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This special issue of Business Systems Research (SI of the BSR) is being co-published by the Slovenian Society INFORMATIKA – Section for Operational Research (SSI -SOR). It focuses on recent advances in Operations Research and Management Science (OR / MS), with a particular emphasis on linking OR / MS with other areas of quantitative and qualitative methods in the context of a multidisciplinary framework. The ten papers that were chosen for this Special Issue of the BSR present advancements and new techniques (methodology) in the field of Operations Research (OR), as well as their application in a variety of fields, including risk management, mathematical programming, game theory, gravity, spatial analysis, logistics, circular economy, continuous improvement, sustainability, e-commerce, forecasting, Gaussian processes, linear regression, multi-layer perceptron, and machine learning.

Purpose This paper aims to explore and analyse stakeholders’ perceptions of the development priorities and suggests more effective strategies to assist sustainable economic growth in the United Arab Emirates (UAE). Design/methodology/approach The authors use the World Bank data set, which collects various stakeholders’ opinions on the UAE development. First, the exploratory factor analysis has been applied to detect the main groups of development priorities. Second, the fuzzy cluster analysis has been conducted to detect the groups of stakeholders with different attitudes towards the importance of extracted groups of priorities. Third, clusters have been compared according to demographics, media usage and shared prosperity goals. Findings The two main groups of development priorities have been extracted by the exploratory factor analysis: economic priorities and sustainability priorities. Four clusters have been detected according to the level of motivation when it comes to the economic and sustainability priorities: Cluster 1 (High economic – High sustainability), Cluster 2 (High economic – Medium sustainability), Cluster 3 (High economic – Low sustainability) and Cluster 4 (Low economic – Low sustainability). Members of the cluster that prefer a high level of economic and sustainability priorities (Cluster 1) also prefer more diversified economic growth providing better employment opportunities and better education and training for young people in the UAE. Research limitations/implications Limitations stem from the survey being conducted on a relatively small sample using the data collected by the World Bank; however, this data set allowed a comparison of various stakeholders. Future research should consider a broader sample approach, e.g. exploring and comparing all of the Gulf Cooperation Council (GCC) countries; investigating the opinions of the expatriate managers living in the UAE that are not from GCC countries; and/or including other various groups that are lagging, such as female entrepreneurs. Practical implications Several practical implications were identified regarding education and media coverage. Since respondents prioritize the economic development factors over sustainability factors, a media campaign could be developed and executed to increase sustainability awareness. A campaign could target especially male citizens since the analysis indicates that males are more likely to affirm high economic and low sustainability priorities than females. There is no need for further diversification of media campaigns according to age since the analysis did not reveal relevant differences in age groups, implying there is no inter-generational gap between respondents. Originality/value This paper contributes to the literature by comparing the perceived importance of various development goals in the UAE, such as development priorities and shared prosperity indicators. The fuzzy cluster analysis has been used as a novel approach to detect the relevant groups of stakeholders in the UAE and their developmental priorities. The issue of media usage and demographic characteristics in this context has also been discussed.

Background: Social supermarkets were developed in Europe after the economic crisis 2008-2014. Their purpose is to decrease food waste that occurs in traditional food supply chains and to ensure access to food to socially endangered citizens. Objectives: This paper analyses the general perception of consumers regarding the mission and purpose of social supermarkets in four Central Eastern European (CEE) countries: Croatia, Poland, Lithuania, and Serbia. Methods/Approach: The paper brings the results of the survey research conducted in the observed CEE countries measuring attitudes towards the relevance and the role of social supermarkets. Results: There is a positive attitude regarding the existence of social supermarkets in all the analysed CEE countries. Less than 10% of respondents claim that there is no need for such organizations. In Croatia, Lithuania, and Poland examinees claim that reduction of food waste rather than reduction of poverty should be emphasized as a mission of social supermarkets. Conclusions: Social supermarkets require improvement of a legal framework, welfare system integration, and implementation of state monitoring. Moreover, larger involvement of religious communities, national and local governments, as supporting institutions is observed as a necessity in all the countries.

Effective business decisions have a positive impact on the overall business of a company. Each business decision should be based on relevant, high-quality, and reliable information prepared by a management accountant in accordance with managers' specific requirements. In order to be support the efficient decision-making process, the management accounting information should be appropriate for a particular business decision, and it should reflect the role, responsibility, and value it has for a manager that participates in the decision-making. This paper aims to investigate whether the management accounting information system is developed in Croatian companies. In order to collect data for the analysis, a web survey was conducted on a representative sample of Croatian companies. Overall, 225 companies from the real sector participated in the survey. The results have shown that managers consider management reports an important basis in decision-making. Furthermore, the findings of this study have shown that managers in large companies use management reports to a greater extent in decision-making than managers in micro, small, and medium-sized companies do.

