Anastasiia Samoilikova, ORCID: https://orcid.org/0000-0001-8639-5282
PhD in Economics, Senior Lecturer, Department of Financial Technologies and Entrepreneurship, Sumy State University, Ukraine
Artem Artyukhov, ORCID: https://orcid.org/0000-0003-1112-6891
Ph.D. in Technical Sciences, Senior Researcher, Department of Marketing, University of Economics in Bratislava, Slovakia
The study actualizes the issue of cooperation between business and science on the way to the commercialization of innovations in modern conditions. A hypothesis is put forward regarding the relationship between the level of cooperation between industry and science (based on the University-Industry Research Collaboration indicator within the Global Innovation Index) and the income from intellectual property. Therefore, the article aims to confirm the existence and establish a cause-and-effect relationship between the level of cooperation between business and science and the amount of income from intellectual property. A bibliometric analysis is carried out at the first stage to confirm the hypothesis, and the main directions of interdisciplinary research related to this issue are highlighted. In the second stage, the research information base is formed based on the statistical data of the World Intellectual Property Organization for a sample of 10 countries – leaders according to the Global Innovation Index of 2022 for the last 10 years (2013-2022). In the third stage, a correlation analysis is carried out to confirm a relationship’s existence and determine its statistical significance, nature and strength. At the fourth stage, a vector autoregression is constructed, based on the results of which Granger testing for cause-and-effect relationships is performed to determine the influence direction between the studied indicators. It is established that the level of cooperation between business and science is the cause and affects the amount of income from intellectual property in 6 of the 8 countries of the sample, in which the cause-and-effect relationship between the studied indicators was confirmed and established; the amount of income from intellectual property is the cause and affects the level of cooperation between business and science in 5 of the 8 countries of the sample, in which the cause-and-effect relationship between the studied indicators was confirmed. At the same time, in 3 out of 8 countries of the sample, in which the cause-and-effect relationship between the studied indicators was confirmed, a two-way influence was found. Accordingly, it is substantiated that the level of cooperation between business and science directly and positively affects the income from intellectual property. Therefore, the strengthening of collaboration between industry and science will contribute to the increase in the amount of income from intellectual property. In turn, the revenue volume from the intellectual property will also contribute to improving and developing cooperation between business and science. The obtained results can be helpful for scientists in further research in related scientific areas and for representatives of the business community, government officials and other persons interested in this issue.
Keywords: business-science coopetition, causal relationships, Global Innovation Index, intellectual property income, research and development, research cooperation, university-industry research collaboration.
JEL Classification: M21, O32, O34.
Cite as: Samoilikova, A., Artyukhov, A. (2023). Analysis of the relationship between “business-science” coopetition and intellectual property receipts. SocioEconomic Challenges, 7(1), 149-157. https://doi.org/10.21272/sec.7(1).149-157.2023
This work is licensed under a Creative Commons Attribution 4.0 International License
- Adams, J. D., Chiang, E. P., and Starkey, K. (2001). Industry-University Cooperative Research Centers. Journal of Technology Transfer, 26(1/2), 73-86. [CrossRef].
- Ankrah, S. N. & Al-Tabbaa, O. (2015). Universities-Industry Collaboration: A Systematic Review. SSRN Electronic Journal. [CrossRef].
- Awasthy, R., Flint, S., Sankarnarayana, R. and Jones, R.L. (2020). A framework to improve university–industry collaboration. Journal of Industry – University Collaboration, 2(1), 49-62. [CrossRef].
- Ćudić, B., Alešnik, P. & Hazemali, D. (2022). Factors impacting university–industry collaboration in European countries. J Innov Entrep, 11, 33. [CrossRef].
- Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: journal of the Econometric Society, 37(3), 424–438. [CrossRef].
- Hall, B. H. (2004). University-Industry Research Partnerships in the United States. Kansai Conference Paper. URL: [Link].
- Hall, B. H., Link, A. N., and Scott, J. T. (2003). Universities as Research Partners. Review of Economics and Statistics, 85, 485-491. [CrossRef].
- He, V.F., Krogh, G., Sirén, S., and Gersdorf, T. (2021). Asymmetries between partners and the success of university-industry research collaborations. Research Policy, 50(10): 104356. [CrossRef].
- Ivascu, L., Cirjaliu, B. and Draghici, A. (2016). Business Model for the University-industry Collaboration in Open Innovation. Procedia Economics and Finance, 39, 674-678. [CrossRef].
