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Nowadays there is a large amount of information at our disposal, which is increasing day by day, and right now the question is not whether we have a method to process it, but which method is most effective, faster and best. When processing large databases, with different data, the formation of homogeneous groups is recommended. This paper presents the financial performance of Hungarian and Romanian food retail companies by using two well-known cluster analyzing methods (K-Mean and K-Medoid) based on ROS (Return on Sales), ROA (Return on Assets) and ROE (Return on Equity) financial ratios. The research is based on two complete databases, including the financial statements for five years of all retail food companies from one Hungarian and one Romanian county. The hypothesis of the research is: in the case of large databases with variable quantitative data, cluster analysis is necessary in order to obtain accurate results and the method chosen can bring different results. It is justified to think carefully about choosing a method depending on the available data and the research aim. The aim of this study is to highlight the differences between the results of these two grouping procedures. Using the two methods we reached different results, which means a different evaluation of financial performance. The results demonstrate that the method chosen for grouping may influence the assessment of the financial performance of companies: the K-Mean method produces a greater variety of groups and the range of results obtained after grouping is larger; whereas, the group distribution and the results obtained by the K-Medoid method are more balanced. [ABSTRACT FROM AUTHOR]

The issue of Industry 4.0 revolution is a highly discussed topic in response the industrial production is one of the most important economic sectors in the European Union. Industrial transformation and digitalization seem to be necessary evolution steps due to the lack of skilled laborers in the EU. Current demographic trends and projections of the future workforce composition are projecting warning signals. Aims: This paper reviews documented efficiency of the Industry 4.0 and adds empirical evidence on the Industry 4.0 readiness from Hungary. The main goal of this article is to calculate the innovative I4.0 Readiness Index in Hungary by using industry level indicators, and to compare results within EU-27 countries. Outcomes will be used to identify clusters of macroeconomic similarity. Data and methods: Literature review was conducted. I4.0 Readiness Index was enumerated based on data from Eurostat. Cluster analysis was used to determine the different country classes (groups) in transition towards Industry 4.0. Results: Denmark has the highest score value of I4.0 Readiness Index among the EU-27 member states, Bulgaria has the lowest performance. Hungary is on the 23rd place in the ranking. Conclusion: EU-27 region may be divided into five clusters. This paper confirmed macroeconomic disparities among countries. Cluster analysis is in accordance with results of the I4.0 Readiness Index. Findings in this paper may be considered in a future research by analysing further I4.0 indicators. [ABSTRACT FROM AUTHOR]

The evaluation of scientific publication performance has become one of the most important and, at the same time, one of the most debated issues in international academic circles. This problem is attenuated by the digital transformation of science; online repositories, indexing systems and online visibility have become key enablers of evidence-based assessments of publication performance. In our paper we examine the scientific publication performance of 2131 members of the public body of the IX. Department of the Hungarian Academy of Sciences As its members represent the diversity of Hungarian social sciences very well, it was possible to make a broader generalization about their achievements regarding their publication performance. We found that one third of this sample has an internationally visible presence in the Repository of Hungarian Scientific Artefact (RHSA) database, which is the central repository that provides data for scientific promotion and assessment. By running a two-step cluster analysis we could identify five typical clusters of scientists based on their publications -- each of them focusing on a different outlet such as books, domestic journals, international journals, or a balanced publication strategy that focuses on all the former types. In contrast to previous findings concerning technology acceptance, we found that the younger generation of Ph.Ds often adopt to the use of RHSA less than more senior individuals that have higher academic positions. [ABSTRACT FROM AUTHOR]

