The World at Their Feet? A Longitudinal Analysis of Born Global Startups and Equity Crowdfunding
Bibliographic record
Abstract
This paper examines two determinants of equity crowdfunding: success in raising targeted amounts during the fundraising period, and longitudinally, the degree of firm survival following the fundraising. We study how a particularly resource constrained type of start-up firm, born global (international new venture), performs when attempting to raise equity through a crowdfunding platform. Our methodology combines inductive qualitative analysis of the investment narrative found in the disclosure documentation, with two-stage multivariate analysis to model longitudinal performance. Based on the modelling, we classify firms as born globals, traditional (slow) globals, born locals, and anchored locals. Among the four types of firms, we find that that born globals are the least likely to enjoy fundraising success but among the more likely to survive post-campaign. Thus, the early internationalization of born globals appears to offset liabilities of newness and foreignness associated with international strategy. Further research is suggested on why crowdfunders do not appreciate the signals in the strategic narrative that lead to better investment outcomes. Given the promising nexus identified between born globals and crowdfunding, additional research is also recommended on how crowdfunding could play a larger role in innovation finance ecosystems.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".