Management of Research and Development Activities in the Context of Strategy Europe 2020

Spišáková, Emília Duľová; Gontkovičová, Barbora; Majerníková, Janka; Spišák, Emil; Pacana, Andrzej

doi:10.17512/pjms.2019.19.2.09

Cited by 5 publications

(7 citation statements)

References 19 publications

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“…The second solution of the optimization problem (12) corresponds with criteria prioritization (14) according to decreasing entropy E 2,4,7,14 values:…”

Section: Results Of Criteria Weight Calculationmentioning

confidence: 99%

“…Thus, the four-clusters solution confirms the stability of clusters of the strongest and weakest countries. 3,4,5,6,7,8,9,10,11,12,14,15,16,17,18,19,20,21,22,24,25,26,27,28 Ireland 7 0.4729 1,2, 3,4,5,6,7,8,9,10,11,12,14,15,16,17,18,19,20,21,22,24,25,26,27,28 Bulgaria 2 0.4448 1,2, 3,4,5,…”

Section: Countries Clustering Solutionmentioning

confidence: 99%

“…The aim of [10] was to evaluate and compare the level of regional development in the selected EU countries (Czech Republic, Poland, Slovakia). The emphasis on the southern countries 2 of 26 of the European Union is made in [12], on the Baltic states in [13], and on the situation in Poland and Slovakia in [14]. The disparities between the highly developed western countries and south European countries in the level of implementation of the strategy are revealed in [9].…”

Section: Introductionmentioning

confidence: 99%

“…Some articles do not cover the trends in progress of all headline indicators, but only certain optional indicators have been selected. The authors were interested in these areas of Europe 2020 strategy development: the performance of education systems [3,12], poverty and social exclusion [16], research and development management [14], unemployment [17], resource efficiency [8], issues of the labor market, income disparities and poverty [18], and greenhouse gas emissions [13].…”

Section: Introductionmentioning

confidence: 99%

“…Research methods most often used in articles are as follows: statistical methods [2,9,14,15,17,19,20], multiple-criteria decision making [4,8,10,11], the DEA (data envelopment analysis) method [3,18], and fuzzy goal programming [7].…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Europe 2020 Strategy Implementation Using Interval Entropy and Cluster Analysis for Interrelation between Two Groups of Headline Indicators

Kosareva

Krylovas

2021

Entropy

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The research analyzes the progress of Member States in the implementation of Europe 2020 strategy targets and goals in 2016–2018. Multiple criteria decision-making approaches applied for this task. The set of headline indicators was divided into two logically explained groups. Interval entropy is proposed as an effective tool to make prioritization of headline indicators in separate groups. The sensitivity of the interval entropy is its advantage over classical entropy. Indicator weights were calculated by applying the WEBIRA (weight-balancing indicator ranks accordance) method. The WEBIRA method allows the best harmonization of ranking results according to different criteria groups—this is its advantage over other multiple-criteria methods. Final assessing and ranking of the 28 European Union countries (EU-28) was implemented through the -cut approach. A -means clustering procedure was applied to the EU-28 countries by summarizing the ranking results in 2016–2018. Investigation revealed the countries–leaders and countries–outsiders of the Europe 2020 strategy implementation process. It turned out that Sweden, Finland, Denmark, and Austria during the three-year period were the countries that exhibited the greatest progress according to two headline indicator groups’ interrelation. Cluster analysis results are mainly consistent with the EU-28 countries’ categorizations set by other authors.

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“…The second solution of the optimization problem (12) corresponds with criteria prioritization (14) according to decreasing entropy E 2,4,7,14 values:…”

Section: Results Of Criteria Weight Calculationmentioning

confidence: 99%

Section: Countries Clustering Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Assessing the Europe 2020 Strategy Implementation Using Interval Entropy and Cluster Analysis for Interrelation between Two Groups of Headline Indicators

Kosareva

Krylovas

2021

Entropy

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European Union and its progress towards affordable and clean energy in context of the Agenda 2030

