Build, Rent and Sell: Options for Commercializing New Technologies Arising from University Research
Bibliographic record
Abstract
This research investigates the strategic governance choices made in commercializing new technologies arising from university research. Departing from the traditional licensing vs. start-up approach, it is proposed that there are three primary methods of commercializing these technologies: 1) Build ? creating a new business based on the technology, 2) Rent ? ongoing development and marketing of the technology to established firms that use the technology in their businesses and 3) Sell ? disposition of the technology to an established firm. Using economic theories of the firm, particularly transaction cost economics, it is hypothesized that: the build option is positively associated with firms deriving revenue primarily from product market activity (H1a) and expending resources on both technology development activities and production activities (H2a); the rent and sell options are positively associated with firms deriving revenue primarily from technology market activity (H1b) and expending resources on technology development activities but not on production activities (H2b). the greater the patent or other legal protection (H3), the risk of substitutes (H5) or the dynamism associated with the technology (H8), the greater the likelihood that the technology will be commercialized using the rent option; the greater the tacitness and complexity (H4) or the greater the volatility associated with the technology, the greater the likelihood that the technology will be commercialized using the build or sell options; and the greater the importance of specialized complementary assets, the greater the likelihood that the technology will be commercialized using the sell option (H6). Three studies were conducted providing differing perspectives on the research question. Study #1 examines three start-ups based on new technologies arising from research conducted at the University of Waterloo. Study #2 analyzes the business activities of a number of Canadian and U. S. public start-up firms using archival data. Study #3 is a survey of university faculty members who have had new technologies arising from their academic research put into commercial use. Hypotheses H1a/b, H2a/b and H3 are supported and Hypothesis H7 received more limited support. Evidence for Hypothesis H5 is in the predicted direction but failed to achieve statistical significance. Hypotheses H4, H5, H6 and H8 are not supported.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".