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1.
Big data usage in European Countries : cluster analysis approach
Mirjana Pejić Bach, Tine Bertoncel, Maja Meško, Daila Suša-Vugec, Lucija Ivančić, 2020, original scientific article

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
Published in DKUM: 14.01.2025; Views: 0; Downloads: 2
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2.
Directions for the sustainability of innovative clustering in a country
Vito Bobek, Vladislav Streltsov, Tatjana Horvat, 2023, original scientific article

Abstract: This paper presents potential improvements through utilizing the cyclical nature of clusters by human capital, technology, policies, and management. A historical review of the formation and sustainable development of clusters in the US, Europe, Japan, China, and other regions is carried out to achieve this. The aim was to identify and assess the prominent occurrence cases, the central institutional actors, the indicators of their innovative activity, and the schematics of successful cluster management. The theory section covers current classification methods and typology of innovation-territorial economic associations. Consequently, a regression analysis model is produced to identify the potential dominant success factors in implementing the innovation policy of the most successful innovative clusters. Comments on the influence of these predictors on the competitiveness and level of innovative development of the 50 inspected countries follow. As a result of qualitative and quantitative analysis, an overview of the best world practice, the new vision, and its priorities are proposed to improve the efficiency at the level of management structures of innovation clusters.
Keywords: cluster, cluster policy, state policy, regression analysis, institutions, innovation, R&D
Published in DKUM: 09.04.2024; Views: 306; Downloads: 127
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3.
Multivariate data analysis of natural mineral waters
Katja Šnuderl, Marjana Simonič, Jan Mocak, Darinka Brodnjak-Vončina, 2007, original scientific article

Abstract: Fifty samples of natural mineral waters from springs in Slovenia, Hungary, Germany, Czech Republic and further countries of former Yugoslavia have been analysed. The mass concentration of cations ($Na^+$, $K^+$, $Ca^{2+}$, $Mg^{2+}$, $Fe^{2+}$, $Mn^{2+}$, $NH^+_4$) and anions ($F^-$, $Cl^-$, $I^-$, $NO^-_3$, $SO_4^{2-}$, $HCO_3^-$), the spring temperature, pH, conductivity and carbon dioxide mass concentration have been measured using standard analytical methods. Appropriate statistical methods and different chemometric tools were used to evaluate the obtained data, namely, (i) descriptive statistics, (ii) principal component analysis (PCA), (iii) cluster analysis, and (iv) linear discriminant analysis (LDA). It was confirmed that Slovenian natural mineral water samples differ most from the German ones but are relatively similar to the Czech and Hungarian ones. Water samples from Hungary are similar to waters from the eastern part of Slovenia.
Keywords: natural mineral water, ion determination, principal component analysis, cluster analysis, linear discriminant analysis
Published in DKUM: 21.12.2015; Views: 1866; Downloads: 101
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4.
Regression analysis of variables describing poultry meat supply in European countries
Miro Simonič, Ksenija Dumičić, Gabrijel Devetak, 2012, original scientific article

Abstract: In this paper, based on the analysis of official FAOSTAT and EUROSTAT data on poultry meat for 38 European countries for years 2007 and 2009, two hypotheses were examined. Firstly, considering four clustering variables on poultry meat, i.e. production, export and import in kg/capita, as well as the producer price in US $/t, using descriptive exploratory and cluster analysis, the hypothesis that the clusters of countries may be recognized was confirmed. As a result six clusters of similar countries were distinguished. Secondly, based on multiple regression analysis, this paper proofs that there exists the statistically significant relationship of poultry meat production on export and import of that kind of meat, all measured in kg/capita. There is also a high correlation between production, as a dependent, and each of two independent variables.
Keywords: poultry meat, marketing strategy, cluster analysis, correlation, multiple regression
Published in DKUM: 10.07.2015; Views: 1544; Downloads: 410
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