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Multicollinearity Analysis of DESI Dimensions for Russian Federation and EU28 with Variance Inflation Factor (VIF)

In: Digital Transformation in Industry

Author

Listed:
  • Zoltán Bánhidi

    (Budapest University of Technology and Economics)

  • Madina Tokmergenova

    (Budapest University of Technology and Economics)

  • Imre Dobos

    (Budapest University of Technology and Economics)

Abstract

This article is aimed to analyze the statistical features of digital dimensions for Russia and the European Union countries (EU28), investigating the relationships and interdependency between the principal dimensions in our dataset, which is based on the principal dimensions of the I-DESI report, commissioned and published biennially by the European Commission. In order to understand the tenets of digital transformation and to develop a sound strategy to improve digital competitiveness, a robust measurement system is vitally important. An essential requirement for the DESI indicators, specified by the European Commission, is that they should not be statistically redundant. Our hypothesis is that the five DESI dimensions are collinear. In this article, the VIF (Variance Inflation Factor) indicator and multidimensional scaling (MDS) models are used for determining the multicollinearity of DESI dimensions. We also investigate linear regression models to find out whether each of the dimensions could be explained in terms of the others, and whether the estimation of filtered dimensions depends on using the enter or stepwise estimation procedure. We find that two of the five dimensions can be expressed as a linear combination of the remaining three with little loss of information. Our results suggest that the “no redundancy” requirement could not seem to be fulfilled by the five principal dimensions.

Suggested Citation

  • Zoltán Bánhidi & Madina Tokmergenova & Imre Dobos, 2023. "Multicollinearity Analysis of DESI Dimensions for Russian Federation and EU28 with Variance Inflation Factor (VIF)," Lecture Notes in Information Systems and Organization, in: Vikas Kumar & Grigorios L. Kyriakopoulos & Victoria Akberdina & Evgeny Kuzmin (ed.), Digital Transformation in Industry, pages 59-70, Springer.
    • Handle: RePEc:spr:lnichp:978-3-031-30351-7_6
    DOI: 10.1007/978-3-031-30351-7_6
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