Key Drivers of Digitalization; EU Context and Baltic Case

Karnītis, Ģirts; Virtmanis, Andris; Karnītis, Edvīns

doi:10.22364/bjmc.2019.7.1.06

Cited by 17 publications

(9 citation statements)

References 7 publications

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“…Algorithms of regression analysis and benchmarking were applied to explore cause-and-effect relationships, to identify the bestperforming leaders, and to determine how those that are currently less successful and lagging behind could progress faster. The authors' previous experiences in using modelling procedures to simulate various socio-economic processes confirms the perspective of such an approach [60,61].…”

Section: Data Mining Algorithms As a Research Toolsupporting

confidence: 57%

Sustainable Development Model of EU Cities Compliant with UN Settings

et al. 2021

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Nowadays, the globally accepted UN concept of sustainable development (SD) is gradually transferred to the city level, including small and medium-sized cities. The implementation of SD settings requires regular measurement of developmental progress to monitor the level achieved in statics and dynamics, and to make strategic decisions for the next period. The existing urban SD indicator systems and indices are not well-suited for the monitoring of specific cities. Benchmarking algorithms and mathematical modelling procedures were applied to create a methodology and mathematical model for measuring the achieved urban SD level and to ensure the most objective selection and proportions of key performance indicators (KPIs) to be included in the model. The model (1) complies with the UN concept, (2) is usable for any EU city, (3) reflects the level of quality of life achieved, and (4) includes a limited number of KPIs related to municipal functionality. Mathematical computation of the (1) causality between the KPIs, (2) selection and proportions of KPIs, and (3) the general level of urban SD, as well as the reasonable combination of universality, accuracy, stability, and simplicity are strong advantages of the model. Using the published mathematical expressions of the model, calculation of the SD level does not require specific skills; the performed stability test also confirms that annual calibration of the model is not necessary. The index will help municipalities in planning and managing SD, and in the rational use of their usually limited resources. As a pilot project, SD level values are calculated for several cities.

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Section: Data Mining Algorithms As a Research Toolsupporting

confidence: 57%

Sustainable Development Model of EU Cities Compliant with UN Settings

et al. 2021

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“…Several studies conduct country-by-country evaluations, comparing different countries or regions based on the main factors of the DESI index. Karnitis et al [50] compares the Baltic countries with the EU and makes a critical statement on the factors of the DESI index, considering that data mining and mathematical modelling are the main drivers of digitisation. Ershova et al [51] assessed the development of the digital economies of Russia, Ukraine, and developed countries and concluded that digital, technology-driven economies would be the engines of growth.…”

Section: Literature Reviewmentioning

confidence: 99%

Convergence and the Matthew Effect in the European Union Based on the DESI Index

et al. 2022

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The European Commission (EC) has monitored Member States' digital progress through the Digital Economy and Society Index (DESI) since 2014. The DESI index currently ranks the EU Member States and monitors their progress based on four core and 33 individual indicators. We sought to determine whether convergence between the Member States could be detected using the DESI’s annual databases. By examining the variation in the indices, we propose the existence of a so-called “Matthew effect”, i.e., the “rich get richer” syndrome among the 27 EU Member States. We also hypothesised that the COVID-19 pandemic would influence the change in the DESI. Issues investigated were those using bibliometric, statistical-mathematical methods. The σ-convergence analysis was used to estimate the reduction over time of the differences between the Member States, while the β-convergence analysis was used to estimate the rate of catching up with the initial level of development. A PCA analysis was performed to verify the Mathew effect with additional λ-variances considering real GDP per capita change. The σ-convergence was confirmed over the period 2016–2021. The β-convergence was significantly confirmed, and the research also revealed that the half-life of catching up is approximately 20 years. The suggestion of a Matthew effect in the 2016–2021 period, although not significantly confirmed, tends to suggest its existence. The COVID-19 pandemic’s impact on the value of the DESI index is likely to be affected, but future studies are needed to find support for this hypothesis. The study concludes that convergence between the EU-27 Member States can be detected based on the DESI, but this does not imply convergence for all four core DESI indicators.

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“…Key findings are that Western Balkan economies are generally ready to provide methodologically aligned data for the DESI indicators. Karnitis et al (2019) compared the Baltic countries with the EU. Banhidi et al (2020) investigate the five principal dimensions of DESI using a series of multivariate statistics.…”

Section: Desimentioning

confidence: 99%

Multidimensional Evaluation of EU and Slovakia in the Context of Digital Transformation

Jenčová¹,

Vašaničová²,

Miškufová³

2023

CEBR

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The Covid-19 pandemic highlighted the cardinal intent, in which digital technologies play a key role in building a sustainable future, revealing disparities between digitally equipped companies and those that are still implementing digital solutions. It showed the difference between urban, rural and remote areas with good connections. This paper is divided into two parts. The first aim is to evaluate the performance of the digital economy and society according to its basic dimensions, quantify and compare the position of 28 European Union countries (considering Great Britain as a member) in the international area using DESI -(Digital Economy and Society Index). It will look to evaluate the overall change in the development of the Slovak Republic between individual periods based on the main dimensions of the composite index. The second part aims to find the clusters of the European Union countries by using data of their rankings within five dimensions of DESI. Correlation analysis is used to meet the first aim, cluster analysis is used to meet the second aim. The data are obtained from the Ministry of Investment, Regional Development and Informatization of the Slovak Republic and the database of the European Centre for Digitization. The results show a statistically significant correlation in the rankings of the EU countries between the analysed years 2016-2020. Moreover, among the European Union countries, there are internal homogeneous and external heterogeneous groups of countries with respect to ranking within DESI dimensions. Cluster analysis has not been used so far in analysing DESI in the existing literature. Therefore, the results of this paper fill such a research gap. Implications for Central European audience: Quantification and comparison of the position of 28 European Union countries in the international area using DESI can help to identify and improve the digital competitiveness of European Union countries. Knowing five dimensions of DESI, its specific subdimensions, and indicators can help to identify controversial areas to which the state should pay attention. This paper deals, in more detail, with the position of Slovakia in the digital competitiveness of the EU using DESI.

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Key Drivers of Digitalization; EU Context and Baltic Case

Cited by 17 publications

References 7 publications

Sustainable Development Model of EU Cities Compliant with UN Settings

Sustainable Development Model of EU Cities Compliant with UN Settings

Convergence and the Matthew Effect in the European Union Based on the DESI Index

Multidimensional Evaluation of EU and Slovakia in the Context of Digital Transformation

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