The most widely used measure for studying social, economic, and health inequality is the Gini index/ratio. Whereas other measures of inequality possess certain useful characteristics, such as the straightforward decomposability of the generalized entropy measures, the Gini index has remained the most popular, at least in part due to its ease of interpretation. However, the Gini index has a limitation in measuring inequality. It is less sensitive to how the population is stratified than how individual values differ. The twin purposes of this paper are to explain the limitation and to propose a model-based method—latent class/clustering analysis for understanding and measuring inequality. The latent cluster approach has the major advantage of being able to identify potential “classes” of individuals who share similar levels of income or one or more other attributes and to assess the fit of the model-based classes to the empirical data, based on different cluster distributional assumptions and the number of latent classes. This paper distinguishes class inequality from individual inequality, the type that is better captured by the Gini. Once the classes are estimated, the membership of estimated classes obtained from the best fitting model facilitates the decomposition of the Gini index into individual and class inequality. Class inequality is then measured by two relative stratification indices based on either the relative size of the Gini between-class components or the relative number of stratified individuals. Therefore, the Gini index is extended and assisted by model-based clustering to measure class inequality, thereby realizing its great potential for studying inequality. Income data from France and Hungary are used to illustrate the application of the method. INSETS: Figure 1. Gini indices and Lorenz curves for income data...;Figure 2. Empirical income distribution of France in 1990...;Figure 3. Income clustering in France, 1990.;Figure 4. Income clustering in Hungary, 1992. [ABSTRACT FROM AUTHOR]

Technological advances are introducing human-robot collaboration to many areas, including the corporate environment. The nature of work and occupations will thus change fundamentally in the future. One of the prerequisites for successfully meeting these challenges is the acceptance of robots, which is influenced by, among other things, the attitudes of individuals. The literature review synthesises the results of theoretical articles focusing on the factors influencing the development of attitudes towards robots. The aim of this paper is to investigate the evolution of attitudes towards robotization in Hungary, using the latest European Value Survey (EVS) data collection, cluster analysis and ANOVA methods. The relevance of attitudinal research on the robot workforce is unquestionable and, to date, no similar research on a Hungarian sample has been conducted. [ABSTRACT FROM AUTHOR]

• Quantification of losses in the food industry would cause difficulties in Hungary. • Generation of losses is considered a phenomenon related to technological processes. • Larger companies and some subsectors consider food loss a more serious problem. • Respondents' motivations for handling the problem are mainly financial. The food industry is estimated to be the second biggest generator of food losses after households. The novelty of this paper is that it focuses on this less analysed sector, conducting exploratory research based on a detailed primary survey among Hungarian food processors with a uniquely large sample of 175 respondents. A SPSS program was used to analyse the data, generating descriptive statistics, statistical tests for exploring relationships between variables and cluster analysis for classifying respondents based on their attitudes towards the problem. In-depth interviews (19) and a workshop were also applied, refining the results. Only half of the respondents maintain exact records of food loss. The leaders of the companies usually do not consider the generation of losses as a serious problem, but instead tend to see it as a natural phenomenon related to technological processes. Based on the cluster analysis carried out, the companies could be classified into two distinguishable groups. Larger companies perceived the loss to be a significantly more serious problem, they recorded it more accurately than the other group and they had more important motivations such as the desire to achieve cost reduction and better allocation of resources. Food processors face several barriers during food loss management: regulatory issues, the lack of a developed market for by-products, insufficient stakeholder collaboration along the food chain and currently, insufficient incentive to reduce losses. Reduction and better utilisation of food loss can only be achieved if there is a fundamental change in food loss regulation: incentives should be provided to drive companies to avoid losses and to make the best use of the by-products. [ABSTRACT FROM AUTHOR]

