Technology Licensing: There are two sides to every agreement
Bibliographic record
Abstract
Why do some patents get licensed while many others do not? The traditional answers to this question focus on how the characteristics of one of the parties (i.e., the licensor or the licensee) impact licensing, and thus do not capture the dyadic factors that drive both parties to make a deal. In this study we examine how characteristics for the licensor, the licensee, and the technology that is being licensed all combine to influence why one patent would be licenced over another. In doing so, we develop and test a model of transorptive capacity to explain how these characteristics interact to reduce three transaction cost activities (identification, evaluation and knowledge transfer) which in turn increase the likelihood a patent will be licensed. Empirical evidence from the biopharmaceutical industry confirms that identification of a licensing partner is facilitated when licensees with a strong monitoring ability choose patents owned by licensors that have the ability to get its patents noticed. The ease of partner evaluation afforded from extensive licensing history from both parties can contribute positively to a patent being licensed, as does the ability of a licensor to transfer technical knowledge to a licensee with the ability to absorb it. From our results we conclude that there are two sides to a licensing agreement and that better matching of the characteristics of each party can alleviate the overwhelming number of unlicensed patents.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".