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Big Data Usage in European Countries: Cluster Analysis Approach

Author

Listed:
  • 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
    Faculty of Organizational Sciences, University of Maribor, 4000 Kranj, 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)

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.

Suggested Citation

  • Mirjana Pejić Bach & Tine Bertoncel & Maja Meško & Dalia Suša Vugec & Lucija Ivančić, 2020. "Big Data Usage in European Countries: Cluster Analysis Approach," Data, MDPI, vol. 5(1), pages 1-16, March.
    • Handle: RePEc:gam:jdataj:v:5:y:2020:i:1:p:25-:d:331445
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    References listed on IDEAS

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    1. Marko Sarstedt & Erik Mooi, 2014. "The Market Research Process," Springer Texts in Business and Economics, in: A Concise Guide to Market Research, edition 2, chapter 2, pages 11-23, Springer.
    2. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    3. Gandomi, Amir & Haider, Murtaza, 2015. "Beyond the hype: Big data concepts, methods, and analytics," International Journal of Information Management, Elsevier, vol. 35(2), pages 137-144.
    4. Frederico Cruz-Jesus & Tiago Oliveira & Fernando Bacao & Zahir Irani, 2017. "Assessing the pattern between economic and digital development of countries," Information Systems Frontiers, Springer, vol. 19(4), pages 835-854, August.
    5. Marko Sarstedt & Erik Mooi, 2014. "A Concise Guide to Market Research," Springer Texts in Business and Economics, Springer, edition 2, number 978-3-642-53965-7, October.
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    Cited by:

    1. Mydyti Hyrmet & Kadriu Arbana & Pejic Bach Mirjana, 2023. "Using Data Mining to Improve Decision-Making: Case Study of A Recommendation System Development," Organizacija, Sciendo, vol. 56(2), pages 138-154, May.
    2. Mirjana Pejić Bach & Božidar Jaković & Ivan Jajić & Maja Meško, 2023. "Investigating the Impact of COVID-19 on E-Learning: Country Development and COVID-19 Response," Mathematics, MDPI, vol. 11(6), pages 1-21, March.

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