Search

In this paper, an approach is proposed for assessing the performance of experts in the group from two perspectives: (1) individual consistencies and (2) deviations from the group decision. The quality of performance of the experts is based on combining the standard and rough analytic hierarchy process (AHP) with the technique for order of preference by similarity to the ideal solution (TOPSIS). The statistical method CRITIC is used to derive weights for the TOPSIS method before the experts are assessed based on demonstrated consistency and deviations from the group. Common performance indicators, such as consistency ratio, Euclidean distance, compatibility, and Spearman's correlation coefficient, are proposed for re-grouping experts before making the final decisions. A genetic algorithm enables the efficient solving of this complex clustering problem. Implementing the described approach and method can be useful in comparable assessment frameworks. A critical aspect is conducting a thorough pre-assessment of the competence of potential decision makers, often referred to as experts who may not consistently exhibit apparent expertise. The competence of decision makers (which does not have to be associated with compatibility) is evidenced by selected consistency parameters, and in a way, a pre-assessment of their competence follows Plato's 'government of the wise' principle. In the presented study, the compatibility of individuals in the group with the collective position (group decision) is measured by parameters related to their compatibility with the group solution and statistical deviation while ranking decision elements. The proposed multi-model-based approach stands out for its resilience in conducting thorough pre-assessment of the quality (competence) of potential decision makers, often regarded as experts who might not consistently display evident expertise. The wetland study area in Serbia is used as an example application, where seven measures for reducing the risk of drought were evaluated by twelve experts coming from different sectors and with different backgrounds and expertise. [ABSTRACT FROM AUTHOR]

Many methods exist for solving the problem of evaluating efficiency in different processes. They are divided into two basic groups, parametric and non-parametric methods, which can have significant differences in the results. In this study, the authors consider the process of assessing the business climate depending on realized foreign investments. Due to the expected difference in efficiency assessment using different approaches, the goal of this paper is to create an optimization model of an ensemble for efficiency assessment that uses both types of methods with the aim of creating a symmetrical approach that achieves better results than each type of method individually. The proposed solution simultaneously analyzes the impact of different factors on foreign investments in order to determine the most important factors and thus enable each local government to ensure the best possible efficiency in this process. The innovative idea of this study is in the inclusion of classification and feature selection methods of machine learning to fulfill the set goal. Our research, focused on a specific case study in various cities across the Republic of Serbia, evaluated the effectiveness of that process. This study extends previous research and confirms the published results, highlighting the advantages of the newly proposed model. [ABSTRACT FROM AUTHOR]

Retail supply chains are key on any market. Their significance has long been recognized in the literature and in practice. Various factors such as pandemics, economic crises, wars, and natural disasters have further increased interest in this area. However, the most recent research has focused more on resilience, sustainability, energy consumption, and a circular economy, while the efficiency of logistics processes has been almost completely overlooked. Logistics process efficiency in retail supply chain is a fundamental principle without which all mentioned performances cannot have desired values. This gap is precisely the main motivation of this research. In this paper, research models in literature are presented which can be used, with some modifications, to measure the efficiency of the retail supply chain. The models were based on the data envelopment analysis (DEA) approach. Four main groups were identified: standard DEA models, efficiency decomposition models, network models, and game-theory-based models. In the second part of the paper, various approaches were tested on a real example of a trading company operating in Serbia. Seven supply chains were observed, each consisting of a distribution center (DC) and retail store (RS). Variables used were the number of pallet places, logistics costs, number of deliveries, accuracy of deliveries, and turnover. The results showed the advantages and disadvantages of different approaches in real examples. The main contributions of this paper lie in unique approaches to measuring the efficiency of the retail supply chain. The paper creates an excellent foundation for future research and measurements on real systems, which is equally useful for researchers and industry experts. [ABSTRACT FROM AUTHOR]

