Business ecosystems and new venture business models: an exploratory study of participation in the Lead To Win job-creation engine
Why this work is in the frame
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Bibliographic record
Abstract
Technology entrepreneurs are launching and growing new businesses within business ecosystems, but little is known about how ecosystem participation impacts new venture business models. This research is an exploratory study of new venture business models within Lead To Win - a business ecosystem developed as a 'job-creation engine' for Canada's capital region. The three-phase research design is comprised of: 1) a field study of the Lead To Win field setting; 2) a multiple case-study of participating new ventures launched by six founders; 3) development of evidence-based propositions relating ecosystem participation and new venture business models. There are two key findings. First, more intense participation in the ecosystem is associated with higher business model differentiation, sophistication, and extent of change. Second, entrepreneurs participating more intensely in the ecosystem report a greater breadth of benefits.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it