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Causality: Intelligent Valuation Models in the Digital Economy

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  • Dmitry Nazarov

    (Department of Business Informatics, Ural State University of Economics, 620144 Yekaterinburg, Russia)

Abstract

The study of the economic process can be presented as a chain of reflections on the causes and consequences of the particular phenomenon’s occurrence, within the framework of which scientists try to study and understand the nature of cause-and-effect relationships and find out the mechanisms of their occurrence. This article discusses three well-known conceptual approaches to the assessment of causation in socioeconomic sciences: successionist causation, configurational causation, and generative causation. The author gives his own interpretation of these approaches, constructs graphic interpretations, and also offers such concepts as a linear sequence of factors, the causal field, and the causal space of factors in the economy and socioeconomic processes. Within the framework of these approaches, the development trends of these and new models are formulated, taking into account the transition of the world economy to a digital format. The article contains specific examples from the author of the causality models’ implementation in scientific research related to assessing the impact of corporate culture on the main indicators of an organization’s performance in various contexts.

Suggested Citation

  • Dmitry Nazarov, 2020. "Causality: Intelligent Valuation Models in the Digital Economy," Mathematics, MDPI, vol. 8(12), pages 1-16, December.
  • Handle: RePEc:gam:jmathe:v:8:y:2020:i:12:p:2174-:d:457358
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    References listed on IDEAS

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    Cited by:

    1. Dmitry Nazarov & Yerkebulan Baimukhambetov, 2022. "Clustering of Dark Patterns in the User Interfaces of Websites and Online Trading Portals (E-Commerce)," Mathematics, MDPI, vol. 10(18), pages 1-12, September.
    2. Rui Luo & Lijia Sun & Yin Kuang & Ping Deng & Mengna Lu, 2022. "Research on the Graphical Model Structure Characteristic of Strong Exogeneity Based on Twin Network Method and Its Application in Causal Inference," Mathematics, MDPI, vol. 10(6), pages 1-13, March.
    3. Yuxia Guo & Huiying Mao & Heping Ding & Xue Wu & Yujia Liu & Hongjun Liu & Shuling Zhou, 2022. "Data-Driven Coordinated Development of the Digital Economy and Logistics Industry," Sustainability, MDPI, vol. 14(14), pages 1-18, July.
    4. Kobiljon Khushvakhtzoda (Barfiev) & Dmitry Nazarov, 2021. "The Fuzzy Methodology’s Digitalization of the Biological Assets Evaluation in Agricultural Enterprises in Accordance with the IFRS," Mathematics, MDPI, vol. 9(8), pages 1-16, April.

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