A hybrid approach to innovation by social enterprises: lessons from Africa
Bibliographic record
Abstract
Purpose – This paper aims to provide a detailed analysis of the key capabilities needed for social enterprises to succeed in the context of extreme poverty. Facilitating growth and alleviating poverty in the world’s most impoverished regions requires introducing innovative solutions to achieve social impact while generating financial returns. Design/methodology/approach – This paper studies two social enterprises operating in Africa. Semi-structured interviewers were conducted with co-founders of the organizations. The transcribed interviews were analyzed through an open coding process, iterated to overarching categories, and compared between the organizations using a grounded theory approach. Secondary archival data and respondent validation were used to triangulate these findings. Findings – This paper proposes a model that highlights five key capabilities social enterprises need to tackle complex societal challenges while overcoming resource constraints and institutional voids. The processes followed to develop and deploy these capabilities are delineated, and the necessity of hybrid mechanisms that blend non-profit and private-sector approaches is shown as a key enabler for social enterprises to meet their dual objectives. Research limitations/implications – This research is limited to two cases studies from two different industries in Africa. Future research would refine and extend the proposed model to increase generalizability. Originality/value – This paper addresses a gap in the literature on understanding innovation and entrepreneurship in Africa, and it proposes a model for innovation derived from data. This paper also offers insights to the growing community of social entrepreneurs looking to develop sustainable solutions to societal challenges.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".