The Impact of Socio-Economic Factors on Digital Skills in the Population of the EU 27 Countries

Authors

DOI:

https://doi.org/10.34021/ve.2024.07.03(5)

Keywords:

digital skills, socio-economic factors, education and training programs

Abstract

This research investigates digital skills across the 27 EU countries, examining how incentive and disincentive factors shape these competencies, particularly under varying socio-economic conditions. Using a quantitative methodology, the study applies cluster analysis and linear ordering methods to classify countries by digital skills indicators, utilising data from the Eurostat Digital Economy and Society database. Key analytical methods—including Hellwig’s method, order counting, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)—are employed to pinpoint crucial factors that stimulate digital competencies, such as investment in research, training programmes, and innovation. Findings indicate that countries within clusters featuring higher values in these stimulative factors tend to adopt a proactive approach to digital development. These clusters frequently correlate with substantial investments in skills training and comprehensive educational policies. Conversely, clusters characterised by high disincentive variables, such as limited funding and socio-economic disparities, show slower progress in digital skills development, highlighting barriers in educational and social inclusion systems. The results reveal marked spatial disparities across the EU. Leading countries typically have robust education systems promoting lifelong learning and inclusivity, while lagging countries face structural challenges, including centralised education systems and urban-rural divides. This disparity underscores the need for sustained investment in education, training, and policy support to enhance workforce digital skills. The study highlights that digital competencies are closely intertwined with each country's educational and socio-economic frameworks, recommending targeted interventions to foster lifelong digital literacy and reduce skills gaps. As the digital landscape continues to evolve, interdisciplinary research is essential for addressing both quantitative and qualitative aspects of digital skill development. Future studies should aim to bridge the digital divide through comprehensive strategies for upskilling populations, ensuring inclusive digital integration across all EU countries.

Downloads

Download data is not yet available.

References

Ferrari, A. (2013). DIGCOMP: A framework for developing and understanding digital competence in Europe (Y. Punie & B. Brecko, Eds.). Publications Office of the European Union. https://doi.org/10.2788/52966.

European Commission. (2021). The future of education for digital skills. EIT Digital. https://www.eitdigital.eu.

European Commission. (2021). 2030 Digital Compass: The European way for the digital decade. https://eufordigital.eu/wp-content/uploads/2021/03/2030-Digital-Compass-the-European-way-for-the-Digital-Decade.pdf.

van Dijk, J. (2006). Digital divide research, achievements, and shortcomings. Poetics, 34(4–5), 221–235. https://doi.org/10.1016/j.poetic.2006.05.004.

European Commission. (2018). Digital Europe Programme. https://commission.europa.eu/funding-tenders/find-funding/eu-funding-programmes/digital-europe-programme_en.

Armitage, R., & Nellums, L. B. (2020, May). COVID-19 and the consequences of isolating the elderly. The Lancet Public Health, 5(5), Article e256. https://doi.org/10.1016/S2468-2667(20)30061-X.

Vyshnevskyi, O., Liashenko, V., & Amosha, O. (2019). The impact of Industry 4.0 and AI on economic growth. Scientific Papers of Silesian University of Technology. Organization and Management Series, 140, 391–400.

Ruiu, M. L., & Ragnedda, M. (2020). Digital capital and online activities: An empirical analysis of the second level of digital divide. First Monday, 25(7). https://doi.org/10.5210/fm.v25i7.10855.

Lloyds Bank. (2020). The essential digital skills of the UK population, 15+: UK Consumer Digital Index 2020. Ipsos. https://www.ipsos.com/sites/default/files/ct/publication/documents/2023-03/230310-lloyds-uk-essential-digital-skills-for-work.pdf.

van Dijk, J. (2020). The digital divide. Polity Press.

Bol, T., Witschge, T., Van Engen, M. L., & De Koster, W. (2018). "Turn it off!" Social influence on excessive media use. Computers in Human Behavior, 84, 437–446. https://doi.org/10.1016/j.chb.2018.03.048.

van Deursen, A., & van Dijk, J. A. G. M. (2021). Rethinking Internet skills: The contribution of gender, age, education, Internet experience, and hours online to medium- and content-related Internet skills. Poetics, 39(2).

Balaji, B., & Rames, L. (2020). The socio-economic variables – Impact on digital literacy: A literature perspective approach. Journal of Critical Reviews, 7(15), 1478-1481.

Scherer, R., & Siddiq, F. (2019). The relation between students’ socioeconomic status and ICT literacy: Findings from a meta-analysis. Computers & Education, 138, 13-32. https://doi.org/10.1016/j.compedu.2019.04.011.

Fox, S. (2016). An equitable education in the digital age: Providing internet access to students of poverty. Journal of Education & Social Policy, 3(3), 12-20.

