Local relationships matter! The impact of intellectual capital on entrepreneurial bricolage in African social entrepreneurs
Bibliographic record
Abstract
Social entrepreneurs aim to create societal value while pursuing financial sustainability. However, they typically face several challenges and constraints when operating in resource‐scarce environments. For this reason, social entrepreneurs typically engage in entrepreneurial bricolage, which is described as a process of using whatever tools and resources necessary that are immediately available. The behavioral theory of entrepreneurial bricolage attempts to understand what entrepreneurs do when faced with resource constraints. In this vital process, little empirical research has been conducted to investigate what drives social entrepreneurs to engage in such a way. This study aims to understand the antecedents of bricolage and, in particular, empirically test its link to intellectual capital. A survey was administered to 115 social entrepreneurs from Ghana and Sierra Leone. Data analysis shows that relational capital plays a crucial role in driving social entrepreneurs to engage with bricolage. In fact, the quality of local relationships and external support received (by suppliers, customers, and communities) is positively related and statistically significant with entrepreneurial bricolage. The results of this study not only extend the academic literature of bricolage in social entrepreneurship but also point out the focal role of relational capital as an enabler toward effectively operating in difficult conditions in developing African countries. We thus provide theoretical implications to the field of social entrepreneurship through the lens of intellectual capital and knowledge management. Practical implications are provided to social entrepreneurs operating in developing countries, such as government, NGOs, and agencies seeking to support entrepreneurship initiatives. Limitations and future research opportunities are suggested as well.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".