Competition and Innovation in Markets for Technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We examine the impact of product market competition on innovation in markets for technology. An innovator makes an investment in quality-improving innovation that can be licensed to one (targeted licensing) or all (market-wide licensing) product market competitors. Our model points to a U-shaped relationship between competition in licensee product markets and innovation in the market for technology: at low levels of competition, market-wide licensing is optimal, and competition reduces innovation, whereas at high levels of competition, targeted licensing is optimal and competition increases innovation. Our empirical analysis using a large panel of U.S. data provides clear support for these predictions linking competition, innovation, and licensing. This paper was accepted by Joshua Gans, business strategy. Funding: J.-E. (de) Bettignies gratefully acknowledges financial support by the Social Sciences and Humanities Research Council of Canada [Grant 435-2013-1863]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4574 .
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it