DEVELOPMENT OF AN ALGORITHM FOR CLASSIFYING SOCIO-ECONOMIC SYSTEMS OF REGIONS BY THE LEVEL OF INNOVATION POTENTIAL MANAGEMENT

Abstract

Background. In modern conditions the economic success of regional development is determined by the speed of managerial responses to changes and challenges in the external environment. The purpose of the study was to develop an algorithm for classifying socio-economic systems according to the level of management of innovation potential of the regions, as part of the long-term objectives of building a competitive economy, which is an important factor in achieving technological sovereignty of the country. Materials and methods. When compiling the dataset of actual values of the integral level of performance of the tools of management of innovation potential of socio-economic mesosystems the analytics of Leontief Centre – AV Group Consortium (LC-AV), based on the data of Rosstat version 2022 on the data of 2020–2021 was used. The article applied methods of univariate clustering based on the Fisher algorithm, hierarchical cluster analysis, k-means clustering, fuzzy clustering. Euclidean distance was used to assess dissimilarity, Ward's method of agglomeration was used, centring was not performed, truncation was determined based on the adapted Hartigan index. Results. An 8-stage process of selecting an effective data clustering algorithm is proposed for application. Identification of 85 socioeconomic mesosystems of the Russian Federation using the author's methodology of classification by the level of innovation potential management is carried out. Conclusions. In the matrix of balanced scientific and technological development the influence of innovation potential management factors on five groups of socio-economic mesosystems clusters (strong, medium-strong, medium, medium-weak and weak innovators) was investigated, which will give further opportunity to develop managerial decisions regarding the development of differentiated strategies and policies adapted to the needs and characteristics of each mesosystem in order to improve competitiveness, technological sovereignty and quality of life.

Keywords

• management
• innovation potential
• algorithm
• data clustering
• socioeconomic systems of regions