Leveraging Social and Intellectual Capital for Social Entrepreneurship: A Model for Sustainable Business Practices in an Uncertain Environment
Bibliographic record
Abstract
Social entrepreneurship helps solve social issues and bridges public and private sectors. This study uses a comprehensive framework to examine social, intellectual, and external risks in social entrepreneurship and its effects on society and the environment. Using the literature review’s variables, this study developed a conceptual model. The empirical research is based on a survey of 252 social entrepreneurs from different industrial/service sectors. The findings show that intellectual capital helps identify and seize social opportunities. Social capital—social networks, knowledge, trust, and critical resources—guide social entrepreneurs through complex social business environments. The study’s novel approach to external uncertainty in social entrepreneurship shows that firms can design risk-resilient strategies to build sustainable business models by considering external uncertainty. Organizations can consider social entrepreneurship’s social and environmental impacts to create businesses that address the root causes of societal issues. The study adds to theoretical understanding by incorporating a variety of factors that influence social entrepreneurship function and frameworks in driving social and environmental impact.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".