INTERDEPENDENCE ASSESSING FOR NETWORKED READINESS INDEX ECONOMIC AND SOCIAL INFORMATIVE FACTORS*

• Main Authors: Maksym Sitnicki, Iryna Netreba
• Format: Article
• Language: English
• Published: Izdevnieciba “Baltija Publishing” 2020-03-01
• Series: Baltic Journal of Economic Studies
• Subjects: factor analysis fa; network readiness index (nri); information technologies; economic impacts; macroeconomics; development trends assessment
• Online Access: http://baltijapublishing.lv/index.php/issue/article/view/789

The purpose of the paper is to identify the common factors and their influence on features of NRI and, as a result, the impact on the competitiveness and well-being of Ukraine. The most influential economic indicators for the similar economic changes in the European countries are determined. Exploratory factor analysis has been used to uncover the underlying structure of relationships between measured variables that constructs the value of the Networked Readiness Index (NRI). Methodology. This research is based on a materials for the Eastern European countries, including Ukraine, Bulgaria, the Czech Republic, Estonia, Latvia, Lithuania, Poland and Romania, which have been used for the numerical evaluation of the data. The selection criterion for these countries is in many respects a similar evolutionary path of market economy development. Exploratory factor analysis (EFA) is used to investigate possible relationships between variables that are unique factors and NRI. In this case, EFA is used to analyze the relationship between Environment subindex (Political and regulatory environment with Business and innovation environment), Readiness subindex (Infrastructure, Affordability and Skills), Usage subindex (Individual usage, Business usage, and Government usage) and Impact subindex (Economic impacts and Social impacts) or observable variables and how it is affected by total summary NRI. As the predefined structure has not been set, EFA is used to measure the underlying factors that affect the variables in the data structure. Selecting factors and variables so as to avoid too much similarity of characteristics is also important. The set of subindexes values is divided on 31 variables corresponding to the reports' data. EFA has been carried out on R programming language for statistical computing by using environment and graphics supported by the R Foundation for Statistical Computing (GNU project). Results. Data dependency estimation for the macroeconomically significant Network Readiness Index has been implemented. It is proposed to construct a space of constituent parameters. Eigenvectors have been obtained for an array of data for the economies of eight European countries, which allow us to estimate the general development trends for macroeconomic decision-making problems. In particular, three complex factors are identified. Practical implications. The vectors determine change of the constructs of the value of the Networked Readiness Index of countries. EFA with dataset rawfl, method is maximum likelihood, diagonals of the correlation matrix are equal to squared multiple correlations. PA test is carried out to compute the eigenvalues for the correlation matrix. The study also made it possible to forecast the pace of development of information technology under the influence of the global viral pandemic COVID 19, which will launch a global economic and social recession. Value/originality. The algorithm proposed in this research is proved improving of discriminating between indicators in construct of the value the Networked Readiness Index.