Ksenija Dumičić

Statistical Methods Use in Small Enterprises: Relation to Performance

Abstract Background: There are several factors that lead to an improved level of competitiveness and increased net income of enterprises. Previous studies have shown that an appropriate use of statistical methods has positive effects on the level of competitiveness and on enterprises’ net income in general. Objectives: This study analyses the statistical methods use in Croatian small enterprises. The goal of this research is to establish whether most Croatian small enterprises use statistical methods and whether the enterprises that use statistical methods on average have greater net income than the enterprises that do not use statistical methods. Methods/Approach: A web survey was conducted on the sample of 631 small enterprises in Croatia in 2012. In the analysis a complex survey design was taken into account. Results: The results have shown that in most Croatian small enterprises statistical methods are not used. The enterprises that use statistical methods on average have greater net income than the enterprises that do not use them. Conclusions: The results suggest that even though the use of statistical methods in small enterprises leads to higher net income, they are not used in the majority of Croatian small enterprises. If Croatian small enterprises want to succeed on the demanding European Union’s market they should consider using statistical methods in their business.

A case study of the Croatian beer market structure and performances

This paper considers the structure and performances of the Croatian beer market, as well as consumers’ attitudes and preferences. The results emphasise that the domestic brands with their quality and tradition, in spite of the imported beer brands’ influence, are the dominant brands on the Croatian beer market.

Exploratory study of insurance companies in selected post-transition countries: Non-hierarchical cluster analysis

This paper focuses on the analysis of business practice of insurance companies in selected post-transition European countries. Specifically, it covers Croatian, Slovenian, Hungarian and Polish insurance markets in the year 2014 comprising the total of 119 insurance companies. Employing the non-hierarchical cluster analysis by applying the k-means approach, insurance companies are segmented into seven groups using various variables such as ROE, the share of premium ceded to reinsurance, the number of years operating in the insurance market, leverage, gross premium written and the share of life insurance premium in the total premium. Furthermore, these seven clusters have been grouped according to the country of origin, ownership and the type of insurance companies. Results indicate that specific groups of insurance companies in these countries share common characteristics, which are not based solely on the country of origin and the type of insurance.

Tourism spending behaviour before and after the 2008 financial crisis A dynamic panel investigation

This research note analyses the long-run and short-run relationships between tourism expenditure, income and transportation costs based on quarterly data for European countries spanning the period from 1996Q1 to 2014Q1. As the first macroeconomic study to explore how tourist spending behaviour potentially reacted to the 2008 financial crisis, this article uses the heterogeneous panel data technique of Pesaran et al. The results of this pilot study reveal interesting differences in the long-run relationships between the examined variables before and after 2008Q3. Since this study uses a rather simple expenditure model, future studies might extend their focus to other demand variables to obtain a better understanding of changed tourist spending behaviour induced by the crisis.

Measuring recent changes of insurance gross premiums distribution using ten inequality measures: case study of Croatia

Abstract This paper investigates the extent of gross premium distributions inequality for life and non-life insurance in Croatia in the period 2000–2012. The number of companies for life insurance was the same in 2000 and 2012, at 17. The number of non-life insurance companies varied between 16 and 24, and was 20 at the end of 2012. The Lorenz curve and nine numerical concentration measures applied mostly show a slight decrease of a moderate concentration for both types of insurance over time, with only the Rosenbluth index being an exception, showing a mild increase of concentration. The concentration of gross premium is slightly stronger for non-life then for life insurance. The standard errors of the trend models for the entropy indexes are smaller, and coefficients of determination are higher for the quadratic compared to the linear trend models for both types of insurances, life and non-life. Since the forecasts’ quality measures for both types of insurance show that the quadratic trend is superior compared to the simple linear trend model, it was used for short-term forecasting of the entropy indexes. The quadratic trend model gave forecasts showing digressive-progressive dynamics for non-life, and progressive-digressive for life insurance entropy indexes.

