The Role of Embeddedness of Migrant Start-ups in Local Entrepreneurial Ecosystems During the COVID-19 Crisis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As with all start-ups, the COVID-19 pandemic has led to a changing environment for migrant start-ups. These changes have posed many challenges to altering strategic behaviour and approaches to driving business. We explored migrant start-ups’ embeddedness in entrepreneurial ecosystems by analysing data from 14 semi-structured interviews with start-ups from Berlin's knowledge-intensive business services sector. We argue that the success of migrant start-ups during crises is dependent mainly on the embeddedness in the local entrepreneurial ecosystem. Thus, we expect entrepreneurs to utilise local networks, infrastructures and interactions to help them cope with the challenges and pave the way for local and international business activities. Our results indicate that embedding in local entrepreneurial ecosystems and a sense of belonging, especially during the business formation phase, play a vital role for migrant start-ups in general and crisis. Revitalising the concept of local embeddedness while considering business development stages, this study challenges the prevailing notion of transnational networks as the sole determinant of entrepreneurial success. Instead, we advocate for greater recognition of the significance of accessing local resources, including market knowledge, social relationships, and institutional support, as fundamental factors driving business development and crisis management within the host country. By recognising and nurturing these local resources, policymakers and support organisations can create an enabling environment that empowers migrant start-ups to thrive, adapt, and contribute to the local EE and economic wealth.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it