We are living in turbulent times. The spread of COVID-19 disease has affected all aspects of our life but the most important is to prevent human suffering and save lives. COVID-19 represents not only public health emergency but has become a global economic problem. It has affected all economic sectors threatening global poverty. The sectors that have been hardest hit with the global pandemics are sports and entertainment industry, travel industry, hospitality industry, oil industry and financial markets and others. The important question that arises is what catalyses the spread of the disease? In this paper the relationship between population density and spread of COVID-19 is observed which is goal of the paper. For the purpose of the analysis the correlation between the population variables (population and population density) and COVID-19 variables (total cases, deaths and total tests) on a global country level (209 countries) and regional level of individual countries with the most cases of infection (the United States of America, Spain, Italy, France, Germany and the United Kingdom) is observed. In addition, the case of Croatia will be also analysed. The results have shown that on a country level variable population is statistically significant in all regression models for total cases, deaths and total tests variables whereas variable population density was not. This conclusion was also valid when the USA states, Spain and German regions were observed. On the other side, in the case of Italian, French and United Kingdom’s regions the results have shown that, not only variable population, but variable population density is statistically significant in all regression models meaning that the disease will spread faster in a more populated countries or areas. Limitations of the paper are related to unavailability of data for all COVID-19 variables for some countries. The research results from this paper can be important and relevant for economic and health policy makers to guide Covid-19 surveillance and public health decision-making.

An accurate weather forecast is the basis for the valuation of weather derivatives, securities that partially compensate for financial losses to holders in case of, from their perspective, adverse outside temperature. The paper analyses precision of two forecast models of average daily temperature, the Ornstein-Uhlenbeck process (O-U process) and the generalized autoregressive conditional heteroskedastic model (GARCH model) and presumes for the GARCH model to be the more accurate one. Temperature data for the period 2000-2017 were taken from the DHMZ database for the Maksimir station and used as the basis for the 2018 forecast. Forecasted values were compared to the available actual data for 2018 using MAPE and RMSE methods. The GARCH model provides more accurate forecasts than the O-U process by both methods. RMSE stands at 3.75 °C versus 4.53 °C for the O-U process and MAPE is 140.66 % versus 144.55 %. Artificial intelligence and supercomputers can be used for possible improvements in forecasting accuracy to allow for additional data to be included in the forecasting process, such as up-to-date temperatures and more complex calculations.

The aging of European societies is reshaping their population pyramids. The increase in life expectancy and the decrease in the fertility rate lead to an increasing share of the elderly population. This leads to rising age-related expenditures, especially public pension expenditures to GDP. Consequently, economies are reforming their pension systems to make them more sustainable. Next to the aging-related challenges, the new EU members, eleven post-socialist economies: Bulgaria, Croatia, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, and Slovenia share a similar history of restructuring of their pension systems and establishment of a multi-pillar system. The objective of this article is to examine the similarities and differences between the pension systems of the selected post-transition economies of the European Union to establish the basis for further research, simulations, and assumptions on the impact of future pension reforms. For that purpose, we apply Ward’s clustering methodology on three variable groups in three selected years: 1996, 2006, and 2016. The idea of clustering economies in three years with a 10-year gap is relevant since it reveals how the cluster structure is changing over time. Additionally, three periods represent three different phases in the pension systems’ development. Three groups of variables were used for cluster analysis. First, pension systems’ characteristics include average effective retirement age, pension expenditure, and replacement rate. Second, demographics encompass fertility rate, life expectancy at the age of 65, net migration rate, and old-age dependency ratio. Third, the macroeconomics and labour market variables refer to the GDP growth rate, real labour productivity, labour force participation rate, and the unemployment rate. Results of cluster analysis show that the composition of the countries in the extracted clusters changes significantly, both throughout the observed period and when looking at different variable groups. Our results revealed that the pension systems of economies with later retirement proved to be more sustainable, during the three observed periods, due to lower pension expenditures that are in turn positively reflected in the favourable economic conditions and their labour market.

