Exploiting new university technologies in product innovation: an empirical of the information and communications technology industry of Canada
Bibliographic record
Abstract
Universities are important sources of new technological knowledge for firms. This study examines cases of new university technology used specifically in new product innovation. Survey data was collected on 66 new product development projects within 52 established companies involving new technology from 26 universities. The companies were sampled from the information and communications technology sector in Canada. A key objective was to understand strategic importance of the new university technology to the firms. This paper presents the rich descriptive data that was obtained from the research model testing to be done at a later date. Data is presented for four main areas: general project information; product and market characteristics; technology transfer issues; and technological/strategic fit. Evidence suggests that new university technology is an importance strategic resource for product innovation and that the technologies are closely associated with the firm's core competencies. The resulting new products appear to be relatively new to the firm and to the marketplace and considerably enhance customer-perceived value. Firms seem to favour relationships with universities that are in close proximity. Exclusivity rights do not appear to be a dominant feature in the firm-university relationships.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".