Ilija Stojanović

This study aims to identify the determinants of bank financial performance in GCC countries (Bahrain, KSA, Kuwait, Oman, Qatar, and the UAE) using 450 bank-year observations from 2014 to 2024. The analysis focuses on two profitability indicators: Return on Assets (ROA) and Return on Equity (ROE). The methodology comprises descriptive statistics, one-way ANOVA with post hoc tests, multiple regression analysis, and tests of between-subjects effects. The results show significant cross-country differences in profitability across the GCC region. The ANOVA findings confirm differences in the means and variances of ROA and ROE. The regression analysis indicates that the net interest margin has a positive and significant effect on performance, while leverage, credit risk, and loan intensity have negative effects. The tests of between-subjects effects confirm that country-level differences persist after controlling for financial determinants. The findings indicate structural differences within the GCC banking sector.

Artificial intelligence (AI) plays a dual role in the environmental sustainability transition: while AI can support decarbonization and resource optimization, its development and deployment also impose computational and environmental demands. This review synthesizes 281 peer-reviewed studies retrieved from EBSCOhost and published between July 2024 and July 2026. Following PRISMA guidelines, bibliometric and thematic analyses were conducted to address three research questions: computational efficiency and the environmental footprint of AI (RQ1); AI applications for decarbonization and green transformation (RQ2); and rebound effects, structural trade-offs, and governance (RQ3). Here, three thematic clusters were identified. Green AI: Computational Footprint & Model Efficiency dominates the literature (n = 175; 62.28%), followed by AI for Green: AI-Driven Decarbonization & Green Transition (n = 59; 21.00%) and The Paradox, Rebound Effects & Governance (n = 47; 16.73%). The findings show progress in reducing model-level computational demands and applying AI to energy, industrial, mobility, and resource management systems. However, they also indicate that improvements in relative efficiency do not translate into reductions in absolute environmental impact, particularly when deployment scale, rebound effects, life-cycle emissions, and institutional conditions are taken into account. The paper argues that AI sustainability should be evaluated from a system-level perspective that integrates computational efficiency, environmental utility, deployment scale, and governance.

This paper investigates multi-attribute decision-making (MADM) problems within the framework of spherical fuzzy (SF) sets for the evaluation and selection of artificial intelligence (AI) technologies in smart agriculture. The increasing complexity and uncertainty associated with agricultural decision-making, driven by environmental variability, technological advancements, and sustainability requirements, necessitate robust and flexible decision-support methodologies. To address this challenge, the Aczel–Alsina (AA) t-norm and t-conorm are incorporated into the spherical fuzzy environment to extend existing aggregation mechanisms. Accordingly, two novel aggregation operators, namely the Spherical Fuzzy Aczel–Alsina Geometric (SFAAG) operator and the Spherical Fuzzy Aczel–Alsina Averaging (SFAAA) operator, are proposed, and their fundamental mathematical properties, including idempotency, monotonicity, boundedness, and stability, are thoroughly investigated. Furthermore, a MADM framework based on the proposed operators is developed to support the selection of AI-based smart agriculture technologies under spherical fuzzy information. To demonstrate the applicability of the proposed approach, a case study involving the evaluation of five AI technologies, namely smart irrigation systems, crop disease identification systems, agricultural drone surveillance, smart harvesting robots, and AI-based weather forecasting systems, is conducted using productivity improvement, cost reduction, environmental sustainability, and technical reliability as evaluation criteria. Comparative analyses with existing aggregation-based decision-making methods confirm the effectiveness, robustness, and reliability of the proposed framework in handling complex and uncertain decision-making environments. The results indicate that the proposed approach provides an efficient and reliable decision-support tool for the assessment and selection of AI technologies in smart agriculture.

This research aims to conduct a strategic analysis of rural tourism in Zadar County using the SWOT method. Tourism activities in the county are mostly focused on the sea and sun, while rural areas remain underdeveloped. Strengthening tourism in rural settlements is therefore important for achieving sustainable development across both coastal and inland areas of the county. In order to determine which advantages rural tourism should be built on, which shortcomings require urgent correction, which potentials should be used and which threats should be mitigated, the weight of the SWOT analysis statements will be determined using the SiWeC (Simple Weight Calculation) method. Expert opinion will be used for this purpose. The results of the research will contribute to the improvement of rural tourism in Zadar County.

