A hybrid approach to innovation by social enterprises: lessons from Africa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it