For the interpretation of digital entrepreneurship, the context in which the entrepreneurial process takes place plays an important role. The context emphasizes that to analyze the entrepreneurial process, it is no longer sufficient to analyze only the entrepreneurial actions, but also the environment in which the entrepreneurial process takes place. The aim of this paper is to analyze the part of the context in which the entrepreneurial process takes place in the Republic of Serbia, Montenegro, Bosnia and Herzegovina and Hungary. Following the methodology of The European Index of Digital Entrepreneurship Systems (EIDES), the paper analyzes the system framework conditions, in the scale-up phase of the enterprise life cycle, in the digital dimension. The aim of this paper is a comparative analysis of three countries in transition with one country belonging to the European Union. After identifying the most developed factors, as well as the most underdeveloped factors, the paper discusses the conditions that influenced the results achieved in the four previously listed countries and why their improvement is important. [ABSTRACT FROM AUTHOR]

The opportunity for large amounts of open-for-public and available data is one of the main drivers of the development of an information society at the beginning of the 21st century. In this sense, acquiring knowledge from these data using different methods of machine learning is a prerequisite for solving complex problems in many spheres of human activity, starting from medicine to education and the economy, including traffic as today's important economic branch. Having this in mind, this paper deals with the prediction of the risk of traffic incidents using both historical and real-time data for different atmospheric factors. The main goal is to construct an ensemble model based on the use of several machine learning algorithms which has better characteristics of prediction than any of those installed when individually applied. In global, a case-proposed model could be a multi-agent system, but in a considered case study, a two-agent system is used so that one agent solves the prediction task by learning from the historical data, and the other agent uses the real time data. The authors evaluated the obtained model based on a case study and data for the city of Niš from the Republic of Serbia and also described its implementation as a practical web citizen application. [ABSTRACT FROM AUTHOR]

The financial systems of the West Balkan countries are mainly based on banks. For an efficient financial system, it is of immense importance that the banks operating in it do so with high levels of efficiency. Furthermore, efficiency is needed when it comes to involving banking institutions in the financial flows in order to maintain sustainability of the financial construction. The aim of this paper is to determine whether there is a difference in efficiency between the considered countries and thus to show which changes the decisionmakers have to make in order to improve the efficiency of their banking systems. We analyze data from the revised financial statements of all banks operating in Albania, Bosnia and Herzegovina, Montenegro, North Macedonia and Serbia for the period from 2015 to 2019, using loans and investment as input variables and interest income, non-interest income and net income as output variables. The results obtained from the output-oriented DEA model with a variable return to scale have shown higher efficiency levels in North Macedonia, Bosnia and Herzegovina and Montenegro, while Serbia and Albania show lower efficiency. The individual analyses have shown that in order to improve efficiency levels, improvement is needed more in the correction of the investment amounts then in loan placements. [ABSTRACT FROM AUTHOR]

This research paper defines service value as the multidimensional overall user evaluation of the efficiency of a particular service in the context of the benefits they provide, measured through satisfaction and loyalty of consumers and, in this particular case, of entrepreneurs. By distributing a questionnaire comprising a set of descriptive questions and a five-point Likert scale, the authors managed to collect 281 valid responses from entrepreneurs in Serbia. Structural equation modelling was used to evaluate the impact of service value factors on satisfaction and, ultimately, loyalty. Following a confirmatory factor analysis, the authors found that quality, personal relationship, expediency, leadership, and perceived costs significantly impacted the bank users' satisfaction, which is ultimately converted into lasting loyalty towards the dominant bank used in a daily routine or running personal business. Even though new forms of business are more and more focused on the individual and subjective attitude towards banking service, a large number of determinants still need to be identified. In this research, all hypotheses about the influencing factors were confirmed. [ABSTRACT FROM AUTHOR]

This paper describes one of the non-linear (and non-stationary) stochastic models, the GSB (Gaussian, or Generalized, Split-BREAK) process, which is used in the analysis of time series with pronounced and accentuated fluctuations. In the beginning, the stochastic structure of the GSB process and its important distributional and asymptotic properties are given. To that end, a method based on characteristic functions (CFs) was used. Various procedures for the estimation of model parameters, asymptotic properties, and numerical simulations of the obtained estimators are also investigated. Finally, as an illustration of the practical application of the GSB process, an analysis is presented of the dynamics and stochastic distribution of the infected and immunized population in relation to the disease COVID-19 in the territory of the Republic of Serbia. [ABSTRACT FROM AUTHOR]