The specificity of the business of agro-food companies is that their products have little or no impact on the environment. However, environmental pollution of these products is caused by the use of packaging. Therefore, it is necessary to apply the principles of the circular economy in the business of companies. Applying green packaging that has little or no impact on the environment helps in preserving the environment. Companies usually purchase packaging from suppliers and therefore, it is necessary to choose the right supplier from which to purchase green packaging to support the implementation of the circular economy. The aim of this research is to select a green packaging supplier for company X in order to influence the development of a circular economy in the company’s business. Based on this, the following research question is considered in this paper: how can the selection of a green packaging supplier influence the implementation of a circular economy at company X? The research covers ten criteria used in this selection, with which eight suppliers were observed. Because every decision-making process in the economy is characterized by risk and insecurity that affects the uncertainty in decision-making, an intuitionistic fuzzy set (IFS) was used. Determining the importance of weights was performed directly based on the ratings of the decision-maker (DM) and the steps of the SiWeC (Simple Weight Calculation) method, as well as using the Entropy method. The compromise results of these methods showed that the most important criteria for assessing the life cycle of packaging are transparency and ethics in business. The ranking of suppliers was carried out using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method and its results showed that supplier 5 is the first choice for establishing long-term cooperation in the procurement of green packaging.

The development of technology has influenced agricultural production and the establishment of the Agriculture 4.0 system in practice. This research is focused on the selection of equipment and machinery suppliers for the needs of the MAMEX Company. When selecting suppliers, an approach based on the application of an intuitionistic fuzzy set for decision-making was used. This approach allows the uncertainty present in decision-making to be incorporated, considered, and, hopefully, reduced in order to make a final decision on which of the observed suppliers is the most suitable for this company. Ten criteria were used that enable the application of sustainability in the supply chain. Eight local suppliers of equipment and machinery were observed with these criteria. The results obtained by applying the SWARA (Step-wise Weight Assessment Ratio Analysis) method showed that the most important criterion for selecting suppliers is the reliability and quality of equipment and machinery, while the results of the CORASO (COmpromise Ranking from Alternative Solutions) method showed that the SUP2 supplier is the best choice for establishing partnership relations with the MAMEX company. This supplier should help the MAMEX company improve its business and achieve better results in the market. The contribution of this research is to improve the application of intuitionistic fuzzy sets in decision-making, and to emphasize the importance of equipment and machinery in agricultural production in the Agriculture 4.0 system.

The objective of this study was to provide decision-making assistance in selecting electric vehicles (EVs). The multi-criteria decision-making methods (MCDM), criteria importance through inter-criteria correlation (CRITIC) and evaluation by distance from ideal solution of alternatives (EDISA), along with the technical specifications of EVs, were employed to facilitate the decision on purchasing an EV. A total of 14 minivans were analysed based on 10 criteria. The findings from the CRITIC method indicated that the most significant criteria are battery charging and vehicle consumption. The EDISA method indicated that EV11 exhibited the best characteristics and represented a prudent purchase decision. Nevertheless, the ultimate decision must consider additional factors beyond just the technical specifications, as numerous elements affect the final choice, necessitating an examination of other attributes of the EV.

More and more investments are being made in energy conversion projects from renewable energy sources (RESs), and a large number of investors are entering this sector. The focus of this study is the decision-making by the investor BD Green Energy in the Brčko District of Bosnia and Herzegovina. In order to choose the RES system that would realize this investment in the most efficient way, expert decision-making based on the fuzzy–rough approach and the Bonferroni mean operator was used. Determining the importance of the criteria was conducted using the fuzzy–rough SiWeC (simple weight calculation) method. The results of this method showed that all used criteria have similar importance for the investor. RES system selection was conducted using the fuzzy–rough CoCoSo (combined compromise solution) method. The results of this method showed that investing in photovoltaic (PV) energy is the best for the investor. This research provided guidance on how investors should make investment decisions in RES systems with incomplete information and uncertainty in the decision-making process.

Each crisis provides food for thought concerning project management, and lessons can be drawn for the future regarding risk mitigation. In that milieu, there has been a lot of scientific discussion about the effects of the COVID-19 pandemic on project implementation. That discussion pointed to numerous factors that led to delays in the execution of projects. Thus, this study aims to identify the main factors that caused project delays during the pandemic. For this purpose, we used factor analysis, more precisely principal component analysis, to determine the main culprits that caused project delays. Factor analysis pointed to two primary causes of project delays during the pandemic, namely Epidemic-Related Disruptions and Supply chain disruptions. These findings indicate two basic groups of factors enabling a much more focused approach to project management should similar crises occur in the future.

This study is focused on the problem of contractor selection for the implementation of a solar power plant project to produce electricity from sustainable sources for the needs of the company Voćar. The goal of this research is to select a construction contractor to install a solar power plant using sustainability criteria. With this power plant, the company Voćar can reduce its electricity costs and contribute to the production of sustainable energy. A total of three main sustainability criteria were used, in which six auxiliary criteria were symmetrically distributed. With these criteria, six suppliers were analyzed, and expert decision making was carried out with the application of the fuzzy–rough approach. To define the weights of the criteria, the SWARA method was utilized in this study. Based on the findings of this method, the most important criteria are the ecological criteria. Using the CRADIS method, the contractors were ranked, and the results show that contractor C6 has the best results and is the first choice for implementing this project. Choosing the best supplier increases the sustainability of project implementation and the realization of the expected effects.