Modeling and Analysis of Digital Innovation Development Processes
M. A. Elovskaya ()
Administrative Consulting, 2026, issue 3
Abstract:
Innovations in the modern world are the main driver of economic development. Digital innovations are the most significant, being cross-cutting in nature and therefore having the most significant impact on economic growth. This study identifies patterns in the development of digital innovations in the economy using quantitative analytical methods. The structural characteristics of innovation activity, the degree of diffusion of digital technologies, and the effectiveness of their implementation are considered. The aim of this article is to identify patterns in the development of digital innovations in the Russian economy using quantitative assessment methods, including constructing an integral index and analyzing the structural characteristics of innovative digital activity. The article proposes an integral index of digital innovations based on the normalization of statistical indicators and weighted aggregation. An analysis of this indicator's dynamics for 2020–2024 is conducted, revealing imbalances in sectoral development and institutional limitations. The need for a transition to more complex models for assessing the effectiveness of digital transformations is substantiated, as calculations using the author's digital innovation effectiveness indicator indicate declining returns.
Date: 2026
References: Add references at CitEc
Citations:
Downloads: (external link)
https://www.acjournal.ru/jour/article/viewFile/2994/2145 (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:acf:journl:y:2026:id:2994
Access Statistics for this article
More articles in Administrative Consulting from Russian Presidential Academy of National Economy and Public Administration. North-West Institute of Management.
Bibliographic data for series maintained by ().