Data mining is an efficient methodology for uncovering and extracting information from large databases, which is widely used in different areas, e.g., customer relation management, financial fraud detection, healthcare management, and manufacturing. Data mining has been successfully used in various fraud detection and prevention areas, such as credit card fraud, taxation fraud, and fund transfer fraud. However, there are insufficient researches about the usage of data mining for fraud related to internal control. In order to increase awareness of data mining usefulness in internal control, we developed a case study in a project-based organization. We analyze the dataset about working-hour claims for projects, using two data mining techniques: chi-square automatic interaction detection (CHAID) decision tree and link analysis, in order to describe characteristics of fraudulent working-hour claims and to develop a model for automatic detection of potentially fraudulent ones. Results indicate that the following characteristics of the suspected working-hours claim were the most significant: sector of the customer, origin and level of expertise of the consultant, and cost of the consulting services. Our research contributes to the area of internal control supported by data mining, with the goal to prevent fraudulent working-hour claims in project-based organizations.

The aim of this study is to analyse the variations and changes in business demography and macroeconomic features for 28 European countries in 2012 and 29 European countries in 2016 as well as the relations among these features based on the following variables: enterprises birth rates, enterprises death rates, the level of GDP per capita and the unemployment rate. Using the hierarchical cluster analysis approach, the second aim of this research is to form and compare the clusters of European countries with similar characteristics according to these variables for both years. The descriptive statistics results show that a relatively high heterogeneity of European countries relative to the observed variables was present in both years, but the change in variables averages suggests an overall improvement of economic and business demography features in 2016. The correlation analysis revealed that the death rate of enterprises had a statistically significant negative correlation with GDP per capita in both years and a significant positive correlation with the unemployment rate and with the birth rate of enterprises only in 2012. The hierarchical cluster analysis resulted in seven clusters in each observed year. The comparison of clustering solutions for 2012 and 2016 detected the European countries for which the movement between clusters could be seen as a deterioration and those for which it can be seen as an improvement in some or all of the observed variables. These results have important implications for public policies aimed at reducing death of enterprises and unemployment.

The appropriate application of statistical methods in enterprises should have an important role in business decision-making processes. However, Croatian enterprises still tend to have certain resistance to statistical methods. The new challenges introduced by the COVID-19 pandemic emphasised the importance of conducting statistical analyses as support for making business decisions. In order to investigate the situation and attitudes towards the use of statistical methods, primary research was conducted in the form of a web survey on a sample of 768 Croatian enterprises, of which 40% use statistical methods in their business. The research revealed the level of statistical methods use in Croatian enterprises, demonstrated which statistical methods Croatian enterprises prefer, and defined who is most responsible for their use. Furthermore, the reasons for the use of statistical methods and the major obstacles to the use of more intensive statistical methods were also investigated. The results are described and discussed on an overall level and by considering the size of the enterprises.

Globalizacija je dovela do smanjenja troškova prijevoza i značajnog širenja međunarodne trgovine. Međutim, postoje dokazi koji upućuju na neuspješno smanjenje trgovinskih troškova, prikazano porastom koeficijenta udaljenosti u standardnom gravitacijskom modelu. U ovom radu istaknuta je važnost intraregionalne trgovine pri objašnjenju zagonetke izostale globalizacije ili zagonetke udaljenosti. Procijenjen je panel regresijski model korištenjem bilateralnih trgovinskih podataka za uvoz EU-15 zemalja u razdoblju 2001. – 2018. godine. Model slučajnih efekata odabran je kao najprikladniji model s obzirom na vremenski nepromjenjive karakteristike podataka. Učinak udaljenosti predstavljen je varijablom udaljenost definiranom kao udaljenost između glavnih gradova zemalja trgovinskih partnera. Dodatne binarne varijable uključene u analizu su zajednička granica, pripadnost EU-15 grupi zemalja i pripadnost istom kontinentu. Rezultati analize pokazali su da zagonetka izostale globalizacije još uvijek vrijedi. Vrijednosti regresijskih koeficijenata varijable udaljenost porasle su u promatranom razdoblju kako u procijenjenom cjelokupnom panel modelu, tako i u modelima specificiranih za pojedine zemlje. Nadalje, vrijednost koeficijenta regresije značajno ovisi i o uzorku koji se uzima. Na primjer, kada se promatraju samo trgovinski tokovi u rasponu od 1 000 km, vrijednosti regresijskih koeficijenata varijable udaljenost vrlo su velike, što implicira porast učinka udaljenosti.

