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Tools and Techniques for Economic Decision Analysis provides a thorough overview of decision models and methodologies in the context of business economics. Highlighting a variety of relevant issues on finance, economic policy, and firms and networks, this book is a reference source for managers, professionals, students, and academics interested in emerging developments for decision analysis.

This paper identifies the conditions under which the private pension funds generate superior retirement outcomes compared to public pension system. The research objective is to determine the probability of success of the selected investment strategies in achieving the public pension system replacement rate, and the probability of the realization of extremely unfavourable outcomes. The methodology used in this paper includes the comparative analysis of simulated financial results of the four selected investment strategies implemented in the private pension fund model and the defined retirement benefits generated within the public pension system. For the simulation of the financial results at retirement, Monte Carlo simulation technique has been used. The authors have found that the success rate of the private pension fund in achieving superior financial results in comparison to public pension system is high, but only for the contribution rates higher than 10%. At low contributions rates, the extremely aggressive strategy is the only one that generates moderate success rate. Also, the probability of realization of extremely unfavourable financial results is lowest for the conservative strategy, which suggests that for the relatively high levels of the contribution rate, it is the most appropriate option for the pension fund members.

The paper explores the relationship between the diversification of bank activities and a set of bank performance indicators by running multiple regression on panel data set of 22 operating banks in Serbia in the time period spanning the last 15 reporting years. We have found a positive influence of the degree of diversification, measured both by income composition and earning assets composition indicators, on the levels and stability of the banks’ return on equity. For ROA-related performance measures the relationship is not conclusive. We have also tested whether the presence of COVID-19 crisis challenged the observed regularity and confirmed that it has had tendency to reverse the long-term relationship.

U radu se analiziraju determinante neto kamatne marže banaka u izabranim državama Jugoistočne Evrope u periodu od 2012. do 2021. godine. U skup su uključene države sa srodnim društveno-ekonomskim obeležjima i karakterom finansijskog sistema. Cilj rada je da identifikuje zajedničke determinante neto kamatne marže u posmatranim bankarskim sistemima. Polazeći od prethodnih teorijskih i empirijskih istraživanja, analiza je sprovedena na bazi skupa podataka o makroekonomskim i varijablama koje obeležavaju bankarski sektor primenom metoda regresije sa običnim najmanjim kvadratima (pooled OLS regression), jer preliminarnim analizama podataka nisu utvrđeni uslovi za primenu panel regresije sa fiksnim ili slučajnim efektima. Prediktorske promenljive u modelu su: bruto domaći proizvod po gravi stanovnika, inflacija, devizni kurs, realna kamatna stopa, mere koncentracije, veličine, kapitalizacije, likvidnosti bankarskog sektora i prisustva kreditnog rizika u bankarskom sektoru. Kao kriterijumska promenljiva korišćena je neto kamatna marža (NIM). Rezultati su potvrdili da je model statistički značajan, i da varijaciji kriterijumske promenljive značajno doprinose devizni kurs, realna kamatna stopa, stepen koncentracije i veličina bankarskog sektora. Značajne varijable su pokazale i očekivani pravac uticaja na kretanje NIM. Rast deviznog kursa, realne kamatne stope, stepena koncentracije i veličine bankarskog sektora praćen je povećanjem neto kamatne marže banaka u analiziranim državama za posmatrani period, uz nepromenjene vrednosti ostalih varijabli. Analiza nije potvrdila uticaj ostalih prediktora na NIM.

The paper discusses market trends and analyzes the regularities that appear on the Serbian national housing market and regional submarkets. It is assumed that, apart from the Common Market driving forces, the market for newly constructed houses and the market for the existing housing stock behave like two separate segments of the housing market with the imperfect adjustment of prices. The prime focus of the analysis is on the divergence between the prices in those two segments, with a special interest in the process of mutual adjustments. Granger causality tests are employed in order to reveal whether there is a causal relationship between the price indices in those two segments and it has been found that there is a causality relation between the existing housing market and the newly constructed house market prevailing among the regional submarkets. The same methodology is applied to test if there is any such causality between the regional markets. The results have confirmed a likely influence of the Belgrade new construction market on the other regional markets. The findings will help understand the process of price adjustments between the two market segments and will lead to policy recommendations.

