Suchergebnisse

Starting from the fact that the rapid spread of the SARS-CoV-2 virus and the implementation of social distancing strategies have dramatically affected all aspects of human lives at global, national, and micro levels, this paper focuses on examining the impact of the COVID-19 pandemic on the economic performance of selected European countries. To perceive and understand this impact, the authors applied a complex research methodology based on the combined application of suitable univariate and multivariate methods of statistical analysis. The classification of 40 European countries into different groups, in terms of the selected set of COVID-19 indicators in 2020, was performed through hierarchical agglomerative cluster analysis, while statistical evaluation of the quality of the obtained solution of a non-hierarchical procedure, based on the k-means method, was implemented. The classification consists of four clusters of countries identified as the "optimal" clustering solution. The authors conducted the analysis and comparison of profiles of the formed clusters of countries in terms of their average GDP growth rates in 2020 using the statistical methods of descriptive analysis and hypothesis testing. This study reveals that a cluster of countries with a relatively "lower" severity of the COVID-19 health consequences recorded a higher average GDP growth rate compared to groups of countries that suffered more serious consequences and vice versa. The obtained results, which indicate the connection between the magnitude of the negative health and economic consequences of the COVID-19 pandemic, can serve as additional support to policymakers in making decisions aimed at mitigating pandemic impacts and crisis management.

Polazeći od činjenice da su brzo širenje virusa SARS-CoV-2 i provedba strategija socijalnog distanciranja dramatično utjecali na sve aspekte života ljudi na globalnoj, nacionalnoj i mikro razini, ovaj rad se fokusira na ispitivanje utjecaja pandemije COVID-19 na gospodarski učinak odabranih europskih zemalja. Kako bi se uočio i razumio ovaj utjecaj, korištena je složena metodologija istraživanja koja se temelji na kombiniranoj primjeni prikladnih univarijatnih i multivarijatnih metoda statističke analize. Klasifikacija 40 europskih zemalja u različite grupe u pogledu odabranog seta COVID-19 pokazatelja u 2020. godini izvršena je primjenom hijerarhijske aglomerativne klaster analize, dok je za statističku ocjenu kvalitete dobivenog rješenja korišten ne-hijerarhijski postupak baziran na k-means metodi. Klasifikacija koja se sastoji od četiri klastera zemalja identificirana je kao "optimalno" rješenje. Analiza i usporedba profila formiranih klastera zemalja s obzirom na njihove prosječne stope rasta BDP-a u 2020. godini provodi se statističkim metodama deskriptivne analize i testiranja hipoteza. Provedeno istraživanje otkriva da je klaster zemalja s relativno "manjom" ozbiljnošću COVID-19 zdravstvenih posljedica zabilježio višu prosječnu stopu rasta BDP-a u usporedbi s klasterima zemalja koje su pretrpjele teže posljedice, i obratno. Dobiveni rezultati koji upućuju na povezanost veličine negativnih zdravstvenih i gospodarskih posljedica COVID-19 pandemije mogu poslužiti kao dodatna potpora kreatorima politike u donošenju odluka usmjerenih na ublažavanje posljedica pandemije i suzbijanje krize. ; Starting from the fact that the rapid spread of the SARS-CoV-2 virus and the implementation of social distancing strategies have dramatically affected all aspects of human lives at global, national, and micro levels, this paper focuses on examining the impact of the COVID-19 pandemic on the economic performance of selected European countries. To perceive and understand this impact, the authors applied a complex research ...

Technological advancement across human activities has brought about accelerated generation of huge amounts of data. Consequently, researchers are faced with the problem how to determine adequate ways of turning the available data mass into useful knowledge. Data analysis adapted to these changes when data mining was developed as an approach to data analysis from different perspectives which reveals significant hidden regularities. This paper presents conceptual characteristics of decision tree, an important data mining method which is, due to its explorative nature, exceptionally suitable for detection of data structure when analysing various problem situations. The empirical section of the paper demonstrates applicative characteristics of this method using CHAID algorithm in leadership studies: an interdependence of selected personal characteristics and the manager's leadership style has been investigated. The aim of the paper is to develop a classification model for identification of the dominant leadership style. The study was conducted on the sample of 417 managers of privately owned small-sized enterprises in Serbia, using a specially designed questionnaire. The classification model identified the set of six statistically significant personal characteristics as predictors of dominant leadership style.

Starting from the fact that pronounced differences in the level of development of regions within a particular country can have a serious and significant (negative) impact on its socio-political stability, as well as the performance of the national economy as a whole, it is very important to create conditions for ensuring balanced and sustainable regional development. Due to its pronounced multidimensional nature, the analysis of regional economic disparities is a very complex and statistically demanding task. In this paper, a multivariate methodological framework for the classification of districts in Serbia according to the achieved level of economic development, into internally-homogeneous / externally-heterogeneous groups, based primarily on the application of hierarchical agglomerative clustering procedure and examination of interdependencies between five selected relevant economic indicators, is presented. The statistical validity of the obtained "optimal" classification of districts is additionally tested and confirmed with the results of one-factor multivariate analysis of variance. The resulting categorization clearly and unequivocally confirms the presence of pronounced inequalities regarding the achieved level of economic development between NUTS 3 level territorial units in Serbia, and the existence of regional economic polarization, primarily in direction "developed north – undeveloped south".

Abstract
The research focus on the analysis of interdependence of economic and ICT development results from the fact that, in recent decades, ICTs have become a dominant factor in generating socio-economic prosperity of countries. ICT expansion is a stimulus for economic growth, and vice versa. In order to empirically verify the observed interdependence, on the example of the selected 37 European countries, a two-stage statistical analysis was conducted, based on a combined application of cluster analysis and a oneway analysis of variance. Using specific data from 2017, the following was conducted: (a) formation of groups of countries by implementing cluster analysis based on the three structural components of the ICT Development Index; and (b) by applying the ANOVA method and using the values of GDP per capita, testing the validity of assumption regarding the existence of statistically significant differences, in terms of the achieved level of economic development between the formed groups of countries of different levels of ICT development. The obtained results suggest the extraction of three clusters of countries and the acceptance of the defined hypothesis. Serbia is classified within the cluster of medium ICT development level and is characterized by the value of GDP per capita that is significantly below the group average, which indicates the possibility and the need for improvement in the ICT field.

The objective of this study is to explore the role and relevance of leaders' gender in their choice of dominant leadership style in Serbian companies and institutions. The categories we use in our research are based on the two most popular classifications of leadership styles, established in Iowa and Ohio studies (which identified authoritarian, democratic and laissez-faire style and task-oriented or relationship-oriented leadership style respectively). For data collection we used a 31-statement questionnaire, applying the method of proportional stratified random sampling. The sample contained responses of 79 randomly selected respondents (leaders) from randomly selected organizations in Serbia. To determine whether there is statistically significant dependence between the identified dominant leadership styles and leaders' gender we applied the Chi-square test of independence as a quantitative statistical method. Our results confirmed statistically significant interdependence between the observed pairs of categorical variables. More precisely, they indicated that female leaders incline towards task-oriented and authoritarian leadership styles, while male leaders prefer relationship-oriented and democratic styles.