In pursuit of diversification opportunities, efficiency, and revenue diversification: A generalization and extension for social entrepreneurship
Bibliographic record
Abstract
Abstract Research Summary Our study generalizes and extends a cumulative body of research on diversification into the contexts of social entrepreneurship. We examine the extent to which internationalization and program diversification affect a coveted, yet understudied, outcome for social organizations—revenue diversification—and investigate efficiency as a boundary condition of those relationships. Our longitudinal analyses, based on roughly 53,000 observations of Canadian nonprofits that engage in social entrepreneurship, indicate that program diversification is positively related to nonprofits' revenue diversification, but internationalization is not. Further, we found that efficiency compliments these relationships. Our findings are partially in line with research from the for‐profit literature, but also extend this line of research by suggesting that different types of diversification are associated with revenue diversification in the context of social entrepreneurship. Managerial Summary Founders, executives, and managers of social organizations often strategically design and implement programs and internationalization strategies to achieve better organizational outcomes. Our results reveal that pursuing program diversification opportunities are related to revenue diversification and that highly efficient nonprofits are more likely to increase their revenue diversification as they pursue more program diversification and internationalization opportunities. Social entrepreneurs and their organizations may therefore learn from, build upon, and to some degree tailor theories and strategies of for‐profit organizations to better serve their social and environmental mission and ensure long‐term financial stability.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".