Entrepreneurship, Teamwork and the Commercialization of Ideas
Bibliographic record
Abstract
Entrepreneurs are often advised to team up with other people in order to increase the chances to commercialize their ideas. Less known is the mechanism through which this occurs, and whether there is empirical evidence on the veracity of this advice and the measurement of the returns to team up with others. Understanding teamwork, however, matters for economic policy and business strategy. Without such knowledge, it is likely that existing economic policy addressed at promoting collaboration between entrepreneurs and partners is not optimal. The paper estimates the value added of teaming up with others for the commercialization of ideas. Estimating the effect of teaming up in the probability of commercialization and in the returns of commercializing ideas is subtle. It is subtle because teaming up in a project is endogenous to the quality of the project. For instance, partners are more likely to team up with better projects in order to compensate their opportunity cost of providing technical skills, social capital and financial resources. Similarly, entrepreneurs with quality projects are more likely to be financially constrained, and consequently they seek financial help through partners to commercialize their ideas. In our context, an implication of the endogeneity problem is that the fact that projects with teams have higher probability and larger profits is not only reflecting the value added of partners, but also captures that entrepreneurs with better projects are more likely to seek partners and that partners prefer to team up with better projects. To separately identify the quality of the project from the value added by partners, we propose a structural model of teamwork. We estimate the parameters of the model using a survey of individual inventors based on the list of independent inventors from the Canadian Innovation Center.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".