Science‐based innovation via university spin‐offs: the influence of intangible assets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 of intangible assets has 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it