Suchergebnisse

The aim of the paper is to compare the performance of economic researchers in Austria, Romania and the Visegrad 4 (Czech Republic, Hungary, Poland, and Slovakia) using performance indicators of researchers from the Scopus and SciVal databases. In the comparison of countries, Austria is included as a benchmark country, while the other five countries represent the countries of the former Eastern bloc. In the study, the definition of an economic researcher is based on indicators that can be obtained from databases. Paper pursued two goals. First, by presenting the relationships between the data obtained from the Scopus/SciVal databases, to present the most important key indicators, then to group the researchers with the help of the analyzed indicators, and to compare the publication performance of the chosen countries. A researcher is considered to be an economic researcher in the study whose at least thirty percent of the published articles in the SCImago database are in the subject areas of Business, Management, and Accounting and Economics, Econometrics, and Finance.

AbstractConsidering ecological issues in supplier evaluation and management alongside business considerations is getting more recognition among firms. Data envelopment analysis (DEA) is one of those methods, which is frequently suggested by the literature to support management decisions. However, the data requirements of the method should be an important consideration. The literature often addresses the issue of desirable outputs and undesirable input as an important data related problem in case of the ecological use of DEA. This paper will present a new solution to manage these data problems along with connecting the evaluation of management criteria, environmental criteria and total cost aspects. The proposed environmental supplier selection problem is an extension of a former paper. The new model examines the effect of inventory related costs, such as EOQ costs of inventory holding or ordering costs on the selected supplier, extended with newly introduced scaled values of input and output indicators. The usage of scaled values is motivated by the problem of invariance to data alteration. In addition to the uncertainty of the data, the paper looks for a functional relationship between the input and output criterion values and the efficiency that can be assigned to them using DEA.

The Network Readiness Index (NRI) is one of the indicators that shows the level of digital development of countries. The NRI for 2021 shows the development of 130 countries, in contrast to the 45 countries covered by the International Digital Economy and Society Index (I-DESI) of the European Union, which measures only the most developed countries. This paper aims to determine the relationship between 12 sub-pillars of NRI. We use Principal Component Analysis (PCA) to perform a mapping of our data to a lower-dimensional space, and further analyse the causal relationships between the principal sub-pillars using partial correlation coefficients, concluding that two of the twelve main sub-pillars can be explained by ten independent sub-pillars. Thereafter, we use cluster analysis to group our objects (i.e. the 130 countries) into clusters.

This paper focuses on forecasting of products with sporadic demand. The demand for such products is not continuous but diffused seemingly at random, with a large proportion of zero values in the analyzed time series. The sporadic character of demand patterns actually means that the information available on the demand for previous selling periods is patchy, resulting in lower quality of data available. Under such circumstances demand forecasting is a challenging task. We present the results of a case study, where forecasting practice of a pharmaceutical wholesaler firm –we call it Pharma– is analyzed and developed. We present state-of-the-art knowledge related to demand forecasting of sporadic products and test suggestions related to them. We show that these suggestions can only partly be backed. We extend therefore the suggested product classification scheme and recommend using the concept of demand data aggregation. This will reduce sporadicity and result in higher quality forecasting. Aggregation also helps to specify the recommended forecast period, the length of time recommended to calculate the forecast for. The managerial consequences of these suggestions are also discussed, and future research directions are highlighted.

Supplier selection is an important business decision. Beside traditional management criteria the environmental aspects are getting often recognition. In this paper the method of Data Envelopment Analysis (DEA) is used to study the extension of traditional supplier selection methods with environmental factors. The focus will be on the weight selection process which can control the selection. In this method we divide the criteria in two manners: the traditional and environmental (green) factors. Then with the help of DEA we are searching a weight system with which the environmental criteria can influence the decision with a representation of the green factors. To choose the mentioned weight system, we apply DEA (Data Envelopment Analysis) with common weights analysis (CWA) method. In this case of DEA/CWA the common weights are calculated with a linear programming problem.

The theory of economic motion was András Bródy's main interest. This paper presents a simplifi ed framework of Bródy's economics. His multi-sector production and price theory is based on the Marxian theory of value reinterpreted by using measurement considerations. Economic motion in this framework is driven by technology represented by the internal proportions of production, not by external shocks. Prices and proportions jointly determine the economic structure and its motion (duality of prices and volumes). We derive the laws of motion of production and use of goods (consumption and accumulation) based on technological accounting balances. These laws determine a cyclical pattern. Using numerical examples we demonstrate how external changes in technology and valuations are propagated in changing the cyclical pattern of motion.

A new wave of applications of the dynamic Leontief model brought into the forefront the singularity problem of the capital matrix. In these applications the singularity of the capital matrix is a common occurrence which complicates the solution of the model. In the singular case the model cannot be transformed in a direct forward recursive form. The method presented in this paper determines the length of a backward system (τ). Several applications stop at observing singularity while referring to the theoretical possibility of the solution. In particular, the singularity of the capital matrix played a prominent role in Bródy's extensive contributions to the input-output literature but he never ventured into the details of its various solutions. We demonstrate that a number of papers dealing with the Leontief model with singular capital matrix based their solutions on similar regularity assumptions. Our formulation in this paper offers a brief overview of the approaches that can be followed in a wide range of applications confronting with the singularity problem.

In recent years, researchers have sought to specify precisely what is meant by the ecological footprint, and there are some methods for calculating it. This paper features a new calculation method for determining the ecological footprint (EFP). The basis of our model is the dynamic Leontief model. If our method is applied, one can determine that a dynamic ecological footprint is a sequence of footprints for periods. We also calculate the ecological footprint for both closed and open economies. Our model contains elements taken from the Leontief model: capital accumulation, and integration of exports and imports into the model through input-output panels. All periods are treated as interdependent, rather than as a series of stand-alone data. Most notably, the model separates capital accumulation from the final use of capital, i.e., investment and final consumption. We illustrate the results with numerical examples.

Digitalization and technological advances develop together with our society. The European Commission intends to monitor this development using the International Digital and Society Index (I-DESI) and provide an objective comparison of the participating countries. This comparison of the counties can be an important part of the roadmap of digital transformation for companies and other participants in the market. The aim of this study is to further analyze the 2020 data of I-DESI using multivariate statistical methods not included in the official report. Our objective is to show whether there are differences between the EU and non-EU countries (discriminant analysis, variance analysis), whether the dimensions of the I-DESI index are overlapping (correlation analysis, factor analysis), and whether different country groups can be formed (cluster analysis). Answering these questions, we can give a useful tool to companies for a more successful digital transformation.

This paper continues the authors' earlier analysis, in which we used five principle dimensions of the International Digital Economy and Social Index (I-DESI) for the 28 countries of the EU and the Russian Federation to examine how Russia's development relates to that of other EU countries. The aim of this paper is not to establish a ranking, but to determine the relationship between each dimension and the groups into which these 29 countries can be divided by multivariate statistical analysis tools and to analyze the group to which the Russian Federation belongs. We use Principal Component Analysis (PCA) to map our data to a lower-dimensional space (revealing two latent dimensions), and to analyze causal relations between the principal dimensions using partial correlation coefficients, concluding that two of the five main dimensions can be explained by three independent dimensions. Thereafter, we use cluster analysis to group our objects (i. e. the 29 countries) into clusters, and multidimensional scaling (MDS) to visualize the location of these groups and countries on the plane of the two components (from the PCA), focusing on the Russian Federation. According to our results, the Russian Federation can be classified as a moderately developed country in terms of its I-DESI score, but its location on the plane of principle components differs from the group of moderately developed EU-countries, forming a separate "group" on its own, largely owing to the unique characteristics of the country's digital development.