In this paper, the authors present new palaeoecological and sedimentological results from the Upper Palaeolithic site of Ságvár, Hungary. Simple (abundance and dominance) and advanced (cluster analysis, principal component analysis, correspondence) statistical analyses of malacological results were carried out, determining eight malacological zones (MZs) and the key species of the fauna evolution in the sequence. Furthermore, an age–depth model was calculated via Bayesian modelling using new radiocarbon age data. The accumulation rate (AR) was calculated by deriving data from the age–depth model. The palaeoecological reconstruction indicated a cool/cold climate and chiefly steppe environment, with low loess ARs. Not only the vegetation cover but also the geographical setting of the sequence could have led to the low AR values. However, in the uppermost part of the sequence, during the GS 3 and GS 2.1c stadials, the reconstructed palaeoclimate indicated a warming period. Nevertheless, this contradiction is not unique in the Carpathian Basin. The settled Upper Palaeolithic hunters around 22 300 cal a bp lived in a progressively warming wooded steppe environment, hunting mainly for reindeer. Climate change might have forced them to migrate away, following the reindeer. [ABSTRACT FROM AUTHOR]

Two approaches of local weather type definitions are presented and illustrated for selected stations of Poland and Hungary. The subjective classification, continuing long traditions, especially in Poland, relies on diurnal values of local weather elements. The main types are defined according to temperature with some subtypes considering relative sunshine duration, diurnal precipitation totals, relative humidity and wind speed. The classification does not make a difference between the seasons of the year, but the occurrence of the classes obviously reflects the annual cycle. Another important feature of this classification is that only a minor part of the theoretically possible combination of the various types and sub-types occurs in all stations of both countries. The objective version of the classification starts from ten possible weather element which are reduced to four according to factor analysis, based on strong correlation between the elements. This analysis yields 3 to 4 factors depending on the specific criteria of selection. The further cluster analysis uses four selected weather elements belonging to different rotated factors. They are the diurnal mean values of temperature, of relative humidity, of cloudiness and of wind speed. From the possible ways of hierarchical cluster analysis (i.e. no a priori assumption on the number of classes), the method of furthest neighbours is selected, indicating the arguments of this decision in the paper. These local weather types are important tools in understanding the role of weather in various environmental indicators, in climatic generalisation of short samples by stratified sampling and in interpretation of the climate change. [ABSTRACT FROM AUTHOR]

This paper determines the characteristic air mass types over the Carpathian Basin for the winter (December, January, and February) and summer (June, July and August) months dependant on levels of the main air pollutants. Based on the ECMWF data set, daily sea-level pressure fields analysed at 00 UTC were prepared for each air mass type (cluster) in order to relate sea-level pressure patterns with the level of air pollutants in Szeged. The data comprise daily values of twelve meteorological and eight pollutant parameters for the period 1997–2001. Objective definition of the characteristic air mass types is achieved by using the methods of Factor Analysis and Cluster Analysis. According to the results, during the winter months five air mass types (clusters) were detected based on higher concentrations of primary pollutants that occur with high irradiance and low wind speed. This is the case when an anticyclone is found over the Carpathian Basin and over the region south of Hungary, influencing the weather of the country. Low levels of pollutants occur when zonal currents exert influence over Hungary. During the summer months anticyclones and anticyclone ridge situations are found over the Carpathian Basin. (During the prevalence of anticyclone ridge situations, the Carpathian Basin is found at the edge of a high pressure centre.) As a result of high irradiance and very low NO levels, secondary pollutants are highly enriched. [ABSTRACT FROM AUTHOR]

Objective: The objective of the article is to examine how respondents' technological readiness (as an individual factor besides demographic characteristics) influences channel preference (in-store, online big- and smallscreen at different stages of the purchasing decision process for high-value electronic devices (products). Research Design & Methods: The research encompassed data collected by a quantitative online survey of 415 respondents in Hungary. To identify homogenous groups in the sample, we used cluster analysis based on factors we determined among the technology-readiness variables. Findings: We identified the technological readiness index 2.0 (TRI) segments in our sample and our findings confirmed that the perceived technological readiness has a significant influence on customers' channel choice. Implications & Recommendations: Customer experience (CX) is far more than satisfaction with the product; it is influenced by the total purchasing decision process starting at the need recognition and ending at the postpurchasing stage. The difficulties and uncertainties in any stage of the decision-making process result in anxiety and reduce the CX. The uncertainty can arise from factors related to the product, individual, or channel. Contribution & Value Added: Although the sample is not representative, it provides insight into how Hungarian respondents can be segmented based on technological readiness and how this affects their channel preferences during the customer journey through purchase decisions regarding electronic devices. [ABSTRACT FROM AUTHOR]

