Abstract
In this work, several types of innovation diffusion are considered and formalized. The modeling takes into account the diffusion of innovations of different groups and innovations of the same group. The uniform type of diffusion of innovations and by the type of transfer are considered, which are, respectively, short-range and long-range in nature. A mixed type of distribution was also investigated, combining the features of the first two varieties. These processes are described by the authors by the methods of linear algebra. During formalization, the concept of innovative potential was introduced and a corresponding formula was obtained that defines it. The concepts presented in the work are very promising both from the point of view of theory and from the point of view of applications and calculations related to the description of the spread of technologies and innovations in social and economic systems. This topic of modeling innovative processes is very promising and will be actively developed in the future.
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References
Kurth J (2011) A tale of four crises: the politics of great depressions and recessions. Orbis 55(3):500–523. https://doi.org/10.1016/j.orbis.2011.04.017
Diakonova S, Artyshchenko S, Panfilov D, Gusev M (2022) Factors influenced on the connection density in the innovation diffusion process. In: Beskopylny A, Shamtsyan M (eds) XIV international scientific conference “INTERAGROMASH 2021”. Lecture notes in networks and systems, vol 246. Springer, Cham. https://doi.org/10.1007/978-3-030-81619-3_98
Diakonova S, Artyshchenko S, Sysoeva D, Surovtsev I (2020) Study of periodic processes of changing the number of innovations in relation to Kondratiev cycles. In: The collection: E3S web of conferences. Key trends in transportation innovation, KTTI 2019, p 04031. https://doi.org/10.1051/e3sconf/2020.15.7.04031
Dyakonova SN, Artyshchenko S, Sysoeva DV, Surovtsev IS, Karpovich MA (2020) On the application of the thermal conductivity equation to describe the diffusion process. In: The collection: E3S web of conferences. XIII international scientific and practical conference “state and prospects for the development of agribusiness – interagromash 2020”. (in Engl).
Zhang X (2018) Frugal innovation and the digital divide: developing an extended model of the diffusion of innovations. Int J Innov Stud 2(2):53–64. https://doi.org/10.1016/j.ijis.2018.06.001
Wersching K (2010) Schumpeterian competition, technological regimes and learning through knowledge spillover. J Econ Behav Organ 75:482–493. https://doi.org/10.1016/j.jebo.2010.05.005
Miranda LSM, Lima CAS (2011) On the forecasting of the challenging world future scenarios. Tech Forecast Soc Change 78(8):1445–1470. https://doi.org/10.1016/j.techfore.2011.04.001
Coccia M (2010) The asymmetric path of economic long waves. Technol Forecast Soc Chang 77:730–738. https://doi.org/10.1016/j.techfore.2010.02.003
Costa Ribeiro L, Gomez de Deus L, Mendes Loreiro P, Albuquerque EDM (2017) A network model for the complex behavior of the rate of profit: exploring a simulation model with overlapping technological revolutions. Struct Chang Econ Dyn 43:51–61. https://doi.org/10.1016/j.strueco.2017.07.001
Vargas G, Rodríguez CM (2013) Un análisis microeconómico de los efectos de la innovación en el desarrollo y el bienestar social. Economía, pp 64–76. https://doi.org/10.1016/S0185-0849(13)71341-0
Jouvet P-A, de Perthuis C (2013) Green growth: from intention to implementation. Int Econ 29–55:134. https://doi.org/10.1016/j.inteco.2013.05.003
Solheim MSW, Boschma R, Herstad SJ (2020) Collected work experience and novelty of innovation content. Res Policy 49(1):103856. https://doi.org/10.1016/j.respol.2019.103856
Juliao-Rossi J, Forero-Pineda C, Losada-Otalora M, Peña-García N (2019) Trajectories of innovation: a new approach to studying innovation performance. J Bus Res 115:322–333. https://doi.org/10.1016/j.jbusres.2019.11.026
Audretsch DB, Belitski M (2020) The role of R&D and knowledge spillovers in innovation and productivity. Eur Econ Rev 123:103391. https://doi.org/10.1016/j.euroecorev.2020.103391
Gulshan SS (2011) Innovation management: taking advantage of open platforms by internalizing internal and external innovations. Psychol-Soc Behav Sci 25:46–53. https://doi.org/10.1016/j.sbspro.2011.10.527
Raynard M (2017) Understanding academic e-books through the diffusion of innovations theory as a basis for developing effective marketing and educational strategies. J Acad Librarianship 43(1):82–86. https://doi.org/10.1016/j.acalib.2016.08.011
Schuelke-Leech B-A (2018) A model for understanding the orders of magnitude of disruptive technologies. Technol Forecast Soc Change 129:261–274. https://doi.org/10.1016/j.techfore.2017.09.033
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Diakonova, S., Artyshchenko, S., Shevchenko, L., Tsaregorodtseva, O. (2023). Modeling the Varieties of Territorial Diffusion of Innovations. In: Guda, A. (eds) Networked Control Systems for Connected and Automated Vehicles. NN 2022. Lecture Notes in Networks and Systems, vol 509. Springer, Cham. https://doi.org/10.1007/978-3-031-11058-0_102
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