A Methodology of Phased Assessment of Competitiveness of Trading Enterprise

Arkhiiereiev, Sergii; Mieshkova, V. V.

doi:10.32983/2222-4459-2019-11-210-221

Cited by 2 publications

(6 citation statements)

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“…The synergistic effect of such digital networks facilitates the transfer of information resources and at the same time requires a change in approaches to the process of production and consumption, the creation of the most favorable conditions for the implementation of vital activities of the digital social system, ensuring the reproduction of active human resources as a productive force and a factor of economic growth of the economy (Alter, 2008). The methodology of digitalization of the labor market on the basis of human potential involves determining the parameters of the flexicurity model, which depends on economic and social challenges and is based on a combination of various indicators (Chornodid, 2013). On the one hand, they shape the trend of changes in the digital field of the labor market, and on the other hand, they assess the level of efficiency in the use of the country's human potential, which ensures the interaction of economically active human resources in the international space (Gregg & Andrijasevic, 2019).…”

Section: Figure 1 Concept Of Socio-economic Dimension Of Human Potentialmentioning

confidence: 99%

“…The general form of the correlation matrix is given by formula (1) (Chornodid, 2013;Craig et al, 1997):…”

Section: Digitization Of Social Capitalmentioning

confidence: 99%

“…To solve the task of aggregation of indicators, the index method is used, on the basis of which the intermediate (synthetic) integral indicator ( i S ) is calculated for each group of components of the socio-economic dimension of human potential in the digital space of the labor market (formula (2)) (Chornodid, 2013;Craig et al, 1997): Ranking the influence of each group of indicators of the socio-economic block on the indicator of the effectiveness of the use of human potential in the digital space of the labor market of the countries of the world.…”

Section: Digitization Of Social Capitalmentioning

confidence: 99%

“…The initial indicators are converted from absolute to normalized with the determination of the degree of achievement of the optimal value of digitalization of sources in relation to the activation of human resources in the labor market, which are distributed according to standardized factors of influence (formula (3) -( 4)) (Chornodid, 2013;Craig et al, 1997):…”

Section: Digitization Of Social Capitalmentioning

confidence: 99%

“…The next step is the ranking of intermediate (synthetic) indices for each group of components of the socio-economic dimension of human potential, which enables the calculation of the integral index ( SDHC I ) according to formula (5) (Chornodid, 2013;Craig et al, 1997):…”

Section: Digitization Of Social Capitalmentioning

confidence: 99%

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Socio-Economic Dimension of Human Potential of World Countries in the Digital Space of the Labor Market

Vasyl’yeva,

Sokolov,

Lisnichenko

et al. 2023

QAJ

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The article examines the special socio-economic conditions of human potential, which forms and expands the flow of labor in the international digital labor market. The concept of the socio-economic dimension of human potential based on the unity of its components is presented. The hypothesis of effective use of human potential at all levels of its formation is justified by relevant HRM technologies that align the labor market situation with the rate of modernization of business, state policy, and social institutions, as well as the new nature of the digital economy's growth. It is also demonstrated that, in the context of the informational transformation of the labor market, the concentration, implementation, and effective socio-economic dimension of human potential form a consistent state policy, which is strengthened by the possibilities of territorial decentralization and is aimed at overcoming disparities in the use of human resources, reducing the gap between potential and existing social-economic level of development of the countries of the world. The authors also provide the sectoral structure of employment in Ukraine and the average annual rate of growth of the indicator level in the leading countries with a digital direction of labor market development, as well as comparative socio-economic measurement and forecasting of Germany's effective use of human potential, subject to the expansion of labor inflows and the improvement of the working capacity of active human resources.

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Section: Figure 1 Concept Of Socio-economic Dimension Of Human Potentialmentioning

confidence: 99%

“…The general form of the correlation matrix is given by formula (1) (Chornodid, 2013;Craig et al, 1997):…”

Section: Digitization Of Social Capitalmentioning

confidence: 99%

Section: Digitization Of Social Capitalmentioning

confidence: 99%

Section: Digitization Of Social Capitalmentioning

confidence: 99%

Section: Digitization Of Social Capitalmentioning

confidence: 99%

See 3 more Smart Citations

Socio-Economic Dimension of Human Potential of World Countries in the Digital Space of the Labor Market

Vasyl’yeva,

Sokolov,

Lisnichenko

et al. 2023

QAJ

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Modeling of the Synthetic Indicator of Competitiveness of Agricultural Enterprises: A Methodological Approach to the Use of Neural Network Tools

Chikov,

Khaietska,

Yuliia

et al. 2023

ФКДПТП

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The article is devoted to the development of a methodical approach to modelling a synthetic indicator of the competitiveness of agricultural enterprises using the tools of neural networks.The authors used general scientific and special research methods, such as monographic, logical-theoretical, statistical and economic-mathematical, visualization, system analysis, taxonomy and neural network modelling, generalization, logical abstraction and conclusion generation. The study was based on materials from the State Statistics Service of Ukraine, scientific developments of foreign and domestic scientists on the defined topic, and financial statements of the agricultural enterprises of Vinnytsia region LLC «Ahrokompleks «Zelena dolyna», PJSC «Dashkivtsi», LLC «Selyshchanske», PE «Dary sadiv», PE «Fortuna» the main type of economic activity of which according to Classification of economic activities 01.11 – cultivation of cereals (except rice), legumes and oilseeds. The article develops and presents a non-classical approach to the assessment of the competitiveness of agricultural enterprises has been developed, which is based on the principles of neural network modelling. It allows to obtain a well-founded quantitative indicator, which can be easily interpreted into a linguistic evaluation on a three-level scale of competitiveness and used for comparison, monitoring and making sound decisions on improving the competitiveness of agricultural enterprises.The non-classical approach complements traditional methods of competitiveness assessment, expanding their capabilities and eliminating certain limitations. The use of neural network modelling in competitiveness assessment allows to take into account complex and non-linear relationships between different factors and indicators, which contributes to an increase in the objectivity and accuracy of competitiveness assessment, which in turn allows enterprises to make better decisions and improve their strategies to achieve success in the market.The results of the study can be used to support strategic decision-making in the agricultural sector, identify priority development directions, and improve the competitive strategies of enterprises and the functioning of business processes.

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A Methodology of Phased Assessment of Competitiveness of Trading Enterprise

Cited by 2 publications

References 0 publications

Socio-Economic Dimension of Human Potential of World Countries in the Digital Space of the Labor Market

Socio-Economic Dimension of Human Potential of World Countries in the Digital Space of the Labor Market

Modeling of the Synthetic Indicator of Competitiveness of Agricultural Enterprises: A Methodological Approach to the Use of Neural Network Tools

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