Psychological aspects of social security in Ukraine

The article examines the competitiveness of Ukrainian banks influenced by economy digitalization, the dynamic spread of electronic payments and e-commerce, as well as innovative technologies aimed at providing digital services. When shifting to an Online Platform business model, a bank can expand its range of banking products, attract more customers, thereby forming a competition policy and gaining competitive advantages. The paper aims to assess the digitalization level affecting the general competitiveness of banks and its components based on Ukrainian banks. For this purpose, the following methods were used: standardized input statistical indicators, comparison and ranking, a cluster analysis, and a regression and correlation analysis. The cluster analysis confirmed the current role of digitalization as a competition driver that determines the competitive advantages of banks and creates additional opportunities to expand the customer base and the range of services. The correlation and regression dependence of the competitive position identified by the activity indicators of certain banks on the level of competitive digitalization confirmed a close direct impact on the competitive position of personal deposits arising from the development of digital banking technology; the pre-tax income, profiles of assets and personal loans, and corporate deposits are subject to a significant direct impact, while the weakest direct impact determines corporate loans. The foregoing substantiates the feasibility of large-scale introduction of innovative digital technologies by banks to maintain competitive positions in the banking sector of the economy. Applying the proposed approach based on certain regression equations, managers of Ukrainian banks will be able to assess the efficiency and make appropriate decisions concerning investing in digital tools and services.

Modern economy is characterized by rapid qualitative and quantitative changes that significantly affect the nature of economic, socio-economic and social relations. Innovative processes and trends are very specific manifestations, which are reflected in the economic and marketing theory. A greater place in science and practice is occupied by the concepts of new economy, knowledge economy, knowledge society. Therefore, the study of knowledge economy marketing becomes more and more relevant.
The paper is aimed to develop a technique for selection of the key parameters for building the model of national knowledge economy marketing.
For this purpose, it is proposed to conduct a cluster analysis based on aggregated data. Classification of differences between clusters is given. As a result of classification, the authors have identified a group of indicators, which make all clusters distinctive and, first and foremost, determine positions of countries in the global landscape. These indicators are interpreted as key factors of the knowledge economy.
Based on the suggested mathematical functions, the authors assessed the value of every key factor within the selected group. It became the second step in selecting the parameters to build a multifactor model of knowledge economy marketing at the national level. The paper also justifies that it is reasonable to use cognitive approach to address challenges in the sphere under consideration. This approach is able to become a sound basis for building the model of national knowledge economy marketing in the form of cognitive map.

The establishment of a company cannot be separated from its environmental and social factors. Sustainability reports start from those applied to current companies because there are forms of corporate accountability to stakeholders and community considerations of the company to provide social responsibility. This study finds out and empirically proves that there are differences in each Global Reporting Initiative (GRI) G4 indicator in the company’s sustainability report in each industry classification. The authors investigate the dominant indicators in each industry classification based on sustainability reports. The data are obtained from 28 GRI G4-based company sustainability reports in 2016 and 2017. The analytical method in the study is the K-means clustering analysis. The results of study indicate the differences in GRI G4 in 2016 and 2017. The researchers find out that the dominant indicator expressed in the financial industry is an economic indicator. Meanwhile, in the mining, transportation and infrastructure industries, basic and chemical industries etc. the dominant indicators to be disclosed are environmental indicators. This research provides a theoretical basis for sustainability and environmental reporting, particularly in the context of developing countries. It is expected that this study should also inform business practitioners as well as policymakers vis-à-vis sustainability reporting in practice.