Patents and Articles Related to Cooperation in Universities, Using Poisson Regression Models


  • Suzana Leitão Russo Universidade Federal de Sergipe
  • Daiane Costa Gumarães Universidade Federal de Sergipe
  • Cleide Mara Barbosa da Cruz Universidade Federal de Sergipe
  • Cleo Clayton Santos Silva Universidade Federal de Sergipe



University-industry, Cooperation, Regression Poisson Models


University-industry cooperation is the formation of partnership relationships that exist in Science and Technology Institutions with industries, that is, it is the cooperation that exists between universities and industries. In this sense, there are several ways of forming relations between universities and industry, and for this to happen, it all boils down to cooperation, since both agents need to agree with certain achievements, there must be communication between them. Despite all the existing benefits, even if there is reciprocity between these agents that seek a common denominator, there are still divergences that remain as a difficulty factor for this cooperation, since there are several differences found in the academic and industrial environment. Thus, we sought to analyze how are the production of articles aimed at university-industry cooperation, as well as patents related to this subject, through specific bases. A forecast analysis was also carried out using the Poisson Regression models, where it was found, in the data of patents and articles, a superdispersion, therefore, it was necessary to adjust the deviation G ^ 2 or as known deviance, and with the adjustment of overdispersion the models were adequate and confirmed in the forecast made. We thank Capes and CNPq for their support and financial support.


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DOI: 10.31686/ijier.vol8.iss5.2309

How to Cite

Russo, S. L., Costa Gumarães, D. ., da Cruz, C. M. B., & Santos Silva, C. C. (2020). Patents and Articles Related to Cooperation in Universities, Using Poisson Regression Models. International Journal for Innovation Education and Research, 8(5), 43–59.
Received 2020-03-23
Accepted 2020-05-02
Published 2020-05-01

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