Skip to main content

Information Technology to Assess the Enterprises’ Readiness for Innovative Transformations Using Markov Chains

  • Conference paper
  • First Online:
Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making (ISDMCI 2022)

Abstract

The paper presents the results of research regarding the processes of assessing the degree of readiness of enterprises for innovative activities, functioning in conditions of uncertainty of economic ties and relations. A conceptual model of an innovative activity management system has been developed, aimed at improving the diagnostic and decision-making mechanisms based on the use of Markov chain tools. For its practical implementation, a simulation model has been created for assessing the degree of readiness of enterprises for innovation in the form of a directed graph, in which the vertices represent the states of the process, and the edges represent transitions between them. A distinctive feature of the model is that it is not time, but the sequence of states and the number of a step with a hierarchy of sampling intervals, which is considered as an argument on which the process of assessing the degree of readiness of enterprises for innovative activities depends. The flexibility of such a model is ensured by adaptability to the influences of the external environment with the possibility of adjusting it to each information situation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Babichev, S., Skvor, J.: Technique of gene expression profiles extraction based on the complex use of clustering and classification methods. Diagnostics 10(8) (2020). https://doi.org/10.3390/diagnostics10080584

  2. Babichev, S.A., Kornelyuk, A.I., Lytvynenko, V.I., Osypenko, V.V.: Computational analysis of microarray gene expression profiles of lung cancer. Biopolymers Cell 32(1), 70–79 (2016). https://doi.org/10.7124/bc.00090F

    Article  Google Scholar 

  3. Bilovodska, O., Kholostenko, A., Mandrychenko, Z., et al.: Innovation management of enterprises: Legal provision and analytical tools for evaluating business strategies. J. Optim. Ind. Eng. 14, 71–78 (2021). https://doi.org/10.22094/joie.2020.677820

  4. Chernev, V., Churyukin, V., Shmidt, A.: Modeling the economic sustainability of an enterprise using Markov chains with income. Bull. South Ural State Univ. 4, 297–300 (2006)

    Google Scholar 

  5. Gagliardi, F., et al.: A probabilistic short-term water demand forecasting model based on the markov chain. Water 9(7), 507 (2017)

    Article  Google Scholar 

  6. Haque, S., Mengersen, K., Stern, S.: Assessing the accuracy of record linkages with Markov chain based Monte Carlo simulation approach. J. Big Data 8(1), 1–25 (2021). https://doi.org/10.1186/s40537-020-00394-7

    Article  Google Scholar 

  7. Huang, Q., et al.: A chan-vese model based on the Markov chain for unsupervised medical image segmentation. Tsinghua Sci. Technol. 26(6), 833–844 (2021)

    Article  Google Scholar 

  8. Khmaladze, E.: Testing hypothesis on transition distributions of a Markov sequence. J. Stat. Plann. Inference 215, 72–84 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  9. Kuznetsova, M.: Scientific and Iinnovative Activities, chap. Statistical Publication, p. 380. State Statistics Service of Ukraine (2020)

    Google Scholar 

  10. Kuznichenko, V., Lapshin, V.: Generalized scarcity exchange model for continuous processes with external control. Econ. Manag. 5, 92–95 (2017)

    Google Scholar 

  11. Litvinenko, V.I., Burgher, J.A., Vyshemirskij, V.S., Sokolova, N.A.: Application of genetic algorithm for optimization gasoline fractions blending compounding. In: Proceedings - 2002 IEEE International Conference on Artificial Intelligence Systems, ICAIS 2002, pp. 391–394 (2002). https://doi.org/10.1109/ICAIS.2002.1048134

  12. Ludwig, R., Pouymayou, B., Balermpas, P., et al.: A hidden markov model for lymphatic tumor progression in the head and neck. Sci. Rep. 11(12261) (2021). https://doi.org/10.1038/s41598-021-91544-1

  13. Lytvynenko, V., Lurie, I., Krejci, J., Voronenko, M., Savina, N., Taif, M.A.: Two step density-based object-inductive clustering algorithm. In: CEUR Workshop Proceedings, vol. 2386, pp. 117–135 (2019)

