Returnee entrepreneurial entry decisions among forced and voluntary returnees in Ethiopia: A comparative study
Bibliographic record
Abstract
Returnee entrepreneurship has become an important topic of interest due both to the increasing number of return migrants and the particular nature of their entrepreneurial activities. In some cases, such as in Taiwan, China, and Israel, voluntary returnees have made a significant impact on their home country’s economic development. However, some expatriates are forced to return due to rapid changes in the political and economic situations of their host countries. We compare and examine these two different cohorts in Ethiopia to understand what attributes are transportable and facilitate entrepreneurship, as well as barriers for the two different groups. Scholarly understanding of what drives returnee entrepreneurial entry decisions remains limited, even more so regarding sub-Sahara Africa. Using the mixed embeddedness perspective, this paper aims to unveil the multi-level drivers of returnee entrepreneurial entry decisions by comparing forced and voluntary returnees to Ethiopia. Based on in-depth interviews with 25 returnees, abductively, the findings indicate the interactive influence of personal and interpersonal factors, simultaneous engagement, and opportunity promise on returnee entrepreneurial entry decisions. Specifically, for the voluntary returnees, childhood aspirations, altruistic desire, simultaneous engagement, and nostalgia, coupled with migration capital and opportunity promise influence their business entry decisions. For the forced returnees, lack of options, regrets about migration, preconceptions, tacit capital, and government support drive their entry decisions. We discuss how these factors are contingent on migrants’ pre-, post-, and during-migration conditions in facilitating returnee entrepreneurship. We also illuminate the distinctive differences between forced and voluntary returnees. Implications for theory and practice are indicated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".