The panel VAR approach to modelling the housing wealth effect : evidence from selected European post-transition economies

Abstract Following Friedman’s permanent income hypothesis and Ando and Modigliani’s lifecycle hypothesis, this paper empirically studies the role of house prices and income in determining the dynamic behaviour of consumption in selected European post-transition economies using the panel vector autoregression (PVAR) approach and quarterly data covering the period from the first quarter of 2002 until the second quarter of 2012. With the shocks being recognized using the customary recursive identification scheme, we found that the response of personal consumption to the housing wealth shock is initially positive, but short lived.

Multivariate analysis of determinants of Internet banking use in European Union countries

The main objective of the paper is to analyse the determinants of Internet banking use in European Union countries based on EUROSTAT data for 2011. The considered determinants of Internet banking use are selected in line with relevant theoretical and empirical research and submitted to factor analysis which yielded two factors. The first extracted factor comprises Information and Communication Technology (ICT) infrastructure, i.e. broadband penetration rate and level of Internet access. The second factor comprises individuals’ level of Internet skills and GDP per capita. On the basis of the two mentioned factors, European Union countries are grouped into two clusters using cluster analysis. In order to analyse the impact of selected variables, a multiple regression analysis of Internet banking use is performed using extracted factors as independent variables. The regression analysis shows that an increase in ICT infrastructure leads to an increase in Internet banking use.

Business Client Segmentation in Banking Using Self-Organizing Maps

Abstract Segmentation in banking for the business client market is traditionally based on size measured in terms of income and the number of employees, and on statistical clustering methods (e.g. hierarchical clustering, k-means). The goal of the paper is to demonstrate that self-organizing maps (SOM) effectively extend the pool of possible criteria for segmentation of the business client market with more relevant criteria, including behavioral, demographic, personal, operational, situational, and cross-selling products. In order to attain the goal of the paper, the dataset on business clients of several banks in Croatia, which, besides size, incorporates a number of different criteria, is analyzed using the SOM-Ward clustering algorithm of Viscovery SOMine software. The SOM-Ward algorithm extracted three segments that differ with respect to the attributes of foreign trade operations (import/export), annual income, origin of capital, important bank selection criteria, views on the loan selection and the industry. The analyzed segments can be used by banks for deciding on the direction of further marketing activities.

Statistical Analysis of the Development Indicators’ Impacts on E-Commerce of Individuals in Selected European Countries

Abstract The aim of this paper is to analyse the influence of the development level indicators on the e-commerce, i.e. on the online purchase by individuals, in selected European countries in 2013. In the analysis, the main variable under study and all the independent variables are included as standardised. Based on nine variables, the principal component analysis with varimax rotation was performed and the two extracted factors were used as the regressors in the multiple regression analysis. In the regression model both components, Factor 1, which includes seven variables, called Prosperity, Investing in Education and IT Infrastructure, and Awareness, and Factor 2, comprised of two variables, called IT Skills, are statistically significant at the significance level of 1%. Both factors show a positive correlation with the online purchase of individuals. Inclusion and analysis of distributions and impacts of even nine independent variables, which make up two distinct factors affecting the e-commerce, make a new contribution of this work.

Exploring impact of economic cost of violence on internationalization: Cluster analysis approach

Companies have to deal with many different challenges in their environment every day. In this article, focus is given to inspecting the impact of the economic costs of violence on 119 countries worldwide. In the analysis, the CAGE model, which consists of four complex domains: Cultural, Administrative, Geographic, and Economic differences, is used to group countries according to their development level and the level of violence costs. However, in the clustering analysis, except the CAGE variable, 10 additional variables describing development level and the economic costs of violence are used as well. According to the conducted nonhierarchical cluster analysis, in which a k-means approach was used, six groups of countries are recognized. Most countries can be found in the cluster of developing countries with low economic costs of violence. So, the results pointed out that, despite the low economic costs of violence, a large number of countries are still struggling with other key environmental challenges that prevent them from achieving their full economic development potential.