Extending the Resource-Based View of Social Entrepreneurship: The Role of Artificial Intelligence in Scaling Impact
Bibliographic record
Abstract
This paper extends the resource-based view (RBV) of social entrepreneurship by introducing artificial intelligence (AI) as a dynamic, integrative capability that enhances the acquisition and optimization of four foundational forms of capital: human, social, political, and financial. While social ventures have long faced constraints in scaling impact due to resource limitations and institutional barriers, AI technologies—such as predictive analytics, machine learning, and natural language processing—offer new pathways for improving operational efficiency, stakeholder engagement, advocacy strategies, and financial sustainability. Through the development of a conceptual model and a series of theoretical propositions, this study positions AI as a transformative force that not only strengthens individual resource domains but also enables synergistic feedback loops across them. In doing so, the paper contributes to emerging debates on technology adoption in hybrid organizations, scalability in resource-constrained contexts, and the evolution of strategic management theory in the digital age. Practical implications are outlined for social entrepreneurs, policymakers, and funders seeking to responsibly integrate AI into social impact ecosystems, and future research directions are proposed to empirically test the framework across sectors and global settings.
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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 | 0.005 |
| 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 | 0.023 |
| Scholarly communication | 0.007 | 0.012 |
| 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".