IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v11y2022i1p3-d1009213.html
   My bibliography  Save this article

Methodology for Economic Analysis of Highly Uncertain Innovative Projects of Improbability Type

Author

Listed:
  • Aleksandr Babkin

    (Higher School of Economics and Engineering, Institute of Industrial Management, Economics and Trade, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg 195251, Russia)

  • Nadezhda Kvasha

    (Department of Economics and Management of Infocommunications, The Bonch-Bruevich Saint Petersburg State University of Telecommunications, Saint Petersburg 193232, Russia)

  • Daniil Demidenko

    (Higher School of Economics and Engineering, Institute of Industrial Management, Economics and Trade, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg 195251, Russia)

  • Ekaterina Malevskaia-Malevich

    (Department of Management, The North-West Institute of Management-Branch of the Russian Presidential Academy of National Economy and Public Administration (RANEPA), Saint Petersburg 199034, Russia)

  • Evgeny Voroshin

    (Department of High-Tech Production Economics, St. Petersburg State University of Aerospace Instrumentation, Saint Petersburg 190000, Russia)

Abstract

Modern conditions for real investment are generally associated with increasing uncertainty, which is even more relevant when evaluating innovative projects. Current innovation analysis methods using a linear model are outdated. At the same time, an open interactive model of the innovation process, formed due to digitalization, allows to connect to innovations at almost any stage of their life cycle. The aim of the study is to form a methodology for the economic analysis of innovative projects implemented in the context of an open innovation model. To achieve the goal, the study defines approaches to innovation projects differentiation. The approach to the analysis methods selection is based on the decision matrix. The developed decision matrix allows to determine the location of each project as its element and to select analysis methods, considering the project’s uncertainty characteristics. The logic of the analysis methods transformation under the influence of a changing uncertainty level determines the combination of the fuzzy-set approach and the concept of real options. The implementation of the project analysis algorithm leads to the choice of an appropriate method for evaluating effectiveness and ensures that the flexible risk response concept under conditions of improbable uncertainty is taken into account when implementing the option model.

Suggested Citation

  • Aleksandr Babkin & Nadezhda Kvasha & Daniil Demidenko & Ekaterina Malevskaia-Malevich & Evgeny Voroshin, 2022. "Methodology for Economic Analysis of Highly Uncertain Innovative Projects of Improbability Type," Risks, MDPI, vol. 11(1), pages 1-20, December.
  • Handle: RePEc:gam:jrisks:v:11:y:2022:i:1:p:3-:d:1009213
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/11/1/3/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-9091/11/1/3/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hanne Lamberts-Van Assche & Tine Compernolle, 2022. "Using Real Options Thinking to Value Investment Flexibility in Carbon Capture and Utilization Projects: A Review," Sustainability, MDPI, vol. 14(4), pages 1-24, February.
    2. J. Muñoz & J. Contreras & J. Caamaño & P. Correia, 2011. "A decision-making tool for project investments based on real options: the case of wind power generation," Annals of Operations Research, Springer, vol. 186(1), pages 465-490, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andreas Welling, 2017. "Green Finance: Recent developments, characteristics and important actors," FEMM Working Papers 170002, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    2. Carol Alexander & Xi Chen, 2021. "Model risk in real option valuation," Annals of Operations Research, Springer, vol. 299(1), pages 1025-1056, April.
    3. Nadarajah, Selvaprabu & Secomandi, Nicola, 2023. "A review of the operations literature on real options in energy," European Journal of Operational Research, Elsevier, vol. 309(2), pages 469-487.
    4. Kim, Kyoung-Kuk & Lee, Chi-Guhn, 2012. "Evaluation and optimization of feed-in tariffs," Energy Policy, Elsevier, vol. 49(C), pages 192-203.
    5. Locatelli, Giorgio & Invernizzi, Diletta Colette & Mancini, Mauro, 2016. "Investment and risk appraisal in energy storage systems: A real options approach," Energy, Elsevier, vol. 104(C), pages 114-131.
    6. Kozlova, Mariia, 2017. "Real option valuation in renewable energy literature: Research focus, trends and design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 180-196.
    7. Huberts, Nick F.D. & Thijssen, Jacco J.J., 2023. "Optimal timing of non-pharmaceutical interventions during an epidemic," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1366-1389.
    8. Contreras, Javier & Rodríguez, Yeny E., 2014. "GARCH-based put option valuation to maximize benefit of wind investors," Applied Energy, Elsevier, vol. 136(C), pages 259-268.
    9. Gorupec Natalia & Tiberius Victor & Brehmer Nataliia & Kraus Sascha, 2022. "Tackling uncertain future scenarios with real options: A review and research framework," The Irish Journal of Management, Sciendo, vol. 41(1), pages 69-88, July.
    10. Alexander, Carol & Chen, Xi & Ward, Charles, 2021. "Risk-adjusted valuation for real option decisions," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 1046-1064.
    11. Yanzhao Li & Ju'e Guo & Yongwu Li & Xu Zhang, 2021. "Optimal exit decision of venture capital under time-inconsistent preferences," Papers 2103.11557, arXiv.org.
    12. Haifeng Zhang & Feng Gao & Jiang Wu & Kun Liu & Xiaolin Liu, 2012. "Optimal Bidding Strategies for Wind Power Producers in the Day-ahead Electricity Market," Energies, MDPI, vol. 5(11), pages 1-20, November.
    13. Liu, Jiangfeng & Zhang, Qi & Li, Hailong & Chen, Siyuan & Teng, Fei, 2022. "Investment decision on carbon capture and utilization (CCU) technologies—A real option model based on technology learning effect," Applied Energy, Elsevier, vol. 322(C).
    14. Chi Truong & Matteo Malavasi & Han Li & Stefan Trueck & Pavel V. Shevchenko, 2024. "Optimal dynamic climate adaptation pathways: a case study of New York City," Papers 2402.02745, arXiv.org.
    15. Delaney, L., 2016. "Equilibrium Investment in High Frequency Trading Technology: A Real Options Approach," Working Papers 15/14, Department of Economics, City University London.
    16. Christina E. Bannier, 2016. "Bewertungsmethoden in der Projektfinanzierung Erneuerbarer Energien [Valuation Methods for Renewable Energy Projects]," Schmalenbach Journal of Business Research, Springer, vol. 68(1), pages 75-110, April.
    17. Cheng, Cheng & Dong, Kangyin & Wang, Zhen & Liu, Shulin & Jurasz, Jakub & Zhang, Haoran, 2023. "Rethinking the evaluation of solar photovoltaic projects under YieldCo mode: A real option perspective," Applied Energy, Elsevier, vol. 336(C).
    18. Trigeorgis, Lenos & Tsekrekos, Andrianos E., 2018. "Real Options in Operations Research: A Review," European Journal of Operational Research, Elsevier, vol. 270(1), pages 1-24.
    19. Zhou, Shan & Yang, Pu, 2020. "Risk management in distributed wind energy implementing Analytic Hierarchy Process," Renewable Energy, Elsevier, vol. 150(C), pages 616-623.
    20. Zhang, Hanyu & Assereto, Martina & Byrne, Julie, 2023. "Deferring real options with solar renewable energy certificates," Global Finance Journal, Elsevier, vol. 55(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jrisks:v:11:y:2022:i:1:p:3-:d:1009213. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.