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Pregled bibliografske jedinice broj: 1054067

Big Data Usage in European Countries: Cluster Analysis Approach


Pejić Bach, Mirjana; Bertoncel, Tine; Meško, Maja; Suša Vugec, Dalia; Ivančić, Lucija
Big Data Usage in European Countries: Cluster Analysis Approach // Data, 5 (2020), 1; 25, 17 doi:10.3390/data5010025 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1054067 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Big Data Usage in European Countries: Cluster Analysis Approach

Autori
Pejić Bach, Mirjana ; Bertoncel, Tine ; Meško, Maja ; Suša Vugec, Dalia ; Ivančić, Lucija

Izvornik
Data (2306-5729) 5 (2020), 1; 25, 17

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
big data ; cluster analysis ; digital divide ; k-means ; enterprise ; industry ; Europe ; quality

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Ekonomija, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Projekti:
IP-2014-09-3729 - Procesna i poslovna intelilgencija za poslovnu izvrsnost (PROSPER) (Bosilj Vukšić, Vesna, HRZZ - 2014-09) ( CroRIS)

Ustanove:
Ekonomski fakultet, Zagreb

Poveznice na cjeloviti tekst rada:

doi www.mdpi.com

Citiraj ovu publikaciju:

Pejić Bach, Mirjana; Bertoncel, Tine; Meško, Maja; Suša Vugec, Dalia; Ivančić, Lucija
Big Data Usage in European Countries: Cluster Analysis Approach // Data, 5 (2020), 1; 25, 17 doi:10.3390/data5010025 (međunarodna recenzija, članak, znanstveni)
Pejić Bach, M., Bertoncel, T., Meško, M., Suša Vugec, D. & Ivančić, L. (2020) Big Data Usage in European Countries: Cluster Analysis Approach. Data, 5 (1), 25, 17 doi:10.3390/data5010025.
@article{article, author = {Peji\'{c} Bach, Mirjana and Bertoncel, Tine and Me\v{s}ko, Maja and Su\v{s}a Vugec, Dalia and Ivan\v{c}i\'{c}, Lucija}, year = {2020}, pages = {17}, DOI = {10.3390/data5010025}, chapter = {25}, keywords = {big data, cluster analysis, digital divide, k-means, enterprise, industry, Europe, quality}, journal = {Data}, doi = {10.3390/data5010025}, volume = {5}, number = {1}, issn = {2306-5729}, title = {Big Data Usage in European Countries: Cluster Analysis Approach}, keyword = {big data, cluster analysis, digital divide, k-means, enterprise, industry, Europe, quality}, chapternumber = {25} }
@article{article, author = {Peji\'{c} Bach, Mirjana and Bertoncel, Tine and Me\v{s}ko, Maja and Su\v{s}a Vugec, Dalia and Ivan\v{c}i\'{c}, Lucija}, year = {2020}, pages = {17}, DOI = {10.3390/data5010025}, chapter = {25}, keywords = {big data, cluster analysis, digital divide, k-means, enterprise, industry, Europe, quality}, journal = {Data}, doi = {10.3390/data5010025}, volume = {5}, number = {1}, issn = {2306-5729}, title = {Big Data Usage in European Countries: Cluster Analysis Approach}, keyword = {big data, cluster analysis, digital divide, k-means, enterprise, industry, Europe, quality}, chapternumber = {25} }

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Emerging Sources Citation Index (ESCI)
  • Scopus


Citati:





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