- Kneller, R., Mongeon, M., Cope, J., Garner, C., and Ternouth, P. (2014). Industry-University Collaborations in Canada, Japan, the UK and USA – With Emphasis on Publication Freedom and Managing the Intellectual Property Lock-Up Problem. PLOS ONE, 9(3): e90302. [CrossRef].
- Koibichuk, V., Samoilikova, A., & Herasymenko, V. (2022). Education and Business in Conditions of Coopetition: Bibliometrics. Business Ethics and Leadership, 6(4), 49-60. [CrossRef].
- Lace, N. & Rumbinaite, G. (2016). Successful university – industry collaboration as a factor for implementation of Smart Specialization Strategy: evidence from Latvia and Lithuania. Proceedings of the 20th World Multi-Conference on Systemics, Cybernetics and Informatics (WMSCI 2016). URL: [Link].
- Lee, Y. S. (2000). The Sustainability of University-Industry Research Collaboration. Journal of Technology Transfer, 25(2), 111-133. [CrossRef].
- Lutchen, K. R. (2018). Why Companies and Universities Should Forge Long-Term Collaborations? Harvard Business Review. URL: [Link].
- Morisson, A. & Pattinson, M. (2020). University-Industry Collaboration. Lille: Interreg Europe Policy Learning Platform. URL: [Link].
- Mowery, D. C. (1999). The Evolving Structure of University-Industry Collaboration in the United States: Three Cases. Report of a Workshop “Research Teams and Partnerships: Trends in the Chemical Sciences”. URL: [Link].
- Mowery, D.C., Nelson, R.R., Sampat, B. and Ziedonis, A.A. (1999). The Effects of the Bayh-Dole Act on U.S. University Research and Technology Transfer: An Analysis of Data from Columbia University, University of California and Stanford University. Forthcoming in Industrializing Knowledge, L. Branscomb and R. Florida, eds. MIT Press, Cambridge, Mass. [Link].
- O’Dwyer, M., Filieri, R. & O’Malley, L. (2022). Establishing successful university–industry collaborations: barriers and enablers deconstructed. J Technol Transf. [CrossRef].
- OECD (2019). University-Industry Collaboration New Evidence and Policy Options. URL: [Link].
- Okamuro, H & Nishimura, J. (2013). Impact of University Intellectual Property Policy on the Performance of University-Industry Research Collaboration. The Journal of Technology Transfer, 38(3), 273–301. [CrossRef].
- Pantanowitz, L., Bui, M.M, Chauhan, C., ElGabry, E., Hassell, L., Li, Z., Parwani, A.V., Salama, M.E., Sebastian, M.M., Tulman, D., Vepa, S., and Becich, M.J. (2022). Rules of engagement: Promoting academic-industry partnership in the era of digital pathology and artificial intelligence. Acad Pathol, 9(1):100026. [CrossRef].
- Pearson, K. (1896). VII Mathematical contributions to the theory of evolution-III. Regression, heredity, and panmixia. Philosophical Transactions of the Royal Society of London. Series A, containing papers of a mathematical or physical character, 187, 253-318. [Link].
- Samoilikova, A., Zhylinska, O., Pal, Z., & Kuttor, D. (2022). «Business-Education-Science» Coopetition and Innovation Transfer for Sustainable Development. Marketing and Management of Innovations, 2, 220-230. [CrossRef].
- Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3/4), 591-611. [CrossRef].
- Shvindina, H. (2017). Innovations of strategic management development: from competition to coopetition. Marketing and Management of Innovations, 1, 180-192. [CrossRef].
- Spearman, C. (1987). The proof and measurement of association between two things. The American Journal of Psychology, 100 (3/4), 441-471. [CrossRef].
- Stata (n.d.). Pairwise Granger causality tests after var or svar. Manuals. URL: [Link].
- THE (2020). University Industry Collaboration the Vital Role of Tech Companies’ Support for Higher Education Research. URL: [Link].
- WIPO (2020). The Global Innovation Index (GII) Conceptual Framework. URL: [Link].
- WIPO (2022). Global Innovation Index 2022. What is the future of innovation driven growth? 15th Edition, Geneva, Switzerland. 89 p. [Link].
- WIPO (n.d.a). Intellectual Property Receipts, % Total Trade. The Interactive Database of The GII Indicators. URL: [Link].
- WIPO (n.d.b). University/Industry Research Collaboration. The Interactive Database of The GII Indicators. URL: [Link].