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Multimodal business analytics: The concept and its application prospects in economic science and practice

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  • Pavel A. Mikhnenko

    (Bauman University, Moscow, Russia)

Abstract

One of the problems of business analysis is obtaining and processing an ever-increasing volume of economic, financial, organizational, political and legal data. Multimodal business analytics is a new methodology combining the methods of classical business analysis with big data technologies, intelligent business analytics, multimodal data fusion, artificial neural networks and deep machine learning. The purpose of the study is to determine the conceptual foundations of the phenomenon of multimodal business analytics and substantiate the prospects for its use in economic science and practice. Methodologically, the study rests on the systems approach, i.e., multimodal business analytics is examined as a unique integrated phenomenon comprised of several interrelated components. The evidence base covers research studies of 2000–2022 on multimodal business analytics from Scopus and the Russian online database eLibrary.ru. Empirical methods were used to collect and evaluate the dynamics of the number of relevant publications and their segmentation by subject areas. We have proposed own thesaurus and ontology of the key terms that make up the phenomenon of multimodal business analytics. It is shown that the use of the concept allows expanding the range of data, exposing hidden interrelations of organizational and economic phenomena and synthesizing fundamentally new information needed for effective decision-making in business.

Suggested Citation

  • Pavel A. Mikhnenko, 2023. "Multimodal business analytics: The concept and its application prospects in economic science and practice," Upravlenets, Ural State University of Economics, vol. 14(6), pages 2-18, December.
    • Handle: RePEc:url:upravl:v:14:y:2023:i:6:p:2-18
    DOI: 10.29141/2218-5003-2023-14-6-1
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    References listed on IDEAS

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    1. Bela S. Bataeva & Aglaya D. Kokurina & Nikita A. Karpov, 2021. "The impact of ESG reporting on the financial performance of Russian public companies," Upravlenets, Ural State University of Economics, vol. 12(6), pages 20-32, October.
    2. Fedorova, E. & Afanasev, D. & Nersesyan, R. & Ledyaeva, S., 2020. "Impact of non-financial information on key financial indicators of Russian companies," Journal of the New Economic Association, New Economic Association, vol. 46(2), pages 73-96.
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    4. A. N. Oleinik, 2021. "Uses of content analysis in economic sciences: An overview of the current situation and prospects," Voprosy Ekonomiki, NP Voprosy Ekonomiki, issue 4.
    5. S. V. Smirnov & S. S. Smirnov, 2022. "Monitoring Russian business cycle with daily indicators," Voprosy Ekonomiki, NP Voprosy Ekonomiki, issue 5.
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    More about this item

    Keywords

    multimodal business analytics; business analysis; data mining; data fusion; neural networks; machine learning;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C89 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other

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