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Record W4379015801 · doi:10.1080/23322373.2023.2187688

Returnee entrepreneurial entry decisions among forced and voluntary returnees in Ethiopia: A comparative study

2023· article· en· W4379015801 on OpenAlexaff
Toli Jembere Amare, Benson Honig

Bibliographic record

VenueAfrica Journal of Management · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTurnoverDemographic economicsBusinessEconomicsManagement

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.320
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

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