Determinants of Innovative Activity in Canadian Manufacturing Firms: The Role of Intellectual Property Rights
Bibliographic record
Abstract
This paper examines how several factors contribute to innovative activity in the Canadian manufacturing sector. First, it investigates the extent to which intellectual property right protection stimulates innovation. Second, it examines the contribution that R&D makes to innovation. Third, it considers the importance of various competencies in the area of marketing, human resource, technology and production to the innovation process. Fourth, it examines the extent to which a larger firm size and less competition serve to stimulate competition-the so-called Schumpeterian hypothesis. Fifth, the effect of the nationality of a firm on innovation is also investigated. Finally, the paper examines the effect of an industry's environment on a firm's ability to innovate. Several findings are of note. First, the relationship between innovation and patent use is found to be much stronger going from innovation to patent use than from patent use to innovation. Firms that innovate take out patents; but firms and industries that make more intensive use of patents do not tend to produce more innovations. Second, while R&D is important, developing capabilities in other areas, such as technological competency and marketing, is also important. Third, size effects are significant. The largest firms tend to be more innovative. As for competition, intermediate levels of competition are the most conducive to innovation. Fourth, foreign-controlled firms are not significantly more likely to innovate than domestic-controlled firms once differences in competencies have been taken into account. Fifth, the scientific infrastructure provided by university research is a significant determinant of innovation.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".