Host versus home country influence on the immigrant entrepreneurial process: an imprinting perspective
Bibliographic record
Abstract
Abstract Since its first use in organisational research, nearly five decades ago, imprinting has gained recognition in entrepreneurship studies. Accordingly, this study utilises the behavioural concept to develop new theorisations to account for the entrepreneurial processes of immigrant entrepreneurs. It pays attention on its effects on immigrant entrepreneurs, particularly when it comes to their decision–making and behaviours towards business creation in Canada. A comprehensive analysis of a dataset generated from a systematically selected group of immigrant entrepreneurs revealed the complexity of their imprints at various stages of their entrepreneurial cycle in the North American country. It emerged that imprinting not only modified their behaviours, attitudes and cognition, but also shaped the trajectory of their entrepreneurial processes. That is, their imprints had an effect on how they identified business opportunities, the types of businesses they pursued, their level of entrepreneurial drive, and the types of resources they acquired or accessed in their new environment. Notably, following a period of normalisation in their new surroundings, their original imprints changed due to diminishing affinity with their country-of-origin. This holds research and policy implications as it uncovers an unfolding but less-understood entrepreneurship phenomenon.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".