This study investigates the spatial and temporal dynamics of housing prices in Serbia, addressing the critical need to understand the drivers of real estate prices and their implications for economic and social welfare. Employing a panel data analysis approach on a unique dataset covering 24 distinct urban areas in Serbia from 2011 to 2021, we examine the relevance of diverse economic, demographic, and infrastructural indicators, providing novel insights within a developing country context. Our findings reveal that the housing market stock-flow model effectively predicts housing price appreciation trends, explaining over 60 percent of variation in property prices. Notably, disparities in labour income, captured by average wages and registered employment rates, emerge as significant determinants of real estate prices, underlining socio-economic disparities within Serbian cities. Housing prices exhibit a positive response to the population/housing stock ratio, suggesting higher prices in cities experiencing faster population growth relative to housing supply. Intensified construction is associated with elevated housing prices. Additionally, we find positive association between the inflation variable and housing prices, underlining real estate’s potential as an inflation hedge. Public service provision and infrastructural amenities also emerge as contributors to higher housing prices in urban areas, emphasizing the importance of comprehensive urban planning strategies. Our study contributes to the literature by providing specific quantitative evidence, advancing the understanding of urban housing market dynamics in developing countries. By offering nuanced insights into determinants of housing prices, our research informs policymakers and urban planners seeking to foster equitable and sustainable urban development strategies.

Serbian SMEs face mounting pressure to stay competitive, agile, and aligned with sustainability goals amid rapid digital change. This mixed-method study—12 qualitative case studies and a survey of 200 firms—examines how AI adoption supports flexible and adaptive strategic transformation. We examine how organizational context and AI readiness translate into the strategic application of AI and, in turn, sustainable development and strategic performance outcomes among Serbian SMEs. Through the AI-Driven Strategic Transformation Framework (AISTF-SME), three adoption types were identified —Traditionalists, Experimenters, and Strategic Adopters—distinguished by digital maturity, strategic integration, and sustainability orientation. While AI is primarily deployed for operational efficiency, firms with higher AI maturity and tighter strategic alignment report stronger gains in agility, innovation, and customer experience; sustainability-oriented use cases remain limited. Key barriers include shortages of technical talent, financial constraints, and insufficient institutional support. We recommend a multi-stakeholder policy approach emphasizing sector-specific AI readiness programs, better access to funding, and stronger university–industry collaboration. The findings enrich digital transformation and sustainability research and offer practical guidance for accelerating AI adoption in transitional economies.

The purpose of the paper is to provide the ranking of Central and Eastern European cities, based on various elements of cities' smart performance. Our analysis enables the evaluation of social, economic and environmental aspects of urban life that represent the determinants of cities' competitive profiles and consequently, the positions on the ranking lists. The research is based on the data on perceptions of citizens on different aspects of urban quality, provided by the Eurostat's Urban Audit Perception Survey. For the assessment of various hierarchically structured indicators of cities' smart performance, a multi-criteria analysis model is developed, combining the AHP (Analytic Hierarchy Process) for determining the relative importance of criteria and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method of ranking. The main finding of the paper implies that direct perceptions of citizens on the overall life satisfaction in the analyzed European cities are not influenced by their smart performance. The comparison of ranks obtained by the constructed multi-criteria model and perceived satisfaction of life indicates a rather weak relation.

Ensuring traffic safety within urban public transport systems is essential for achieving sustainable urban development, particularly in densely populated metropolitan areas. This study investigates the integration of artificial intelligence (AI) technologies to enhance safety performance in closed public transport environments, with a focus on the city of Belgrade as a representative case. The research aims to evaluate how AI-enabled systems can contribute to the early detection and reduction of traffic incidents, thereby supporting broader goals of sustainable mobility, infrastructure resilience, and urban livability. A hybrid methodological framework was developed, combining computer vision, supervised machine learning, and time series analytics to construct a real-time risk detection platform. The system leverages multi-source data—including video surveillance, onboard vehicle sensors, and historical accident logs—to identify and predict high-risk behaviors such as harsh braking, speeding, and route adherences across various public transport modes (buses, trams, trolleybuses). The AI models were empirically assessed in partnership with the Public Transport Company of Belgrade (JKP GSP Beograd), revealing that the most accurate models improved incident detection speed by over 20% and offered enhanced spatial identification of network-level safety vulnerabilities. Additionally, routes with optimized AI-driven driving behavior demonstrated fuel savings of up to 12% and a potential reduction in emissions by approximately 8%, suggesting promising environmental co-benefits. The study’s findings align with multiple United Nations Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities) and SDG 9 (Industry, Innovation, and Infrastructure). Moreover, the research addresses ethical, legal, and governance implications surrounding the use of AI in public infrastructure, emphasizing the importance of privacy, transparency, and inclusivity. The paper concludes with strategic policy recommendations for cities seeking to deploy intelligent safety solutions as part of their digital and green transitions in urban mobility planning.

The paper presents analysis of the achieved development level of the housing market in Serbia. Various factors that have shaped demand and supply are systematized and their impact over the last decade was analysed and monitored. As important ones demographic, macroeconomic and financial factors are singled out and a special importance is given to the analysis of specific historical and socio-political circumstances that have influenced the development of the housing market during the period of analysis.