Assessing the commercial potential of high-tech production business targets in risk-generated innovation economies using fuzzy set methods
Abstract
The purpose of the article is to develop a methodology that will make it possible to use attitudes and expressions of professional language to the maximum extent when selecting business goals of high-tech entrepreneurship during the development, production and sale of the latest products and to reduce the sensitivity of the assessment to small deviations of the factors and increase its reliability. It is proven that the proposed method allows to use the attitude and expression of professional language to the maximum when choosing a business goal. At the same time, the sensitivity of the assessment to small deviations of the factors decreases, and the reliability increases. It has been investigated and established that when using fuzzy methods in the decision-making process in high-tech production, unlike the existing ones, there is an opportunity to actively use fuzzy estimates and different points of view of people who carry out planning or decision-making, as well as fuzzy information expressed in words.
Keyword : commercial potential, business goal, high technologies, fuzzy sets, rapid assessment, risk, innovative economy
How to Cite
Ilyash, O., Zakharchenko, V., Yermak, S., Frolova, L., Mazhara, G., & Smoliar, L. (2025). Assessing the commercial potential of high-tech production business targets in risk-generated innovation economies using fuzzy set methods. Business: Theory and Practice, 26(1), 1–16. https://doi.org/10.3846/btp.2025.20922
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Kiseliova, E. M., Pritomanova, O. M., & Zhuravel, S. V. (2016). Assessment of the investment attractiveness of startups based on neuro-fuzzy technologies. Problems of Management and Informatics, 5, 123–143.
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Kozlovskyi, S., Mazur, H., Vdovenko, N., Shepel, T., & Kozlovskyi, V. (2018). Modeling and forecasting the level of state stimulation of agricultural production in Ukraine based on the theory of fuzzy logic. Montenegrin Journal of Economics, 14(3), 37–53. https://doi.org/10.14254/1800-5845/2018.14-3.3
Kozlovskyi, S., Nikolenko, L., Peresada, O., Pokhyliuk, O., Yatchuk, O., Bolgarova, N., & Kulhanik, O. (2020). Estimation level of public welfare on the basis of methods of intellectual analysis. Global Journal of Environmental Science and Management, 6(3), 355–372.
Liao, M.-S., Liang, G.-S., & Chen, C.-Y. (2013). Fuzzy grey relation method for multiple criteria decision-making problems. Quality & Quantity, 47(6), 3065–3077. https://doi.org/10.1007/s11135-012-9704-5
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Makedon, V., Myachin, V., Plakhotnik, O., Fisunenko, N., & Mykhailenko, O. (2024). Construction of a model for evaluating the efficiency of technology transfer process based on a fuzzy logic approach. Eastern-European Journal of Enterprise Technologies, 2(13(128), 47–57. https://doi.org/10.15587/1729-4061.2024.300796
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Olawore, A. S., Wong, K. Y., Ma’aram, A., & Sutopo, W. (2023). Prioritization of technology commercialization success factors using fuzzy best worst method. Journal of Open Innovation: Technology, Market, and Complexity, 9(3), Article 100096. https://doi.org/10.1016/j.joitmc.2023.100096
Ozdemir, Y. S. (2022). A spherical fuzzy multi-criteria decision-making model for Industry 4.0 performance measurement. Axioms, 11(7), Article 325. https://doi.org/10.3390/axioms11070325
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Sarfaraz, A. H., Yazdi, A. K., Hanne, T., & Hosseini, R. S. (2023). Decision support for technology transfer using fuzzy quality function deployment and a fuzzy inference system. Journal of Intelligent & Fuzzy Systems, 44(5), 7995–8014. https://doi.org/10.3233/JIFS-222232
Sotnyk, V., Kupchyn, A., Trynchuk, V., Glonti, V., & Belinskaja, L. (2022). Fuzzy logic decision-making model for technology foresight. Economic Studies (Ikonomicheski Izsledvania), 31(1), 139–159.