Environmental challenges often present businesses with unexpected situations, and in order to address them, innovation in the direction of sustainability must become an unavoidable activity. This entails the transformation and development of the existing business models, assuming a great business risk. The occurrence of the risk and its extent can only be estimated, which is why it is important to have management models that are able to handle the challenges posed by new, constantly arising risk factors. We analyzed the largest companies based on the number of employees with headquarters or sites in Hungary with regard to the management methods used by them to manage environmental risks. The methods used were the analysis of variance and cluster analysis. Based on the results of the research it is clear that the companies surveyed are already very concerned with environmental opportunities and risks, and they expect that the role of innovations applied to manage them to play a more prominent role in their future target system. However, the level of this is significantly different and does not depend on the financial performance, and at the same time companies can be divided into distinct groups according to the level of environmental risk management. [ABSTRACT FROM AUTHOR]

One of the main aims of European ethnology in the second half of the 20th century was to create the ethnographical atlases of various nations in Europe. The basic purpose of the cartographical elaboration of the regional variants of certain cultural elements of the given notion in o certain system and that of collecting them into atlases wan to create a database on which investigations could be carried oat to define the territorial structure of the given folk culture. The easiest way to define this territorial pattern it the computer elaboration of the database, which means the digitalization and the cluster analysis of the data made by composer. On the methods and on the possibilities of the computer elaboration of the Atlas of Hungarian Polk Culture (AHFC) a paper was held by the author at the 11th Conference of the SIEF's International European Network (Workgroup) on Ethnocartography in Poland (Borsos 2000). At the 12th conference in Slovakia the author talked about the first results of the cluster-analysis (Borsos 2000/2001). In the last decade the computer programs for the digital version of the AHFC have been developed and the digital version has been extended with supplementary maps as welt. At in the digital version we can find not simply scanned pictures of the original sheets but the basic structure of the atlas (hate-map, collecting points) it alto available, it is not only possible but fairly easy to add new (virtual) sheets to the atlas. So the Atlas has been supplemented with maps elaborating tome of the statistical data (demographic and agricultural) of the period between 1900-1910, which is the time interval represented by the cultural data of the atlas. This virtual 10th volume of the atlas contains `sheets' about important information on the cultural picture of the settlements shown and of their cultural environment. The new volume can alto help to draw a more accurate map about cultural regions. Another type of supplementary maps can be teen in the virtual 11th volume showing the regional distribution of the territory inhabited by Hangarians regarding cultural and non-cultural aspects. The last section of the distributional maps shows the regional structure of the Hungarian folk culture based on the computer elaborated data of the first 9 volumes, at well as the synthetic regional structure bated on the comparison of the computer-drawn picture with three other sources: the statistical investigation of the database, the maps of the two virtual volumes and the scientific literature. [ABSTRACT FROM AUTHOR]

The delimitation and classification of peripheral settlements using multivariate statistical methods is presented in this article, with a case study of Hungary. A combination of four different methods provided the basis for the delimitation of settlements defined as peripheral. As significant overlapping was detected between the results of the different methods, peripheries – more than one-fifth of the Hungarian settlements – were identified in a common set of the results. The independence of the results from the applied methods points to the fact that peripherisation is multi-faceted, and the peripheries of Hungary are stable and well-discernible from other regions. After the identification of peripheral areas, we classified these settlements into groups based on their specific features. Multiple steps specifying the relevant variables resulted in selecting the most appropriate 10 indicators and these served as the basis for a hierarchical cluster analysis, through which 7 clusters (types of peripheries) were identified. Five of them comprised enough cases to detect the most important dimensions and specific features of the backwardness of these groups. These clusters demonstrated a spatial pattern and their socioeconomic and infrastructural features highlighted considerable disparities. These differences should be taken into consideration when development policies are applied at regional levels or below. [ABSTRACT FROM AUTHOR]