Megyesiova,

Dul’ová Spišáková,

Gontkovičová

2023

PLoS ONE

Self Cite

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The topic related to affordable and clean energy is currently highly actual. It is essential to realize that affordable and clean energy is energy without negative effects on the environment. Its advantage is that a lot of clean energy is renewable. Therefore, this type of energy contributes positively to the development of several spheres in the economy, such as agriculture, trade, communications, education, health and transport. The aim of the study is to analyze changes in the development of sustainable indicators set of affordable and clean energy in relation to Sustainable Development Goal 7 in the European Union, using cluster analysis to identify the differences and compare changes in the grouping of countries into clusters in the two years studied (2010, 2020). In addition to categorizing countries and confirming differences among member states of the European Union, the results allow us to evaluate the contribution of selected indicators to achieving affordable and clean energy. Our findings indicate that primary energy consumption per capita, final energy consumption in households per capita, energy productivity, share of renewable energy in gross final energy consumption and population unable to keep their home adequately warm shows a positive trend and supports the achievement of the Sustainable Development Goal 7.the energy import dependency is a problematic area in several countries. The results of cluster analysis showed that the largest shifts within the clusters were recorded in Italy, Estonia, and Luxembourg. The most positive shift occurred in Italy due to a significant improvement in four indicators. Luxembourg as a solo country cluster showed in 2020 some of the worst results due to the highest primary energy consumption per capita and the lowest share of renewable energy in gross final energy consumption. The dynamics of Estonia within the clusters was marked by the lowest value of the indicator of dependence on energy imports, as well as an increase in the share of renewable energy and insufficient energy productivity. The results of our study also confirm, that Sweden and Finland are leader countries, which despite high energy consumption are applying renewable energy sources to a greater extent by which they make a positive movement toward affordable and clean energy.

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In search of key determinants of innovativeness in the regions of the Visegrad group countries

Roszko‐Wójtowicz

Dańska-Borsiak

Grzelak

et al. 2022

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Research background: Discussions on the state of the economy in times of crisis focus not only on maintaining or improving innovativeness, but also on the emergence of new dimensions of this phenomenon and changing the significance of individual determinants of innovativeness. Innovativeness is a complex, multidimensional and difficult to measure phenomenon, which implies the need to select various indicators and methods for its assessment. Synthetic measures of innovativeness are widely used in comparative analyses, in particular presenting results in international or interregional cross-sections. The degree of innovativeness should also be assessed at different levels of economic aggregation. The lower the level of aggregation, the easier it becomes to capture the specific determinants of the increase in innovativeness of a given area. Purpose of the article: The main aim of the paper is to attempt to measure the relationship between expenditures and results of innovative activities for NUTS-2 regions of the Visegrad Group countries. Three variables were adopted to describe the effects of innovative activity: PCT patent applications per billion GDP (in PPS), trademark applications per billion GDP (in PPS) and public-private co-publications per million of population. Methods: The study covered 37 NUTS-2 regions of the Visegrad Group countries in the years 2014?2021. From the point of view of the purpose of the paper and the need to search for the relationship between expenditures on innovative activity and the results of this activity, it is worth emphasizing that the use of static and dynamic econometric models proved to be a substantively correct solution leading to the formulation of clear conclusions. Findings & value added: The conducted research confirmed that business R&D expenditure on GDP has a positive effect on inventions expressed by patents and trademarks, especially in the long run. In addition, the literature review and empirical analyses indicate that the main determinants of innovativeness (both before and during the pandemic) are the expenditures of economic entities on R&D, competences expressed by the level of education or participation in tertiary education, as well as the number of ICT specialists and the percentage of people employed in science and technology. Despite the deterioration of many macroeconomic indicators in the countries of the Visegrad Group, the expenditures of the business sector on R&D in most regions did not decrease between 2019 and 2021. The added value of the paper is the presented research procedure, which can be used in analyses of innovativeness also for other groups of regions.

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Management of Research and Development Activities in the Context of Strategy Europe 2020

Cited by 5 publications

References 19 publications

Assessing the Europe 2020 Strategy Implementation Using Interval Entropy and Cluster Analysis for Interrelation between Two Groups of Headline Indicators

Assessing the Europe 2020 Strategy Implementation Using Interval Entropy and Cluster Analysis for Interrelation between Two Groups of Headline Indicators

European Union and its progress towards affordable and clean energy in context of the Agenda 2030

In search of key determinants of innovativeness in the regions of the Visegrad group countries

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