Journal of Economics

Journal Editors and Introduction

Mila Mitreva — 2025-06-26

ANALYSIS OF THE POSITIONING OF TRADING COMPANIES IN SERBIA USING THE TODIM METHOD

Radojko Lukic — 2025-06-26

Researching the positioning of trading companies globally and in each country, which means in Serbia, is a very challenging issue. In doing so, various methods of multi-criteria decision-making are increasingly being used. This provides a realistic basis for improving the positioning of a specific trading company by applying relevant measures. This study investigates the positioning of trading companies in Serbia using the TODIM method. The results of this study show that the top five trading companies in Serbia include: Delhaize Serbia, Lidl Serbia, Mercator-S, Nelt CO and Phoenix Pharma DOO. In Serbia, Delhaize Serbia ranks first in overall performance. The worst-positioned trading company is DOO Vimeksim SRB Novi Sad. To improve the positioning of trading companies in Serbia, it is necessary to manage business income, profit, assets, capital, and human resources as efficiently as possible. In this direction, it is important to adapt as adequately as possible to dynamic business changes. The function of this is the digitization of the entire business.

Is MBI10 the suitable index for the Macedonian Stock Exchange - Аn overview of MSE

Elena Veselinova — 2025-06-26

This research paper investigates the suitability of the MBI10 index for the Macedonian Stock Exchange (MSE) by providing a comprehensive analysis of the exchange's structure, stock liquidity, and the performance of the index in comparison to regional and global benchmarks. The paper begins with an introduction to the MSE, detailing the liquidity of the stocks listed, which is critical for understanding market efficiency and investors’ confidence. A thorough analysis of the MBI10 index follows, evaluating its construction, historical performance, and volatility in relation to other significant regional and worldwide indices. Additionally, the research segments the stocks listed on the MSE by sector, proposing new tailored indices that could better reflect the diverse economic activities present within the market. The research pays particular attention to the effects of key events that have impacted stock valuations, including the
COVID-19 pandemic, the ongoing Ukraine-Russia war, the introduction of new trading days, and significant mergers and acquisitions (M&A). By examining these elements, we aim to assess the resilience and adaptability of the MBI10 in capturing market dynamics and investor sentiments in an evolving economic landscape. The study employed various quantitative methods, including beta calculations, regression analysis, seasonality analysis, and market ratios, to evaluate the valuation of stocks and the suitability of the index MBI10. Ultimately, this paper contributes to the ongoing
discussion regarding the effectiveness of the MBI10 as a representative index for the MSE, providing valuable insights for investors, policymakers and market analysts.

Forecasting Industrial Production in the Eurozone: A Scenario-Based Analysis Using Macroeconomic Indicators

Vesna Georgieva Svrtinov — 2025-06-26

This study develops a scenario-based forecasting framework to predict monthly industrial production in the Eurozone using key macroeconomic indicators: inflation (HICP), unemployment, and the business climate index. Recognizing the limitations of traditional quarterly GDP models, which provide less timely data and lower forecasting frequency, the study focuses on high-frequency indicators that can enable more responsive and immediate forecasts. By utilizing Eurostat data from January 2000 to January 2025, this research explores how monthly economic indicators can be leveraged for accurate and adaptive predictions. The forecasting process applies PyCaret, an automated machine learning library in Python, to compare and select the most effective regression model from eleven tested algorithms. The K Neighbors Regressor was identified as the best-performing model, with a Mean Absolute Error (MAE) of 2.30 and a Root Mean Squared Error (RMSE) of 2.68, showing robust accuracy in industrial production forecasting. The model is then applied to simulate two macroeconomic scenarios: an optimistic scenario with lower inflation and improved sentiment and a pessimistic scenario with higher inflation and weakened sentiment, all while keeping the unemployment rate stable. The results highlight that industrial production is more sensitive to adverse macroeconomic shocks than positive ones, underscoring the critical role of inflation and business sentiment as short-term economic predictors. This study demonstrates the potential of combining high-frequency data with automated forecasting techniques. It offers a scalable and actionable approach for policymakers, analysts, and businesses who seek to monitor economic activity under varying economic conditions and uncertainty. The findings emphasize the importance of dynamic scenario analysis in forecasting, providing a flexible tool for real-time economic monitoring.

