Resumo

Covid-19 has put public health services to the test. Economic systems will soon be put to the test by Covid-19. In order to recover from the effects of the coronavirus, innovation will be essential way (Chesbrough, 2020). Firms must continuously adapt and evolve to thrive in a dynamic, global environment. Despite the continuous change, firms drive markets by utilizing and strategically managing knowledge. Universities are crucial parts of the scientific and technology ecosystem because they provide an endless supply of data and technical capabilities (Berbegal-Mirabent et al., 2015).

Universities lack clear data and methods for tracking and evaluating overall entrepreneurial success (Etzkowitz et al., 2018). Existing technology transfer output metrics are widely considered to be not only insufficiently defined, but also inaccurate (Alessandrini et al., 2013). This article is part of a doctoral research in the area of innovation and technology management on the socioeconomic impacts of university-industry collaborations. This work presents a multivariate statistical analysis of the socioeconomic impacts of university-industry collaborations from the firm’s perspective.

We used the Lima et al. (2021) model based in a systematic literature review of the socioeconomic impacts of university-industry collaborations that impacts were categorized into (1) economic, (2) social and (3) financial. The dimensions were divided into (1) economic: infrastructure, production and processes, and scientific development; (2) social: jobs, skills, and qualification; and (3) financial: purchases, taxes, investments, and income generation. According to the research's focus, a model was cut to evaluate the firm perspective.

Multivariate analysis refers to all statistical techniques that analyzes multiple measurements at the same time. Some multivariate techniques are designed specifically to address multivariate aspects such as factor analysis. Canonical analysis aims to correlate simultaneously numerous metric dependent variables and several metric independent variables (Hair et al., 2009). The survey was sent to the “Ranking 1500 – Empresas + Estadão” of the 1,500 largest companies in Brazil. We collected 210 complete and valid responses from companies that have formalized collaborations with universities.

Factor analysis identified the sets of data into 4 factors in detriment to the initial 3 factors. The 4 factors were categorized into financial benefits; social and community; technological innovation and management of external resources. The professional workforce qualification is correlated with the creation of new high-tech workstations. New technologies commercialization and development of new products are correlated with increased sales. Release of new products is correlated with increased: sales, exports, revenue, profit, commercial and shareholder value.

Although university-industry collaborations are considered as providing the ability to increase socioeconomic growth, there is a literature gap in the field of comprehensive metrics to measure these collaborations' socioeconomic impacts. This work achieved evaluate the socioeconomic impacts of large Brazilian firms that carry out formal collaborations with universities. The analyzes allowed the construction of a model of socioeconomic impacts from the perspective of large Brazilian companies.

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2009). Multivariate Data Analysis: A Global Perspective. 7th ed. Upper Saddle River: Prentice Hall, Print. Lima, J. C. F., Torkomian, A. L. V., Pereira, S. C. F., Oprime, P. C., & Hashiba, L. H. (2021). Socioeconomic Impacts of University–Industry Collaborations–A Systematic Review and Conceptual Model. Journal of Open Innovation: Technology, Market, and Complexity, 7(2), 137. DeVellis, R. F. (2017). Scale development: Theory and applications (Vol. 4). California: Sage publications.