Business Ecosystems and New Venture Business Models : An Exploratory Study of Participation in the Lead To Win Job-Creation Engine
Bibliographic record
Abstract
Technology entrepreneurs are launching and growing new businesses within business ecosystems, but little is known about how ecosystem participation impacts the business models of new ventures.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.A multi-phase research design examines the properties of the field setting, then conducts a multiple case-study of participating new ventures, and develops evidence-based propositions relating ecosystem participation and new venture business models.There are three key findings.First, more intense participation is associated with higher business model differentiation, sophistication, and more changes over time.Second, entrepreneurs participating more intensively in the ecosystem report a greater range of benefits.Third, extant business ecosystem frameworks could not fully describe the Lead To Win job-creation engine; new and better business ecosystem frameworks are needed.An exploratory study of participation in the Lead To Win job-creation engine.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".