Big Data Usage in European Countries: Cluster Analysis Approach

Mirjana Pejić Bach, Tine Bertoncel, Maja Meško, Dalia Suša Vugec and Lucija Ivančić
Additional contact information
Mirjana Pejić Bach: Faculty of Economics and Business, University of Zagreb, 10000 Zagreb, Croatia
Tine Bertoncel: Faculty of Organisation Studies, 8000 Novo Mesto, Slovenia
Maja Meško: Faculty of Management, University of Primorska, 6000 Koper, Slovenia
Dalia Suša Vugec: Faculty of Economics and Business, University of Zagreb, 10000 Zagreb, Croatia
Lucija Ivančić: Faculty of Economics and Business, University of Zagreb, 10000 Zagreb, Croatia

Data, 2020, vol. 5, issue 1, 1-16

Abstract: The goal of this research was to investigate the level of digital divide among selected European countries according to the big data usage among their enterprises. For that purpose, we apply the K-means clustering methodology on the Eurostat data about the big data usage in European enterprises. The results indicate that there is a significant difference between selected European countries according to the overall usage of big data in their enterprises. Moreover, the enterprises that use internal experts also used diverse big data sources. Since the usage of diverse big data sources allows enterprises to gather more relevant information about their customers and competitors, this indicates that enterprises with stronger internal big data expertise also have a better chance of building strong competitiveness based on big data utilization. Finally, the substantial differences among the industries were found according to the level of big data usage.

Keywords: big data; cluster analysis; digital divide; k-means; enterprise; industry; Europe; quality (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2306-5729/5/1/25/pdf (application/pdf)
https://www.mdpi.com/2306-5729/5/1/25/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:5:y:2020:i:1:p:25-:d:331445

Access Statistics for this article

Data is currently edited by Ms. Cecilia Yang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().