This study aims to present the endogenous developmental potential of the Sellye district. We examine the state of the internal capital factors of the area as well as determine indicators that can reveal the spatial structural characteristics of these factors. The main types of settlements of the district, along with the elements above were identified through a cluster analysis based on secondary statistical data. A hierarchical cluster analysis and Ward's method were used to form the clusters. The results show that the endogenous developmental potential of the Sellye district is low; the effects of the socioeconomic problems of the area are reflected in almost all of the examined capital factors. This is especially true for the most disadvantaged and least populated settlements of the district. [ABSTRACT FROM AUTHOR]

We present a framework based on a higher education application preference list that allows a different type of flexible aggregation and, hence, the analysis and clustering of application data. Preference lists are converted into scores. The proposed approach is demonstrated in the context of higher education applications in Hungary over the period of 2006–2015. Our method reveals that efforts to leverage center-periphery differences do not fulfill expectations. Furthermore, the student's top preference is very hard to influence, and recruiters may build their strategy on information about the first and second choices. [ABSTRACT FROM AUTHOR]

We present an application preference, list-based framework to Hungarian universities, which allows different type of flexible aggregation, and hence, analysis and clustering of application data. A novel mathematical method is developed by which preference lists can be converted into scored rankings. The proposed approach is demonstrated in the case of Hungary covering the period of 2006–2015. Our method reveals that the efforts to leverage the geographical center–periphery differences did not fulfil the expectations of policy makers. Also, it turns out that a student's top preference is very difficult to influence, while recruiters may build their strategy on the information of the first but one choice. [ABSTRACT FROM AUTHOR]

In the article we would like to point out the similarity respectively the diversity of tax systems in selected countries of Central Europe. We analyzed the structure and amounts of collected taxes in Slovakia, Hungary and Czech Republic. This analysis should be the starting point for considering the possibility of direct tax harmonization in the EU. The article also deals with the nature, conditions and future of direct tax harmonization in European Union. The objective was to assess the feasibility of full direct tax harmonization in the EU. We used the cluster analysis method to outline possible options for implementing this process in Member countries. The article also includes the opinions of experts and stakeholders on this issue. [ABSTRACT FROM AUTHOR]

The purpose of the study was to explore the relationship between religious doubting and adult attachment style. We aimed at finding out which of the hypotheses explaining the relationship between attachment and religiosity (correspondence and compensation hypotheses) is supported by our data in the case of religious doubting. 712 Hungarian adults participated in the study. Adult attachment was assessed by the Attachment Style Questionnaire4, and for measuring religious doubting the Religious Doubt Scale5 was used. Results showed that, from the ASQ scales, "discomfort with closeness" and "preoccupation with relationships" correlated positively with religious doubts. The relationship was stronger between doubts and "preoccupation with relationships" in the case of women, and "discomfort with closeness" in the case of men, suggesting that there are gender differences in the nature of doubting. On the basis of the cluster analysis performed on the five scales of ASQ, we identified three clusters that could be regarded as equivalent to the secure, avoidant, and anxious-ambivalent attachment styles described in Hazan and Shaver's model6. Individuals in the cluster corresponding to secure attachment scored significantly lower on the religious doubts scale than individuals in the two other clusters. Our study showed that insecure attachment is linked with stronger doubting; this result supports the correspondence hypothesis. However, secure attachment was not related to lower levels of doubting. This might suggest that, in the case of securely attached individuals, religious doubting can be interpreted as a kind of religious explorative behaviour (after Bowlby's theory), that is made possible by the experience of the secure base. Further studies are needed to test this assumption. [ABSTRACT FROM AUTHOR]