R&D process management in the context of digitalization
Keywords:
Management of innovation development, R&D, Stage-Gate model, digital transformation, agricultural machinery company, Agriculture 4.0, intellectual capital, disruptive innovations, econometric modelingSynopsis
The article is devoted to a comprehensive study of the theoretical, methodological, and applied aspects of process management in the context of the Fourth Industrial Revolution. The focus is on the transformation of the new product development system, moving from purely mechanical improvements toward the creation of intelligent robotic systems based on the concept of "disruptive innovations".
The first section systematizes the conceptual foundations of innovation cycle management and defines the role of R&D within the Agriculture 4.0 value chain. The study substantiates a multi-factor functional dependence of management effectiveness on intellectual capital, strategic positioning, time parameters, and the level of uncertainty. The process architecture is based on the implementation of the Stage-Gate model, which serves as a risk filter and a decision-making tool at the early stages of the innovation lifecycle.
The second section presents the results of economic and mathematical modeling for the development of an intelligent "Smart" type coulter. The study demonstrates the critical role of the intellectual component (software and AI algorithms), which accounts for approximately 50% of the total development budget. The investment project parameters are calculated for an agricultural machinery enterprise (the full payback period for the developer is 1.66 years).
An analysis of the implementation efficiency from the perspective of the end-user (small agricultural enterprise) was conducted, establishing that the payback period of the implemented innovation is 2.4 years, which renders digitalization a justified investment in the medium term. The research confirms that within the Agriculture 4.0 framework, strategic advantage is attained through the capitalization of intellectual capital and the rigorous filtering of innovation projects during the early stages of development.
The findings of this study can be utilized by machinery manufacturing enterprises to justify R&D budgets, by scholars to examine the patterns of digital transformation in the agricultural sector, and by investors for the assessment of AgTech startups. The article is addressed to scientists, educators, students of economic and engineering disciplines, agribusiness managers, and innovation management specialists.
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Wang, X. (2024). Too much incentive to innovate? CEO stock option exercise and myopic R&D management. Journal of Product Innovation Management, 41 (6), 1141–1164. https://doi.org/10.1111/jpim.12731
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Wang, R., Usman, M., Radulescu, M., Cifuentes-Faura, J., Balsalobre-Lorente, D. (2023). Achieving ecological sustainability through technological innovations, financial development, foreign direct investment, and energy consumption in developing European countries. Gondwana Research, 119, 138–152. https://doi.org/10.1016/j.gr.2023.02.023
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Zhai, Z., Martínez, J. F., Beltran, V., Martínez, N. L. (2020). Decision support systems for agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture, 170, 105256. https://doi.org/10.1016/j.compag.2020.105256
ARK Group. Available at: https://arkgroupp.com.ua
Main Science and Technology Indicators (MSTI) 2025 (2025). OECD. Available at: https://www.oecd.org/en/data/datasets/main-science-and-technology-indicators.html
R&D expenditure statistics (2024–2026) (2024). Eurostat. Available at: https://ec.europa.eu/eurostat/rd-expenditure
Derzhavnyi biudzhet-2026. Kabinetu Ministriv Ukrainy. Available at: https://kmu.gov.ua/news/derzhbiudzhet-2026-nauka
Farzaneh, M., Wilden, R., Afshari, L., Mehralian, G. (2022). Dynamic capabilities and innovation ambidexterity: The roles of intellectual capital and innovation orientation. Journal of Business Research, 148, 47–59. https://doi.org/10.1016/j.jbusres.2022.04.030
Wang, X. (2024). Too much incentive to innovate? CEO stock option exercise and myopic R&D management. Journal of Product Innovation Management, 41 (6), 1141–1164. https://doi.org/10.1111/jpim.12731
Audretsch, D. B., Belitski, M. (2022). Evaluating internal and external knowledge sources in firm innovation and productivity: an industry perspective. R&D Management, 53 (1), 168–192. https://doi.org/10.1111/radm.12556
Christensen, C. M., McDonald, R., Altman, E. J., Palmer, J. E. (2018). Disruptive Innovation: An Intellectual History and Directions for Future Research. Journal of Management Studies, 55 (7), 1043–1078. https://doi.org/10.1111/joms.12349
Cooper, R. G. (2022). The 5-th Generation Stage-Gate Idea-to-Launch Process. IEEE Engineering Management Review, 50 (4), 43–55. https://doi.org/10.1109/emr.2022.3222937
Chesbrough, H. W. (2003). Open innovation: The new imperative for creating and profiting from technology. Harvard Business School Press.
