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Determinants of the Use of Big Data Technologies by Organizations in Russian Regions

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  • Julia A. Varlamova, Ekaterina I. Kadochnikova

Abstract

A scientific discussion is unfolding around data as a new factor of production that contributes to the transformation of traditional sectors of the economy, industrial integration, and ensures interregional interaction. At the same time, the question of the relationship with such traditional production factors as labor and capital needs to be answered. The study aims to identify the determinants of organizations' use of Big Data at a regional level. The main hypothesis of the study suggests that the key determinants of organizations' use of big data technologies are digital labor, digital capital and the socio-economic characteristics of regions. In the study we proposed a modified knowledge production function which was tested on open data from the Federal State Statistics Service for 85 regions of Russia in 2021-2022. Panel models were constructed using the method of least squares, generalized feasible least squares. The study presents illustrative material made using cartograms and graphical methods. The results of the study distinguished the spatial heterogeneity in the use of Big Data technologies in Russia's regions and differentiation of the regions by volume of digital capital and digital labor. Panel data models with random effects confirmed the positive impact of digital labor and digital capital on organizations' use of Big Data. Among the socio-economic characteristics of regions as determinants of the use of big data technologies, significant effects were obtained for the share of urban population, gross regional product and share of innovation costs. The study identifies the determinants of the development of the data economy in Russian regions, considering geographic, technological, and economic differentiation. The theoretical significance of the study lies in the proposal of the author's concept of a modified knowledge production function, which can be used as a fundamental basis for the development of the theory of data economics. The practical significance of the study lies in the validity of the value of Big Data, the use of which can help institutions and government authorities find new opportunities for the development of the data economy, taking into account regional differentiation, improving the methodology for monitoring the use of digital technologies by organizations, and identifying the key factors influencing the use of Big Data technologies by organizations.

Suggested Citation

  • Julia A. Varlamova, Ekaterina I. Kadochnikova, 2024. "Determinants of the Use of Big Data Technologies by Organizations in Russian Regions," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 23(2), pages 422-451.
    • Handle: RePEc:aiy:jnjaer:v:23:y:2024:i:2:p:422-451
    DOI: https://doi.org/10.15826/vestnik.2024.23.2.017
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    References listed on IDEAS

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    1. Chang, Qing & Wu, Mengtao & Zhang, Longtian, 2024. "Endogenous growth and human capital accumulation in a data economy," Structural Change and Economic Dynamics, Elsevier, vol. 69(C), pages 298-312.
    2. Cong, Lin William & Wei, Wenshi & Xie, Danxia & Zhang, Longtian, 2022. "Endogenous growth under multiple uses of data," Journal of Economic Dynamics and Control, Elsevier, vol. 141(C).
    3. N. V. Novikova & E. V. Strogonova, 2020. "Regional aspects of studying the digital economy in the system of economic growth drivers," Journal of New Economy, Ural State University of Economics, vol. 21(2), pages 76-93, July.
    4. Davide Quaglione & Cesare Pozzi, 2018. "Big data economics: The features of the ongoing debate and some policy remarks," L'industria, Società editrice il Mulino, issue 1, pages 3-16.
    5. Zvi Griliches, 1998. "Issues in Assessing the Contribution of Research and Development to Productivity Growth," NBER Chapters, in: R&D and Productivity: The Econometric Evidence, pages 17-45, National Bureau of Economic Research, Inc.
    6. Julia Varlamova & Ekaterina Kadochnikova, 2023. "Modeling the Spatial Effects of Digital Data Economy on Regional Economic Growth: SAR, SEM and SAC Models," Mathematics, MDPI, vol. 11(16), pages 1-31, August.
    7. Margarita Billon & Fernando Lera-Lopez & Rocio Marco, 2016. "ICT use by households and firms in the EU: links and determinants from a multivariate perspective," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 152(4), pages 629-654, November.
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    More about this item

    Keywords

    data economics; Big Data; digital economy; regional economics; knowledge production function; panel data models.;
    All these keywords.

    JEL classification:

    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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