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Authors:
Martin Maris, ORCID: https://orcid.org/0000-0001-8276-3308 Slovak University of Agriculture, Slovakia
Pages: 110-120
Language: English
DOI: https://doi.org/10.21272/mmi.2022.2-10
Received: 20.01.2022
Accepted: 22.06.2022
Published: 30.06.2022
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Abstract
The paper’s main objective is to investigate the differences in competitiveness among the EU member states. Each nation’s competitiveness is determined by its government’s management capabilities on the national, regional and local levels and thus poses substantial implications to economic growth. For this purpose, hierarchical clustering as the primary method of investigation was selected. The data mining process was based on extracting GCI score data about individual member states. Based on the GCI score evaluation, the cluster analysis showed two groups of EU member states, according to the ‘traditional’ division of old member states (OMS) and new member states (NMS). Results showed a statistically significant gap in GCI scores between the OMS and NMS. Furthermore, the within-class variability in the OMS cluster appears to be higher than in the NMS cluster, which underlines growing disparities among old member states. Most significant differences among both groups prevail in the field of institutions, ICT adoption, business dynamism and innovation. Finally, the link between achieved GCI score and average economic growth has been investigated. Contrary to expectations, more developed member states (OMS) showed, on average, relatively lower economic growth rates over the investigated period than the less developed member states (NMS). The results showed a moderately negative link between the GCI score and economic growth, which suggests that a higher GCI score does not mean achieving higher economic growth, whereas member states with lower total GCI scores could outpace the higher ones in terms of economic growth. In many aspects, the EU still resembles two rails in Europe, and a significant gap between OMS and NMS prevails. Relatively higher economic growth of NMS might help reduce the gap over time. However, it may prove a short-sighted, and significant lags in many crucial factors will stiff the competitiveness in the long term.
Keywords: competitiveness, innovation, cluster analysis, data mining, economic growth
JEL Classification: M21, M38.
Cite as: Maris, M. (2022). Management of Competitiveness in the EU Member States: The Main Strengths and Weaknesses. Marketing and Management of Innovations, 2, 110-120. https://doi.org/10.21272/mmi.2022.2-10
This work is licensed under a Creative Commons Attribution 4.0 International License
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