Sink, swim, or drift: How social enterprises use supply chain social capital to balance tensions between impact and viability
Bibliographic record
Abstract
Abstract Social enterprises seek solutions for some of society's most pressing problems through the development of commercially viable businesses. However, pursuing social impact is often at odds with financial viability, and social enterprises need to engage with a wide range of stakeholders to access tangible and intangible resources to overcome this tension. Although the current literature emphasizes the need for social capital within social enterprises' supply chain relationships, it does not consider the costs associated with the development of such capital. This article examines how social enterprises develop social capital in their supply chain relationships and how this social capital affects their ability to pursue impact and viability. Using data from in‐depth interviews with nine social enterprises, the findings indicate that the roles and positions of beneficiaries in supply chains determine the appropriate forms of social capital needed to sustain simultaneous impact and viability. The empirical insights highlight that structural and relational capital are most valuable within core supply chain relationships, whereas cognitive capital is most beneficial within peripheral relationships aimed at enhancing competitiveness. Further, social enterprises sometimes relinquish power in their supply chain relationships to prioritize impact but develop relational capital to mitigate threats of opportunism. This study advances a contingent view of social capital in cross‐sectoral supply chain relationships and provides valuable implications for managers pursuing 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".