guidance. The South African experience is a role model for the rest of the continent, in this regard. Future research should broaden the sources beyond the three databases utilised, including non-peer-reviewed sources such as grey literature, and extend the focus beyond developing economies.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".