Two-way analysis of variance (ANOVA) without replication is called a factorial ANOVA with two factors. It is used to test if there is a significant difference between means of several sets of data (groups) dependable on two independent factors. It is applied when we have one measurement variable and two nominal variables (usually called ‘factors’ or ‘main effects’). In this chapter hypotheses and assumptions of the method are given. Then the example of the procedure of two-way analysis of variance (ANOVA) without replication is described in details. The two-way analysis of variance (ANOVA) with replication is utilized to simultaneously test the effects of varying two variables for a sample which consists of more than one respondent per a certain combination of variables. The example of the procedure of two-way analysis of variance (ANOVA) with replication is described in details in this chapter. For both procedures the easy to follow examples shows the procedure stepby-step. The practical part includes the guidance for SPSS and for Excel.

In this chapter, the “between-subject” is dealt with, as well as the single hypothesis approach. Both parametrical and non-parametrical versions of the tests are described. All tests are introduced, and the full, step-by-step SPSS guidance is presented. The sections regarding effect size and about writing the report are also included.

The COVID-19 infection started in Wuhan, China, spreading all over the world, creating global healthcare and economic crisis. Countries all over the world are fighting hard against this pandemic; however, there are doubts on the reported number of cases. In this paper Newcomb-Benford Law is used for the detection of possible false number of reported COVID-19 cases. The analysis, when all countries have been observed together, showed that there is a doubt that countries potentially falsify their data of new COVID-19 cases of infection intentionally. When the analysis was lowered on the individual country level, it was shown that most countries do not diminish their numbers of new COVID-19 cases deliberately. It was found that distributions of COVID-19 data for 15% to 19% of countries for the first digit analysis and 30% to 39% of countries for the last digit analysis do not conform with the Newcomb-Benford Law distribution. Further investigation should be made in this field in order to validate the results of this research. The results obtained from this paper can be important for economic and health policy makers in order to guide COVID-19 surveillance and implement public health policy measures.

Background: By joining different regional economic trade agreements, countries achieve preferential trade liberalisation. There are four main types of regional economic agreements in the world today: free trade area, customs union, common market, and economic and monetary union. Objectives: The goal of this paper is the measurement of the export market concentration for the largest European regional economic integrations in the period between 1995 and 2016. Methods/Approach: Various concentration measures have been used in the measurement of export market concentration, but the emphasis is placed on the standardized Herfindahl-Hirschman index as the basic measure of trade concentration. Results: Results of the analysis have shown that the highest concentration level of trade with countries worldwide is among the European Free Trade Association (EFTA) countries, whereas the EU-15 countries seem to have the lowest concentration level. On the other side, the Central European Free Trade Agreement (CEFTA) countries have the highest concentration level of trade with countries from the same group, and again the EU-15 countries have the lowest concentration level, which indicates that the CEFTA countries implemented deeper integration processes related to mutual intra-regional trade. Conclusions: Deep integration processes led to lower values of export market concentration indices for intra-regional trade among countries of the same regional economic integration in comparison to trading with countries worldwide.

The goal of this paper is to present a new approach to examining psychological pricing strategies using the law of anomalous numbers on the global food prices database. Benford’s Law distribution is used in order to see whether first price digits follow Benford’s Law. On top of that, the discrete uniform distribution is used to examine whether the last digit and the last two digits of a price have the same frequency of occurrence or whether there are indications that there are symbolic meanings to price endings. The results of the analysis showed that the global food price digits distribution does not follow the chosen distributions.

The study described here introduces new approach for testing the Heckscher-Ohlin-Vanek (HOV) theorem based on the normalized trade balance concept. The intention was to include in the analysis all countries worldwide but due to the lack of data a certain number of countries had to be excluded. Overall 111 countries were observed according to region and income level for the year 2014. The HOV model was estimated using the sign test. It compared the expected sign of the normalized trade balance or net exports, according to the SITC 2 product classification, with the relative endowment of production factors intensively used in the production of a specific product. Production factors were divided into groups such as produced capital, labour force and natural resources further divided into forests, metals and minerals, oil, coal and gas, pastureland and cropland. Researchers in R&D per million people variable represented the impact of technological differences across countries. The results of the sign test have shown that the HOV theorem held in 55% of cases. The percentage of matched signs was highest for the non-OECD high income countries (75%) and lowest for the lower middle income and low income countries (below 50%).

