Can the External Environment Generate Better Economic Performance in Academic Spin-Offs?
Bibliographic record
Abstract
The strategic stimulus to economic development is innovation. It was defined by Schumpeter as the commercial or industrial application of something new (product, process, production method). New product developments grew out of process innovation, particularly the development of components made from new materials, and the techniques to produce them the value of process innovation is proportional to the level of output produced by a given firm. Based on this we can distinguish two different life cycles: the life cycle of product technology and life cycle process technology. It’s appropriate to understand if and how long these innovation processes lead to positive economic results. The innovation capabilities are the driver of long-term success. The relation should remain significant beyond short-term earnings or become even greater for earnings of a longer-term. The length of a firm’s innovation cycle appears to be a determinant of the relationship between enhanced innovation capabilities and future earnings. The paper proposes a framework to evaluate the impact of academic spin-offs at the local level. Spin-off creation is the most complex way of commercializing academic research but has the highest potential impact on the local context. We develop a framework that takes into account the direct and indirect impacts of spin-offs. In the empirical part of the paper, we apply this framework to a sample of Italian spin-offs between 2001 and 2017. The empirical analysis shows that measured in quantitative terms, the impact of spin-offs on the local economy is quite small. Using the selected variables, It's possible to affirm that the presence of business incubators represents an element capable of positively influencing the performance of academic spin-offs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".