The Impact of Being a Spinout on Startup Performance: Considering Open Innovation as a Moderator
Bibliographic record
Abstract
This research aims to deepen our knowledge of the impact of being a spinout and open innovation on startup performance and analyzes how the relationship between spinouts and open innovation strategy can affect startup performance. To achieve this, I examine the impact of being a spinout on startup performance (i.e., total capital raised) by considering open innovation strategy as a moderator. Using data from TechStars between 2010 and 2019, GitHub, LinkedIn, Rocket Reach, and Crunchbase as main resources and applying quantitative analysis, my analyses indicate that spinouts and open innovation can improve startup performance by increasing the amount of capital they raise. I investigate whether an open innovation strategy moderates the relationship between being a spinout and startup performance, but find no effect. The results have implications for the literatures on spinouts, entrepreneurship, strategy, and innovation. Spinouts can bring rich resources such as knowledge and networks from their parent companies to boost their performance. Moreover, startups can use open innovation as a useful strategy in strengthening their startup performance.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".