Tsihanok, V., & Roik, P. (2018). A method of determining and increasing the consistency of expert assessments for supporting group decision-making. System Research and Information Technologies, 3, 110–121. https://doi.org/10.20535/SRIT.2308-8893.2018.3.10
Yager, R. R. (2009). On generalized Bonferroni mean operators for multi-criteria aggregation. International Journal of Approximate Reasoning, 50(8), 1279–1286. https://doi.org/10.1016/j.ijar.2009.06.004
Yager, R. R. (2013). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems, 22(4), 958–965. https://doi.org/10.1109/TFUZZ.2013.2278989
Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems, 25(5), 1222–1230. https://doi.org/10.1109/TFUZZ.2016.2604005
Yermak, S., & Bugaenko, O. (2016). Implementation of innovative development strategy of a dairy enterprise based on a smart farm concept. Actual Problems of Economics, 12(186), 138–146.
Yermak, S. (2017). Problems of innovative activity development at food industry enterprises of Ukraine. Journal of Hygienic Engineering and Design, 21, 96–102.
Xiao, L., Huang, G., Pedrycz, W., Pamucar, D., Martínez L., & Zhang, G. (2022). A q-rung orthopair fuzzy decision-making model with new score function and best-worst method for manufacturer selection. Information Sciences, 608, 153–177. https://doi.org/10.1016/j.ins.2022.06.061
Zakharchenko, V. (2013). Dynamics of investment and innovation processes in an unstable environment. Oldi-plus.
Zakharchenko, V., & Yermak, S. (2021). Methodological bases of creation of organizational and technological systems in high-tech production. Economics: The realities of time. Scientific Journal, 3(55), 49–60. https://doi.org/10.15276/ETR.03.2021.6
Zhang, Z. (2024). Application of fuzzy decision support systems in risk assessment of Southeast Asian labor market. International Journal of Computational Intelligence Systems, 17, Article 153. https://doi.org/10.1007/s44196-024-00556-y
Zemlickienė, V., Bublienė, R., & Jakubavičius, A. (2018). A model for assessing the commercial potential of high technologies. Oeconomia Copernicana, 9(1), 29–54. https://doi.org/10.24136/oc.2018.002
Aleixo, G. G., & Tenera, A. B. (2009). New product development process on high-tech innovation life cycle. World Academy of Science, Engineering and Technology, 3(10).
Bandarian, R. (2008). Measuring commercial potential of a new technology at the early stage of development with fuzzy logic. Journal of Technology Management & Innovation, 2(4), 73–85.
Bilenko, D., Kozlovskyi, S., Ivanyuta, N., Baidala, V., Lavrov, R., & Kozlovskyi, V. (2022). Efficiency assessment of tax measures in the European countries against the effects of COVID-19. Problemy Ekorozwoju – Problems of Sustainable Development, 17(1), 16–22. https://doi.org/10.35784/pe.2022.1.02
Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40. https://doi.org/10.3316/QRJ0902027
Dziallas, M., & Blind, K. (2019). Innovation indicators throughout the innovation process: An extensive literature analysis, Technovation, 80–81, 3–29. https://doi.org/10.1016/j.technovation.2018.05.005
Eseoglu, G., Yapsakli, K., Tozan, H., & Vayvay, O. (2022). A novel fuzzy framework for technology selection of sustainable wastewater treatment plants based on TODIM methodology in developing urban areas. Scientific Report, 12, Article 8800. https://doi.org/10.1038/s41598-022-12643-1
Evers, N., Cunningham, J. A., & Hoholm, T. (2016). International entrepreneurship in universities: Context, emergence and actors. Journal of International Entrepreneurship, 14(3), 285–295. https://doi.org/10.1007/s10843-016-0188-6
Filyppova, S., Okulich-Kazarin, V., Kibik, O., Shamborovskyi, G., & Cherkasova, S. (2019). Influence of the market of business intellectual services on the innovation safety of EU countries. Journal of Security and Sustainability, 9(1), 347–360. https://doi.org/10.9770/jssi.2019.9.1(26)
Filyppova, S., Bovnegra, L., Chukurna, O., Vudvud, O., & Dobrovolskyi, V. (2021). Assessment of the impact of automatic parking on emissions of harmful substances in the Green Logistic System. In New technologies, development and applications (pp. 815–822). Springer. https://doi.org/10.1007/978-3-030-75275-0_89
Fishburn, P. (1999). Preference structures and their numerical representations. Theoretical Computer Science, 217(2), 359–383. https://doi.org/10.1016/S0304-3975(98)00277-1
Fishburn, P. (1978). Utility theory for decision making. John Wiley & Sons, Inc. https://apps.dtic.mil/sti/pdfs/AD0708563.pdf
Frolova, L., Zhadko, K., Ilyash, O., Yermak, S., & Nosova, T. (2021). Model for opportunities assessment to increase the enterprise innovation activity. Business: Theory and Practice, 22(1), 1–11. https://doi.org/10.3846/btp.2021.13273
Gbadegeshin, S. A. (2017). Commercialization process of high technology: A study of Finnish university spin-off. Academy of Entrepreneurship Journal, 23(2), 1–22.
