What to Do with Technology? From Valuation to Commercialization
Bibliographic record
Abstract
Any firm seeking to commercialize a technological innovation must make a variety of important strategic assessments and choices. What is the technology’s value in the competitive landscape? Should the firm license out or sell its technology for other firms to commercialize, or should it commercialize the technology in-house? If the firm commercializes internally, in what market(s) does the firm position itself? Where in the value chain and technological system does it choose to locate? This series of interrelated questions is relevant to long-standing concerns in innovation management, strategy, and entrepreneurship, but each remains an important open question for research and practice. With four presentations at the frontier of current research featuring complementary theoretical perspectives, data sources, and empirical methods, this symposium sheds light on each of these four questions. Together, the presentations and discussants offer a comprehensive perspective on many of the supply-side strategic choices about “what to do with technology”––namely, technology valuation, activity in markets for technology, and decisions between different commercialization avenues––faced by managers and firms. Competitor portfolios and the value of new technologies Author: Simen Gaens; KU Leuven Author: Bruno Cassiman; KU Leuven Author: Jeroen Van den Bosch; KU Leuven Keep, license or sell? Examining the antecedents of strategic intellectual property transfer Author: Dafna Bearson; Harvard Business School Explaining applications of technology: Evidence from startups commercializing emerging technologies Author: James Addis; University of Toronto Pre-entry knowledge of entrepreneurs and market strategy Author: Seojin Kim; Drexel University
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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".