Science‐based innovation via university spin‐offs: the influence of intangible assets
Bibliographic record
Abstract
University spin‐offs (USOs) have attracted significant attention from scholars and policymakers as an important mechanism for science‐based innovation. The debate on how USOs generate innovation outcomes has often focused on tangible assets, while the role ofintangible assetshas been less explored and remains loosely defined. Yet emerging research suggests that intangible assets, especially in the early stages of a USO's lifecycle, have a critical influence on its survival and future success, highlighting a need for a better understanding of how intangible assets enable science‐based innovation through USOs. Drawing from several streams of literature, we define intangible assets in the context of science‐based innovation through USOs:an intangible asset is a resource that is non‐physical, non‐financial, has long life, and has potential to provide future benefits to the owner.Based on this working definition, we conduct a systematic literature review of the leading innovation management journals and inductively derive a framework outlining the antecedents, processes, and outcomes of science‐based innovation through USOs, focusing on the influence of intangible assets. The framework identifies the categories of resources which can enhance or hinder science‐based innovation through USOs. Such categorization reveals fruitful directions for future research such as a deeper examination of societal outcomes. We conclude by offering recommendations for scholars, practitioners, and policymakers to better leverage intangible assets to enhance science‐based innovation.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".