Investigating the digital convergence in European Union: an econometric analysis of pitfalls and pivots of digital economic transformation

Vasile, A. J.; Chivu, Luminiţa; Sima, Violeta; Gheorghe, Ileana Georgiana; Nancu, Dumitru; Duică, Mircea Constantin

doi:10.1080/1331677x.2022.2142814

Cited by 11 publications

(14 citation statements)

References 32 publications

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“…In recent decades, the escalation of environmental pollution and the challenges of economic transformation have gained increasing prominence in developing nations [ 30 ]. Notably, China grapples with a significant dependence on energy resources [ 31 ].…”

Section: Literature Evaluationmentioning

confidence: 99%

Digital economy and carbon emission: The coupling effects of the economy in Qinghai region of China

Sun,

Di,

Shi

2024

Heliyon

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Section: Literature Evaluationmentioning

confidence: 99%

Digital economy and carbon emission: The coupling effects of the economy in Qinghai region of China

Sun,

Di,

Shi

2024

Heliyon

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“…The dimensions are logically linked and cannot be separated, but their methodological separation allows the complex social phenomenon of digitalization to be studied. The studies analyzing the digital transformation of national economies also examine these four indicators and the changes in their values [44][45][46][47].…”

Section: Desi Of Digital Literacymentioning

confidence: 99%

“…This study examines the evolution of the DESI indicator and GDP per capita from 2015 to 2021, based on the average values of the European Union and Hungary (Table 3). The studies analyzing the digital transformation of national economies also examine these four indicators and the changes in their values [44][45][46][47].…”

Section: The State Of Digital Transformation In Hungary Based On Desi...mentioning

confidence: 99%

Digital Transformation of Hungary’s Economy between 2015 and 2021: Results and Future Objectives

Török

2024

Sustainability

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Hungary is a member of the European Union (E.U.), so more than three-quarters of its trade relations are with the E.U. Hungary’s strategic objective is to be among the top-ten E.U. member states in digitalization by 2030. This study aims to examine the country’s digitization development and planned digitization programs and, based on these, to predict Hungary’s expected digitization status. This study also attempts to answer whether Hungary is among the ten most digitally developed E.U. member states. We use the K-means clustering method to assess the current state of digitization and different generic methods to determine future development. The results show that Hungary’s digital development is close to the middle level of the E.U. However, future digital development in Hungary will be more dynamic than in the European Union. This more dynamic Hungarian growth is predicted to catch up with the average of the E.U.’s digital development. However, the results of the extrapolation calculations show that the Hungarian goal of Hungary being among the ten most developed digital countries in the E.U. by 2030 is unrealistic.

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“…To further investigate the convergence characteristics of digital economy development, scholars calculated the σ convergence and β convergence. For example, Andrei et al used sigma convergence analysis to study the gaps between 28 countries of the European Union, and the research showed that the gaps were gradually narrowing [29]. Xu et al analyzed the convergence level using the data from 2005 to 2009 and predicted the future trend [30].…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial Imbalance, Dynamic Evolution and Convergence of the Digital Economy: Analysis Based on Panel Data of 278 Cities in China

et al. 2023

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Accelerating the development of the digital economy is the way to build a modern industrial system and promote sustainable development. In order to accurately analyze the development status of China’s digital economy, this study introduced a text analysis method to construct an index of the digital economy and surveyed the digital economy based on the panel data of 278 Chinese cities from 2011 to 2019. Moran’s I index, the Dagum Gini coefficient, the kernel density and a Markov chain were used to reveal the space-time difference and dynamic change characteristics. Considering the impact of the spatial correlation and regional division on convergence, we compared the σ values and spatial σ values to study the convergence characteristics after grouping with the decision tree method. The research showed that the digital economy had greatly improved, but it showed a significant imbalance. The research on the regional division of cities according to their geographical distribution and grade showed that the development status of the digital economy was increasingly different, and there was no convergence feature. We chose continuous classification variables and used the decision tree method to divide cities into 10 groups to investigate the convergence. The results showed that the σ values and spatial σ values decreased significantly and showed convergence characteristics. The development of the digital economy showed convergence, indicating that the convergence was greatly affected by the geographical location and grouping basis. Overall, this study contributes to our understanding of the development status of the digital economy, and targeted policy recommendations were proposed to improve the level of digital economy development.

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Investigating the digital convergence in European Union: an econometric analysis of pitfalls and pivots of digital economic transformation

Cited by 11 publications

References 32 publications

Digital economy and carbon emission: The coupling effects of the economy in Qinghai region of China

Digital economy and carbon emission: The coupling effects of the economy in Qinghai region of China

Digital Transformation of Hungary’s Economy between 2015 and 2021: Results and Future Objectives

Spatial Imbalance, Dynamic Evolution and Convergence of the Digital Economy: Analysis Based on Panel Data of 278 Cities in China

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