Bibliographic record
Abstract
Purpose Overseas work experiences have played a critical role in venture creation and success, yet the impact of overseas work experience on returnee entrepreneurs’ venture capital funding in the Chinese market remains understudied. This paper aims to explore the impact of returnee entrepreneurs’ overseas experiences on their opportunities of venture capital funding in China to help better understand the potential benefits that overseas work experiences bring to emerging markets. Design/methodology/approach The authors have conducted a two-year inductive field study to explore the impact of overseas experiences on Chinese returnee entrepreneurs’ funding in the Chinese market with in-depth interviews with returnee capital seekers (or the venture founders) and capital providers. Findings The results show that returnee entrepreneurs are more likely to succeed in acquiring financial resources for their new ventures if they skillfully present their overseas work experiences and international networks to manage the impression constructed by capital providers. Originality/value This research sheds light on how returnee entrepreneurs use impression management in external resource acquisition. It is clear that overseas experience has been regarded a symbol of personal capability closely associated with advanced knowledge and valuable human and social capital in the Chinese context. Resource holders appreciate such an association. The authors suggest that returnee entrepreneurs concerned about how to effectively acquire external resources should reflect upon the ways of presenting themselves to potential investors and fostering a positive image that encourages investors to commit to their ventures.
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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 | 0.004 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".