Technological development has brought humanity to the era of an information society in which information is the main driver. This implies existing large amounts of data from which knowledge should be extracted. In this sense, artificial intelligence represents a trend applied in many areas of human activity. This paper is focused on ensemble modeling based on the use of several machine learning algorithms, which enable the prediction of the risk to human health due to the state of atmospheric factors. The model uses two multi-agents as a technique of emergent intelligence to make a collective decision. The first agent makes a partial decision on the prediction task by learning from the available historical data. In contrast, the second agent does the same from the data available in real-time. The proposed prediction model was evaluated in a case study related to the city of Niš, Republic of Serbia, and showed a better result than each algorithm separately. It represents a reasonable basis for further upgrading both in the scope of different groups of the atmospheric parameters and in the methodological sense, as well as technically through implementation in a practical web citizen service. [ABSTRACT FROM AUTHOR]

Entrepreneurial ecosystems are the main driver of the widespread trend of digitalization, and they open opportunities for the advancement of the digital economy. The digital economy makes its progress through innovative enterprises that can ensure global progress. In order to effectively use the opportunities that open up the process of digitalization, information is needed on how much the Republic of Serbia is able to support the process of discovering entrepreneurship, which is stimulated by digitalization, which is the subject of this paper. The aim of this paper is to analyze the digital entrepreneurial system of the Republic of Serbia, as well as to identify indicators that hinder the development of this system, using appropriate instruments and methods that will be presented in detail in the paper. The results have demonstrated that the starting point for improvement of the digital entrepreneurial system is in the field of finance, with a particular focus on companies in the startup and stand-up phases. Furthermore, a comparative analysis will showcase the digital entrepreneurial system of the Republic of Serbia and the member states of the European Union, where it will be seen that the digital entrepreneurial system of the Republic of Serbia is lagging behind the member states of the European Union in its growth and development. The results will serve as the starting point for policymakers to improve the process of digitalization and the digital entrepreneurial system as a whole. The results show the starting point for the improvement of entrepreneurship in the Republic of Serbia, that is, how small, and medium-sized enterprises can be encouraged on the path to their successful management. [ABSTRACT FROM AUTHOR]

The main motivation to conduct the study presented in this paper was the fact that due to the development of improved solutions for prediction risk of bleeding and thus a faster and more accurate diagnosis of complications in cirrhotic patients, mortality of cirrhosis patients caused by bleeding of varices fell at the turn in the 21th century. Due to this fact, an additional research in this field is needed. The objective of this paper is to develop one prediction model that determines most important factors for bleeding in liver cirrhosis, which is useful for diagnosis and future treatment of patients. To achieve this goal, authors proposed one ensemble data mining methodology, as the most modern in the field of prediction, for integrating on one new way the two most commonly used techniques in prediction, classification with precede attribute number reduction and multiple logistic regression for calibration. Method was evaluated in the study, which analyzed the occurrence of variceal bleeding for 96 patients from the Clinical Center of Nis, Serbia, using 29 data from clinical to the color Doppler. Obtained results showed that proposed method with such big number and different types of data demonstrates better characteristics than individual technique integrated into it. [ABSTRACT FROM AUTHOR]

The aim of this paper is to evaluate the relative efficiency of commercial banks in three developing countries in Europe (North Macedonia, Serbia, and Croatia) in the period from 2015 to 2019, and to provide targets for improvement for the inefficient banks by using DEA. The variables are selected under the income-based approach. Based on the output-oriented BCC model, unusual results are obtained for a few commercial banks in each country, that is, they are BCC relative efficient, which is contrary to the real situation. In order to identify outliers that can affect the efficiency results, a super-efficiency procedure is applied so that banks with a super-efficiency score higher than 1.2 (outliers) or for which a feasible solution was not found are considered in detail and removed, and then the output-oriented BCC model is rerun. Based on the obtained results, the Macedonian commercial banking system shows the highest efficiency (91.1%), followed by the Croatian (90.9%) and the Serbian (81.9%) banking system. The estimated targets for improvement of the inefficient commercial banks could help their top bank management in better resource allocation and making fact-based and faster decisions by which they can improve the operation of the banks they lead and contribute to the stability of the financial system. [ABSTRACT FROM AUTHOR]