Mobilizing Fundamental Research Through Entrepreneurial Capabilities
Bibliographic record
Abstract
Academic scientists play a critical role in creating inventions, but often lack the business acumen necessary to commercialize their discoveries. This study introduces a process model of how, why and when academics can utilize the entrepreneurial capability of technology-market matching (TMM), thereby facilitating the creation of impactful academic spinoffs. While prior research has focused on a simplistic view of product-market fit for commercialization, we explore the more nuanced approach of TMM in the years prior to firm formation through a data-rich, longitudinal case study of the commercialization journey of a star scientist entrepreneur and his lab, theorizing how academics scientists engage in iterative learning and adaptation. At the heart of the process are three dynamics: first, cyclical “nexus switching” between a platform technology and multiple markets to systematically refine technology and narrow market focus; second, transitions in academic scientist’s market awareness leading to the creation of a market-oriented academic spinoff; third, academic scientists iteratively engaging in selective targeting, deliberate excluding, or simmering prospective markets to achieve the right technology-market fit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".