Government Portal. (2022). National economic strategy of Ukraine for the period up to 2030. https://www.kmu.gov.ua/npas/pro-zatverdzhennya-nacionalnoyi-eko-a179
Hindle, K., & Yencken, J. (2004). Public research commercialisation, entrepreneurship and new technology based firms: An integrated model. Technovation, 24(10), 793–803. https://doi.org/10.1016/S0166-4972(03)00023-3
Hung, R. Y. Y., Chung, T. T., & Lien, B. Y. H. (2007). Organizational process alignment and dynamic capabilities in high-tech industry. Total Quality Management & Business Excellence, 18(9), 1023–1034. https://doi.org/10.1080/14783360701594154
Ilyash, О. (2015). Strategic priorities of Ukraine’s social security concept development and implementation. Economic Annals XXI, 152(7–8((1)), 20–23.
Ilyash, O., Lupak R., Vasyltsiv,T., Trofymenko, O., & Dzhadan, I. (2021). Modelling of the dependencies of industrial development on marketing efficiency, innovation and technological activity indicators. Ekonomika, 100(1), 94–116. https://doi.org/10.15388/Ekon.2021.1.6
Kiseliova, E. M., Pritomanova, O. M., & Zhuravel, S. V. (2016). Assessment of the investment attractiveness of startups based on neuro-fuzzy technologies. Problems of Management and Informatics, 5, 123–143.
Kofman, A. (1983). Introduction to the theory of fuzzy sets. Radio and communication.
Kovtunenko, K., Kovtunenko, Y., Shatskova, L., & Yatsenko, M. (2019). Management mechanism as an independent element of the food industry enterprise innovative activity expenditures management system. Journal of Hygienic Engineering and Design, 28, 73–80.
Kozlovskyi, S., Mazur, H., Vdovenko, N., Shepel, T., & Kozlovskyi, V. (2018). Modeling and forecasting the level of state stimulation of agricultural production in Ukraine based on the theory of fuzzy logic. Montenegrin Journal of Economics, 14(3), 37–53. https://doi.org/10.14254/1800-5845/2018.14-3.3
Kozlovskyi, S., Nikolenko, L., Peresada, O., Pokhyliuk, O., Yatchuk, O., Bolgarova, N., & Kulhanik, O. (2020). Estimation level of public welfare on the basis of methods of intellectual analysis. Global Journal of Environmental Science and Management, 6(3), 355–372.
