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Record W2735411605 · doi:10.1108/ijebr-07-2016-0214

Becoming entrepreneurs: how immigrants developed entrepreneurial identities

2017· article· en· W2735411605 on OpenAlexaboutno aff
Zhang Zhen, Douglas Chun

Bibliographic record

VenueInternational Journal of Entrepreneurial Behaviour & Research · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsEntrepreneurshipImmigrationIdentity (music)OriginalityAgency (philosophy)SociologyValue (mathematics)Qualitative researchMarketingPublic relationsBusinessPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to investigate the important process of how entrepreneurial identity is formed and constructed, with the perspective that entrepreneurial identity is social and dynamic, constantly shaped by various life episodes and human interactions, rather than static and unchanging. Design/methodology/approach This qualitative study comprises 30 in-depth interviews with Chinese immigrants in West Canada. These immigrants had been employed professionals under the “Skilled Workers” immigration category but later became entrepreneurs. None of the entrepreneurs in this study had prior business ownership experience, and many of them said that they had never thought about running businesses until they came to Canada. Findings A process model of entrepreneurial identity construction is presented. This paper advances the literature on entrepreneurship through the identification of three stages in the development of entrepreneurial identity: identity exploration, entrepreneurial mindsets building, and narrative development. Originality/value This study has important implications for the understanding of the exploratory and discovery mode of entrepreneurial identity construction. This study also moves away from the contextual and structural hypotheses as the sole explanations for the high rate of self-employment among immigrant entrepreneurs, and provides a useful starting point for a deeper understanding of the agency of immigrant entrepreneurs.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0060.005
Open science0.0040.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.110
GPT teacher head0.379
Teacher spread0.269 · 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.

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

Citations53
Published2017
Admission routes1
Has abstractyes

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