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- Title
Estimating the Licensing Probabilities in the Academic Context: An Empirical Analysis.
- Authors
Leite, Rafael Ângelo Santos; Reis, Igor Bezerra; Walter, Cicero Eduardo; de Aragão, Iracema Machado; Au-Yong-Oliveira, Manuel; Fortes, Paulo Jordão
- Abstract
Licensing technologies are one of the main ways to produce and bring academic research to society. Despite previous studies' dedicated efforts to identify licensing probabilities, the question of how the expertise and prestige that a university has in a given technological field influences the licensing probabilities is still little addressed. This article aims to identify information in patent documents to estimate the probabilities of licensing technologies produced at the university. For that, we performed a data mining of licensed and unlicensed patents from an important Brazilian University (n = 1,578). We estimated the licensing probabilities using the Logistic Regression technique, based on the Maximum Likelihood Estimation. The results suggest that the variables of know-how in the main field and Technological strength in the main field are the most important/influential variables in estimating the probabilities of licensing a given patent. The main conclusion obtained from the results is that: universities, to obtain more licenses, must increase their know-how (expertise) in some technological fields, maintaining a reasonable level between specialization and diversification. Additionally, the higher the citations received (prestige/recognition) by a university in a given technological field, the greater the probability of patent licensing in that technological field. In terms of practical contributions, this study suggests that: investments in specific technological fields generate more competitive advantages for the university and, thus, more technological successes.
- Subjects
MAXIMUM likelihood statistics; PROBABILITY theory; PATENT licenses; ESTIMATES; DATA mining; LOGISTIC regression analysis
- Publication
International Journal of Innovation & Technology Management, 2023, Vol 20, Issue 8, p1
- ISSN
0219-8770
- Publication type
Article
- DOI
10.1142/S0219877023500542