Liao, M.-S., Liang, G.-S., & Chen, C.-Y. (2013). Fuzzy grey relation method for multiple criteria decision-making problems. Quality & Quantity, 47(6), 3065–3077. https://doi.org/10.1007/s11135-012-9704-5
Lytvynenko, S., Tregubov, O., Prykhno, Y., Yakymova, N., Panchenko, I., & Popova, Y. (2022). Transformation of the paradigm of entrepreneurial activity innovative development in the pandemic conditions. International Journal of Agricultural Extension, 10(Special issue 1), 147–156. https://doi.org/10.33687/ijae.010.00.3853
Makedon, V., Myachin, V., Plakhotnik, O., Fisunenko, N., & Mykhailenko, O. (2024). Construction of a model for evaluating the efficiency of technology transfer process based on a fuzzy logic approach. Eastern-European Journal of Enterprise Technologies, 2(13(128), 47–57. https://doi.org/10.15587/1729-4061.2024.300796
Mokiy, A., Ilyash, O., Pynda, Y., Pikh, M., & Tyurin, V. (2020). Dynamic characteristics of the interconnections urging the construction enterprises development and regions economic growth. TEM Journal, 9(4),1550–1561. https://doi.org/10.18421/TEM94-30
Nedosekin, A. (2003). Fuzzy financial management. Audit and financial analysis. http://www.mirkin.ru/_docs/book0308_033.pdf
Olawore, A. S., Wong, K. Y., Ma’aram, A., & Sutopo, W. (2023). Prioritization of technology commercialization success factors using fuzzy best worst method. Journal of Open Innovation: Technology, Market, and Complexity, 9(3), Article 100096. https://doi.org/10.1016/j.joitmc.2023.100096
Ozdemir, Y. S. (2022). A spherical fuzzy multi-criteria decision-making model for Industry 4.0 performance measurement. Axioms, 11(7), Article 325. https://doi.org/10.3390/axioms11070325
Ryzhov, A. P. (1998). Elements of the theory of fuzzy sets and measurement of fuzziness. Dialogue-MGU.
Sarfaraz, A. H., Yazdi, A. K., Hanne, T., & Hosseini, R. S. (2023). Decision support for technology transfer using fuzzy quality function deployment and a fuzzy inference system. Journal of Intelligent & Fuzzy Systems, 44(5), 7995–8014. https://doi.org/10.3233/JIFS-222232
Sotnyk, V., Kupchyn, A., Trynchuk, V., Glonti, V., & Belinskaja, L. (2022). Fuzzy logic decision-making model for technology foresight. Economic Studies (Ikonomicheski Izsledvania), 31(1), 139–159.
Tsihanok, V., & Roik, P. (2018). A method of determining and increasing the consistency of expert assessments for supporting group decision-making. System Research and Information Technologies, 3, 110–121. https://doi.org/10.20535/SRIT.2308-8893.2018.3.10
Yager, R. R. (2009). On generalized Bonferroni mean operators for multi-criteria aggregation. International Journal of Approximate Reasoning, 50(8), 1279–1286. https://doi.org/10.1016/j.ijar.2009.06.004
Yager, R. R. (2013). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems, 22(4), 958–965. https://doi.org/10.1109/TFUZZ.2013.2278989
Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems, 25(5), 1222–1230. https://doi.org/10.1109/TFUZZ.2016.2604005
Yermak, S., & Bugaenko, O. (2016). Implementation of innovative development strategy of a dairy enterprise based on a smart farm concept. Actual Problems of Economics, 12(186), 138–146.
Yermak, S. (2017). Problems of innovative activity development at food industry enterprises of Ukraine. Journal of Hygienic Engineering and Design, 21, 96–102.
Xiao, L., Huang, G., Pedrycz, W., Pamucar, D., Martínez L., & Zhang, G. (2022). A q-rung orthopair fuzzy decision-making model with new score function and best-worst method for manufacturer selection. Information Sciences, 608, 153–177. https://doi.org/10.1016/j.ins.2022.06.061
Zakharchenko, V. (2013). Dynamics of investment and innovation processes in an unstable environment. Oldi-plus.
Zakharchenko, V., & Yermak, S. (2021). Methodological bases of creation of organizational and technological systems in high-tech production. Economics: The realities of time. Scientific Journal, 3(55), 49–60. https://doi.org/10.15276/ETR.03.2021.6
Zhang, Z. (2024). Application of fuzzy decision support systems in risk assessment of Southeast Asian labor market. International Journal of Computational Intelligence Systems, 17, Article 153. https://doi.org/10.1007/s44196-024-00556-y
Zemlickienė, V., Bublienė, R., & Jakubavičius, A. (2018). A model for assessing the commercial potential of high technologies. Oeconomia Copernicana, 9(1), 29–54. https://doi.org/10.24136/oc.2018.002