Petrova, I. L., Shpylova, T. I., Sysolina, N. P.; Petrova, I. L. (Ed.) (2010). Innovatsiina diialnist: stymuly ta pereshkody. Kyiv: Dorado, 296. Available at: https://library.krok.edu.ua/media/library/category/monografiji/petrova_0002.pdf
Li, S., Gao, L., Han, C., Gupta, B., Alhalabi, W., Almakdi, S. (2023). Exploring the effect of digital transformation on Firms’ innovation performance. Journal of Innovation & Knowledge, 8 (1), 100317. https://doi.org/10.1016/j.jik.2023.100317
Dąbrowska, J., Almpanopoulou, A., Brem, A., Chesbrough, H., Cucino, V., Di Minin, A. et al. (2022). Digital transformation, for better or worse: a critical multi‐level research agenda. R&D Management, 52 (5), 930–954. https://doi.org/10.1111/radm.12531
Sun, G., Fang, J., Li, J., Wang, X. (2024). Research on the impact of the integration of digital economy and real economy on enterprise green innovation. Technological Forecasting and Social Change, 200, 123097. https://doi.org/10.1016/j.techfore.2023.123097
Zhuo, C., Chen, J. (2023). Can digital transformation overcome the enterprise innovation dilemma: Effect, mechanism and effective boundary. Technological Forecasting and Social Change, 190, 122378. https://doi.org/10.1016/j.techfore.2023.122378
El Samra, A., James, A., Malik, K. (2023). Disrupt through digital: a study on the challenges faced when digitalizing R&D. R&D Management, 54 (4), 713–723. https://doi.org/10.1111/radm.12637
Kulichyova, A., Kazantsev, N., White, L., Islam, N. (2025). Digital transformation in large established organisations: Four restructuring dilemmas based on dynamic capabilities. International Journal of Management Reviews, 27 (3), 420–450. https://doi.org/10.1111/ijmr.12395
Omol, E. J. (2023). Organizational digital transformation: from evolution to future trends. Digital Transformation and Society, 3 (3), 240–256. https://doi.org/10.1108/dts-08-2023-0061
Mariani, M., Dwivedi, Y. K. (2024). Generative artificial intelligence in innovation management: A preview of future research developments. Journal of Business Research, 175, 114542. https://doi.org/10.1016/j.jbusres.2024.114542
Hoseinzadeh, S., Astiaso Garcia, D. (2024). Ai-driven innovations in greenhouse agriculture: Reanalysis of sustainability and energy efficiency impacts. Energy Conversion and Management: X, 24, 100701. https://doi.org/10.1016/j.ecmx.2024.100701
Lee, C.-C., Qin, S., Li, Y. (2022). Does industrial robot application promote green technology innovation in the manufacturing industry? Technological Forecasting and Social Change, 183, 121893. https://doi.org/10.1016/j.techfore.2022.121893
Ahmad, N., Youjin, L., Žiković, S., Belyaeva, Z. (2023). The effects of technological innovation on sustainable development and environmental degradation: Evidence from China. Technology in Society, 72, 102184. https://doi.org/10.1016/j.techsoc.2022.102184
Wang, R., Usman, M., Radulescu, M., Cifuentes-Faura, J., Balsalobre-Lorente, D. (2023). Achieving ecological sustainability through technological innovations, financial development, foreign direct investment, and energy consumption in developing European countries. Gondwana Research, 119, 138–152. https://doi.org/10.1016/j.gr.2023.02.023
Zheng, J., Zhang, J. Z., Kamal, M. M., Wang, H., Yang, Y., Dey, B., Apostolidis, C. (2025). Empowering Radical Innovation: How Digital Technologies Drive Knowledge Transfer and Co‐Creation in Innovation Ecosystems. R&D Management, 55 (5), 1444–1458. https://doi.org/10.1111/radm.12764
Feng, S., Zhang, R., Li, G. (2022). Environmental decentralization, digital finance and green technology innovation. Structural Change and Economic Dynamics, 61, 70–83. https://doi.org/10.1016/j.strueco.2022.02.008
Pu, T. (2025). How Digital Transformation Shapes Corporate R&D Expenditure: An Exploration of Multidimensional Perspectives and Innovation Consequences. Sage Open, 15 (3). https://doi.org/10.1177/21582440251349263
SmartFirmer. Precision Planting. Available at: https://www.precisionplanting.com/products/smartfirmer
Planters & Planting Equipment. John Deere. Available at: https://www.deere.com/planters
Zhai, Z., Martínez, J. F., Beltran, V., Martínez, N. L. (2020). Decision support systems for agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture, 170, 105256. https://doi.org/10.1016/j.compag.2020.105256
ARK Group. Available at: https://arkgroupp.com.ua
Text Identifier of this Chapter
Chapter 4
Published
September 30, 2025
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Copyright (c) 2026 Nataliia Sysolina, Iryna Sysolina, Volodymyr Amosov, Sergiy Nisfoyan, Olga Chumachenko, Ruslan Kisilov
How to Cite
Sysolina, N., Sysolina, I., Amosov, V., Nisfoyan, S., Chumachenko, O., & Kisilov, R. (2025). R&D process management in the context of digitalization. In R. Rogaczewski, L. Kononenko, V. Savchenko, A. Karnaushenko, Y. Hlavatska, O. Abramova, S. Kononenko, N. Sysolina, I. Sysolina, V. Amosov, S. Nisfoyan, O. Chumachenko, & R. Kisilov, In press. Management of technology and innovation. Scientific Route OÜ®. https://monograph.route.ee/rout/catalog/book/management_of_technology_and_innovation/chapter/171
