Intellectual capital in East and West African social enterprises
Bibliographic record
Abstract
Abstract Purpose This study aims to identify the main factors of knowledge assets (i.e., human, relational and structural capital) that affect the value creation process of social enterprises located in East and West Africa. Design A survey was administered to a sample of social enterprises located in developing countries such as Kenya, Uganda, Sierra Leone and Ghana. The survey was designed to gather background information about social enterprises, social entrepreneurs as well as data pertaining to intellectual capital. Therefore, descriptive statistical analysis, principal component analysis and Pearson correlations were employed to identify the main components of IC for African SEs and the inter‐relationship among intellectual capital components. Findings Research findings confirmed that human capital (i.e., a social entrepreneur's knowledge), relational capital (i.e., local and global relationship quality) and structural capital (i.e., long‐term and up‐to‐date firm knowledge) were validated as important resources for African SEs in the value creation process. Moreover, correlation analysis showed that human capital and relational capital were positively correlated; whereas structural capital was positively correlated with the local and global relationship's quality and with the social entrepreneur's skills. Limitations The main limitations concern the heterogeneity and the restricted sample size due to challenges in the data gathering process. Moreover, the results could potentially be influenced by the context and the low response rate. However, this study can represent a starting point for future research in this unique but important research setting. Originality This study can be considered original for several reasons. First, empirical evidence on knowledge assets in developing countries in Africa is still scarce, despite the potential of being a new frontier for intellectual capital studies and social and economic growth. Second, the use of a survey method as an IC measurement tool in this context is unique. Finally, this study helps in providing a platform for further investigation in Africa.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".