Event-Driven Evolution in Entrepreneurial Ecosystems: Insights From Unicorn Firm Acquisitions
Bibliographic record
Abstract
Entrepreneurial ecosystems are dynamic contexts that evolve through the interactions of heterogenous actors. Current explanations of these evolutionary processes predominantly centre on relatively stable sets of features. As an alternative to feature-oriented explanations, we introduce an event-based approach to investigate the microfoundations of entrepreneurial ecosystem evolution. This study sought to understand how ecosystem actors ascribe meaning to one type of event, a unicorn firm acquisition, through engagement in discourse. Social media discourse data from before and after unicorn firm acquisitions in two Canadian entrepreneurial ecosystems supported a mixed-methods study design. This study quantitively tested for changes in sentiment and employed thematic and cross-case techniques for qualitative analyses. The findings indicate that shifts in sentiment and attitudes are supported by meta-narratives that frame interpretations of entrepreneurship. In doing so, we provide a microfoundations explanation for entrepreneurial ecosystem evolution via events, while validating event-based research in this area.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".