    Google Scholar 

  14. Obhiamo, J., Weke, P., Ngare, P.: Modeling Kenyan economic impact of corona virus in Kenya using dicreate time Markov chains. J. Financ. Econo. 8(2), 80–85 (2020)

    Google Scholar 

  15. Panarina, D.: Arrangement of markov breaking chains in the economy. Vesnik Tyumen State Oil Gas Univ. 11(2(64)), 79–82 (2015)

    Google Scholar 

  16. Pysarenko, T., Kuranda, T., Kvasha, T., et al.: State of and Iinnovative Activity in Ukraine in 2020, chap. Statistical Publication, p. 40. State Statistics Service of Ukraine (2020)

    Google Scholar 

  17. Sharko, M., Gusarina, N., Petrushenko, N.: Information-entropy model of making management decisions in the economic development of the enterprises. Adv. Intell. Syst. Comput., 304–314 (2019). https://doi.org/10.1007/978-3-030-26474-1

  18. Sharko, M., Liubchuk, O., Fomishyna, V., et al.: Methodological support for the management of maintaining financial flows of external tourism in global risky conditions. Commun. Comput. Inf. Sci. (1158), 188–201 (2020). https://doi.org/10.1007/978-3-030-61656-4

  19. Sharko, M., Lopushynskyi, I., Petrushenko, N., et al.: Management of tourists’ enterprises adaptation strategies for identifying and predicting multidimensional non-stationary data flows in the face of uncertainties. Advances in Intelligent Systems and Computing, pp. 135–151 (2020). https://doi.org/10.1007/978-3-030-54215-3

  20. Sharko, M., Shpak, N., Gonchar, O., et al.: Methodological basis of causal forecasting of the economic systems development management processes under the uncertainty. Advances in Intelligent Systems and Computing pp. 423–437 (2020). https://doi.org/10.1007/978-3-030-54215-3

  21. Sharko, M., Doneva, N.: Methodological approaches to transforming assessments of the tourist attractiveness of regions into strategic managerial decisions. Actual Problems of Economy (8 (158)), 224–229 (2016)

    Google Scholar 

  22. Sharko, M., Sharko, A.: Innovative aspects of management of development of enterprises of regional tourism. Actual Problems Econ. 7(181), 206–213 (2016)

    Google Scholar 

  23. Sherstennikov, Y.: Application of the Markov process model to the study of the economic efficiency of the firm. Econ. Herald Donbass 2, 5–12 (2007)

    Google Scholar 

  24. Shmidt, A., Churyukin, V.: Markov models of economic systems. Bull. South Ural State Univ. 9(3), 100–105 (2015)

    Google Scholar 

  25. Vorobyova, K.: The effect of brand perception in Malaysia’s international airline industry during covid 19. Ann. Soc. Sci. Manage. Stud. 6(4) (2021). https://doi.org/10.19080/ASM.2021.06.555693

  26. Vorobyova, K.: The impact of individual work practices, social environment, managerial skills on workers’ productivity: mediating role of international work experience. Int. J. Pharmaceutical Res. 13, 26–27 (2021). https://doi.org/10.31838/ijpr/2021.13.02.438

  27. Wang, L., Laird-Fick, H., Parker, C., et al.: Using Markov chain model to evaluate medical students’ trajectory on progress tests and predict usmle step 1 scores a retrospective cohort study in one medical school. BMC Med. Educ. (21) (2021). https://doi.org/10.1186/s12909-021-02633-8

  28. Zhao, Y., et al.: Spatio-temporal Markov chain model for very-short-term wind power forecasting. J. Eng. 2019(18), 5018–5022 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sharko, M. et al. (2023). Information Technology to Assess the Enterprises’ Readiness for Innovative Transformations Using Markov Chains. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making. ISDMCI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 149. Springer, Cham. https://doi.org/10.1007/978-3-031-16203-9_12

Download citation

Publish with us

Policies and ethics