Social Embeddedness Strategies of Sustainable Startups: Insights from an Emerging Economy
Bibliographic record
Abstract
Social embeddedness describes the extent to which firms are integrated into a social network in different situations and is an important concept in the entrepreneurship literature. Much of the existing research on embeddedness focuses on how entrepreneurs integrate into their host countries or the business activities of transnational entrepreneurs who operate across both their host and home countries. While a limited number of studies have examined sustainable entrepreneurs, previous studies have not sufficiently examined the nature of entrepreneurs’ social embeddedness and its effect on their sustainable entrepreneurial activities. This study seeks to understand how sustainable entrepreneurs utilize their social embeddedness when navigating business challenges. This study followed a multiple-case study approach based on data collected from in-depth inquiries into eight founders of sustainable startups in Nigeria. The findings show that sustainable entrepreneurs use social embeddedness as a strategy to navigate challenges encountered at different stages of their business. The findings make a theoretical contribution by describing how sustainable entrepreneurs use social embeddedness as a strategy to navigate business challenges in a developing country context. The findings offer implications for policymakers of emerging economies and sustainable entrepreneurship support organizations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".