Effect of Inventors’ Characteristics on Commercialization Potential of their Inventions: The Case of Nanotechnology in Canada
Bibliographic record
Abstract
Innovation may significantly contribute to building and maintaining competitive advantage of companies. It is therefore imperative to invest in research activities which will lead to innova-tive accomplishments that can be successfully patented. However, only a small portion of these patents will ever be commercialized in the form of a new product introduction or a patent license. The main purpose of this thesis is to examine various factors which might increase the commercialization potential of the patented inventions. We investigate the impact of collabo-ration patterns of inventors and also their various individual characteristics and the attributes related to their work on the commercialization potential of the inventions. While focusing on Canadian nanotechnology innovation ecosystem we exploit the data spanning 25 years of United States Patent Trademark Office (USPTO) patent documents. Based on the co-inventor-ship information captured in the patents the network of inventors’ collaborations is developed. To evaluate collaboration patterns of inventors the relevant structural properties assessing collaborative intensity and access to knowledge and ideas through the network are measured. Fur-thermore, various attributes and features of inventors related to their education, working expe-rience and to the characteristics of their workplace are collected via Google and LinkedIn. The statistical model assessing the impact of the various collaboration and individual characteristics of inventors on the commercialization potential of their inventions is then developed. The results show that those inventors who collaborate with higher number of other inventors and those who occupy more central positions in the collaboration network and thus enjoy an enhanced access to knowledge and ideas tend to produce inventions with higher commercialization potential. Moreover, the results also indicate that having graduate education in engineering and being employed in non-academic institution, especially in companies with lower number of employees are factors which may enhance the commercialization potential of the patented inventions as well.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".