Bolest COVID-19 je visoko zarazna bolest čiji je uzročnik takozvani koronavirus. Uzročnik bolesti, virus SARS-CoV-2, prvi puta je otkriven krajem 2019. godine u gradu Wuhanu u kineskoj provinciji Hubei. Odatle se velikom brzinom proširio diljem svijeta uzrokujući svjetsku pandemiju COVID-19 bolesti. U Hrvatskoj je prvi slučaj zaraze potvrđen 25. veljače 2020. godine. U radu se opisuje kretanje broja zaraženih, broja umrlih te broja aktivnih odnosno izliječenih osoba od COVID-19 bolesti u Republici Hrvatskoj u razdoblju od 25. veljače 2020. godine do 25. veljače 2021. godine. U radu se također prikazuje i kretanje vrijednosti varijabli povezanih s COVID-19 bolesti na razini županija Republike Hrvatske. Ovaj rad predstavlja temelj za buduća statistička modeliranja vezano za kretanje COVID- 19 varijabli, a što bi trebalo rezultirati rješenjima za obuzdavanje navedene pandemije.

The gravity model of trade has been a workhorse of international economics over the last fifty years. The main variables incorporated in the standard gravity model of trade are gross domestic product of trading countries and distance between them. The previous investigation has been limited only to an econometric estimation and evaluation of regression coefficients in the gravity model and their significance. However, until now there has been no research on investigating the impact of GDP and Distance variables in the gravity model by using sign and rank tests, which is the objective of this paper. This paper adds to the existing literature by employing non-parametric approach to estimating the impact of variables in the gravity model by using sign and rank tests in the case of World countries. The results of the analysis have shown that the GDP variable exhibits a higher distribution of positive signs achieved in the sign test and presents less average errors in the rank test in predicting bilateral trade imports with regards to the Distance variable. Furthermore, the GDP variable also has a relatively higher impact in the gravity model than the Distance variable.

The special issue of Croatian Review of Economic, Business and Social Statistics (CREBSS) is focusing on contemporary issues in statistical methods and data science applications. CREBSS is the first statistical journal established in Croatia and it is published by Croatian Statistical Association (CSA). Statistical methods have widespread use in different application areas. Statistical methods have shown to be very useful methods which, for example, can improve production processes, help in decision making, forecasting and simulate developments in the future periods. However, depending on the analysed processes, available data and used statistical methods, different issues can appear in practice. This special issue brings six papers that cover using statistical methods and their ability of issues solving in different areas like stock market prediction and stock selection, COVID-19 spread analysis, presentation skills of business and economics students, measurement errors of different survey modes and tourism traffic administrative evidence.

The expansion of the Internet has radically changed the way in which citizens travel, book and organise travel arrangements. Since innovation and new information technologies have become crucial determinants to encourage competitiveness in the tourism sector in Europe, this article investigates how selected development indicators influence the Percentage of individuals that use Internet for travel and accommodation services. Eurostat data for 34 European, European Union (EU-28) and selected EU candidates, countries for 2017 were analysed. It has been presented that (1) Gross Domestic Product per capita in Purchasing Power Standards; (2) Percentage of population aged 15 to 64, by tertiary education; as well as (3) Percentage of individuals aged 16 to 74, who have basic or above basic overall digital skills, all correlate positively and strongly with the main variable under study. The conducted regression analysis has shown that variable digital skills has the greatest impact on the main variable under the study. The K-mean clustering of countries resulted with four clusters. The Western Balkan countries can be found in a cluster which has in average the lowest values of all four variables in compare to the averages of other three clusters.

Statističke metode namijenjene su pomaganju poduzećima u praćenju i poboljšanju postojećih poslovnih procesa te u donošenju poslovnih odluka. U radu se ispituje razina primjene statističkih metoda u poduzećima u Hrvatskoj te njihova veza s poslovnim rezultatima poduzeća. Provedeno web anketno istraživanje, u kojem je primijenjen složen dizajn uzorkovanja, pokazalo je da samo kod srednje velikih i velikih poduzeća više od polovice poduzeća primjenjuje statističke metode u svojem poslovanju. Utvrđeno je da postoji statistički značajna razlika u prosječnoj vrijednosti ostvarene neto dobiti u 2016. godini između poduzeća koja primjenjuju statističke metode i poduzeća koja ih ne primjenjuju. Ovisno o promatranoj stratifikaciji, pokazalo se da poduzeća koja primjenjuju statističke metode imaju od 27, 6% do 31, 9% veću šansu ostvarivanja pozitivne neto dobiti u odnosu na poduzeća koja ih ne primjenjuju. Međutim, ovaj zaključak vrijedi samo kad se promatraju poduzeća u cjelini. Dodatne analize pokazale su da duljina i učestalost primjene statističkih metoda nemaju statistički značajan utjecaj na vrijednost neto dobit poduzeća. Zbog toga bi trebalo pridodati više pažnje samoj kvaliteti primjeni statističkih metoda i obrazovanju zaposlenih.