Implementation of IFRS 16 and Its Effect on Earnings Management

Spase Dameski — 2025-06-27

The adoption of IFRS 16 – Leases has introduced significant changes to financial reporting by requiring companies to recognize lease liabilities on their balance sheets. This shift has potential implications for earnings management practices, as companies may adjust financial reporting strategies in response to the new standard. This study explores the possible effects of IFRS 16 on earnings management, focusing on areas such as income smoothing, financial
ratios, and managerial discretion. While IFRS 16 aims to enhance transparency and reduce off-balance-sheet financing, its actual impact on earnings management remains a subject of discussion. By analyzing financial data before and after IFRS 16 implementation, this research seeks to understand whether firms have altered their reporting behavior in response to the standard. The findings contribute to the broader discourse on financial reporting quality, regulatory effectiveness, and managerial incentives under IFRS 16.

Transforming Human Resource Management in Healthcare: The Role of Artificial Intelligence and Industry 5.0

Riste Temjanovski — 2025-06-27

The healthcare sector is undergoing a transformative shift driven by the integration of Artificial Intelligence (AI) and the principles of Industry 5.0. This paper explores how AI is revolutionizing Human Resource Management (HRM) in healthcare, enhancing operational efficiency, optimizing recruitment and talent acquisition, and fostering employee engagement. Industry 5.0 introduces a human-centric approach that emphasizes collaboration between humans and advanced technologies, prioritizing employee well being and creating a more resilient workforce. This study also highlights the experiences of the Western Balkans, where regional adoption of AI in healthcare HRM has demonstrated significant improvements, including reduced recruitment times, enhanced workforce efficiency, and alignment with European Union digital health standards. Through a comprehensive review of current literature, case studies, and statistical data, this paper examines the benefits, challenges, and future implications of AI-driven HRM systems, with a particular focus on predictive analytics, personalized employee development, and proactive workforce planning. It further addresses critical challenges such as data privacy, ethical considerations, and the need for robust governance frameworks to ensure transparency and fairness in AI-driven decision-making. The findings reveal that successful integration of AI and Industry 5.0 principles in healthcare HRM not only enhances organizational agility but also improves public health outcomes, positioning healthcare providers to navigate the complexities of an evolving global healthcare landscape.

Strategic Event Management and Promotion Enhancing Audience Engagement for Radio Stations

Monika Arsova — 2025-06-27

Radio stations must adopt innovative strategies to capture and retain audience attention in an increasingly competitive media landscape. This paper explores the role of strategic event management and promotion as vital tools for enhancing audience engagement in the radio broadcasting sector. By examining contemporary promotional techniques, on-ground and virtual event initiatives, and the integration of digital media platforms, the study highlights how radio stations can create interactive, memorable experiences that strengthen listener loyalty and brand presence. Drawing on case studies, industry reports, and audience behaviour analysis, the research identifies best practices and emerging trends in event-based audience engagement. The findings suggest that well-orchestrated events and targeted promotional campaigns amplify audience participation and contribute to the station’s cultural
relevance and commercial success. The paper concludes by offering strategic recommendations for radio managers and event coordinators aiming to optimize engagement through dynamic, audience-centric programming.

Robots as a catalyst for Marketing automation: Potential and limitations

Valentina Spasova — 2025-06-27

In a world full of technological advancements, change is truly inevitable in every
segment of everyday life. Marketing is synonymous with innovation and change. Technological
advancements and digital transformation have fundamentally changed the way businesses
operate. Rapid automation and the integration of artificial intelligence (AI) have become
fundamental strategies for optimizing and redefining business models. Modern machine
intelligence, including robots, significantly impacts the lives of people around the world. Robots
are increasingly being integrated into marketing as part of innovative solutions to improve
services and improve efficiency. The use of robots in marketing opens up new opportunities for
automation, personalization, and improving the user experience. In this context, it is necessary
to critically analyze the role that robots play in the implementation of marketing and their ability
to transform the conventional business model. This paper aims to explore the effects of
implementing robots in marketing processes, focusing on their impact on efficiency, productivity,
and customer satisfaction. The research will be based on primary and secondary data sources
in order to collect relevant information to meet the research objectives. The data analysis shows
that adaptation to new technologies is inevitable and should be realized gradually. The results
indicate that robots have both positive and negative effects in the implementation in marketing.
Although there are concerns about their impact on jobs, robots are proving to be efficient,
reliable and innovative tools for improving business processes.