Institute for Statistics. (2009). Guide to measuring information and communication technologies (ICT) in education. UNESCO. https://uis.unesco.org/sites/default/files/documents/guide-to-measuring-information-and-communication-technologies-ict-in-education-en_0.pdf.

International Telecommunication Union. (2018). Measuring the information society report 2018: Volume 2.

Stankovičová, I., & Vojtková, M. (2007). Viacrozmerné štatistické metódy s aplikáciami. Iura Edition.

Hellwig, Z. (1968). Zastosowanie metody taksonomicznej do typologicznego podziału krajów ze względu na poziom ich rozwoju oraz zasoby i strukturę wykwalifikowanych kadr. Przegląd Statystyczny, 15(4), 307-327.

Wysocki, F. (2010). The methods of taxonomy for recognition of economic types in agriculture and rural areas. University Publisher Poznan University of Life Sciences.

Sompolska-Rzechuła, A. (2021). Selection of the optimal way of linear ordering of objects: Case of sustainable development in EU countries. Statistika, 101(1), 24-36.

Roszko-Wójtowicz, E., & Grzelak, M. (2019). The use of selected methods of linear ordering to assess the innovation performance of the European Union member states. Economic and Environmental Studies, 19(1), 9-30. https://doi.org/10.25167/ees.2019.49.1.

Chakraborty, S. (2022). TOPSIS and modified TOPSIS: A comparative analysis. Decision Analytics Journal, 2, 1-7. https://doi.org/10.1016/j.dajour.2021.100021.

Nascimento, C. R. S. M. S., Almeida-Filho, A. T., & Palha, R. P. (2023). A TOPSIS-based decision model to establish priorities for sequencing the design of construction projects in the public sector. Mathematical Problems in Engineering, 2023, 1-13. https://doi.org/10.1155/2023/1414294.

Mazurchenko, A., & Maršíková, K. (2019). Digitally-Powered Human Resource Management: Skills and Roles in the Digital Era. Acta Informatica Pragensia, 8(2), 72-87. https://doi.org/10.18267/j.aip.125.

Zhang, J., & Chen, Z. (2023). Exploring Human Resource Management Digital Transformation in the Digital Age. Journal of the Knowledge Economy, 1–17. https://doi.org/10.1007/s13132-023-01214-y.

Alemayehu Tegegn, D. (2024). The role of science and technology in reconstructing human social history: effect of technology change on society. Cogent Social Sciences, 10(1). https://doi.org/10.1080/23311886.2024.2356916.

Anacka, H., & Lechman, E. (2023). Digitalization and digital skills development patterns: Evidence for European countries. In The European Digital Economy (1st ed., pp. 101-119). Routledge. https://doi.org/10.4324/9781003450160-9.

Al-Kubaisi, H. (2022). Centralize or decentralize? The question currently facing schools in Qatar. International Journal of Learning, Teaching and Educational Research, 21, 397–418. https://doi.org/10.26803/ijlter.21.2.22.

Ciarli, T. (2021). Digital technologies and skills: Integration of complementary physical, intangible, and computational technologies. Berkeley Roundtable on the International Economy (BRIE). https://brie.berkeley.edu/sites/files/publications.

Hu, W. (2021). Toward the development of key STEM competencies to meet personal and social development needs. Frontiers. https://www.frontiersin.org/full.

Zhan, Z., Li, Y., Mei, H., & Lyu, S. (2023). Key competencies acquired from STEM education: Gender-differentiated parental expectations. Humanities and Social Sciences Communications, 10, Article 464. https://doi.org/10.1057/s41599-023-01946-x.

Tian, L., & Xiang, Y. (2024). Does the digital economy promote or inhibit income inequality? Heliyon, 10(14), Article e33533. https://doi.org/10.1016/j.heliyon.2024.e33533.

Zielińska, A. (2022). Developing digital skills: Results of a social innovation project in the technology sector. European Research Studies Journal, 25(2), 684–701. https://doi.org/10.35808/ersj/2989.

Chen, P., & Kim, S. (2023). The impact of digital transformation on innovation performance: The mediating role of innovation factors. Heliyon, 9(3), Article e13916. https://doi.org/10.1016/j.heliyon.2023.e13916.

Bryda, G., & Costa, A. P. (2023). Qualitative research in the digital era: Innovations, methodologies, and collaborations. Social Sciences, 12(10), 570. https://doi.org/10.3390/socsci12100570.

Downloads

Published

2024-09-30

How to Cite

Labudova, V., & Fodranova, I. (2024). The Impact of Socio-Economic Factors on Digital Skills in the Population of the EU 27 Countries. Virtual Economics, 7(3), 81–101. https://doi.org/10.34021/ve.2024.07.03(5)

Issue

Section

Articles