Does co-creation drive the creation and success of social enterprises? Evidence from a quasi-natural experiment
Bibliographic record
Abstract
Purpose In developing countries, social enterprise creation is often said to offer a promising solution when public institutions struggle to uplift society from social, economic and environmental challenges. Despite social enterprises’ vital role, they still face challenges such as financial sustainability, market penetration, access to financial resources and maintaining social enterprises’ dual (social and economic) nature. The recent discussion about the positive effects of co-creation processes in institutional creation paves the path for exploring the potential of this approach in creating social enterprise. Therefore, the purpose of this study is to explore an integrative social innovation model based on a co-creation process to help social enterprises address their challenges. Design/methodology/approach This study explores a social innovation model based on co-creation and quantitatively analyzes its impact using the difference-in-differences approach. It used STATA 18 to analyze panel data from the World Bank, Pakistan Bureau of Statistics, Small and Medium Enterprises Development Authority and the United States Agency for International Development-funded PYWD project, covering 2013–2020. Findings This study’s findings indicate a significant positive impact on the creation and growth of social enterprises over time, which is further strengthened in the presence of covariates such as social infrastructure availability, education investment, urbanization and public support institutions. Originality/value This study uniquely emphasizes the ways that can curtail social enterprises to subside potential uncertainties about their sustainability.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".