The knowledge that is based on science, technology, engineering, and mathematics is the basis for the development of any country. Less developed countries lack experts in these areas. Therefore, the ENABLE-BIH project (Enhancing and Advancing Basic Learning and Education in Bosnia and Herzegovina) was introduced in Bosnia and Herzegovina, which aims to improve the situation in the education sector. This study included the Public Institution “Ninth Elementary School” in Brcko District of Bosnia and Herzegovina in which this project was implemented. The study included a total of 125 students from this school. The aim of this study is to examine the difference between attitudes about STEM from the point of view of gender differences and the age of students. After the data were collected, the statements were grouped into appropriate factors using factor analysis. The factor analysis showed that five factors stand out in this research. The results of multiple regression analysis showed that there is no difference between students ‘attitudes regarding gender differences, while there is a difference regarding students’ age. The results of this research showed that the ENABLE-BIH project delivered good results and suggests the importance of implementing similar projects in the future.

This study presents the MCDM model created for the selection of a dump truck for the needs of the army engineering units, based primarily on the truck’s construction features and purchasing and maintenance costs. In this study was used the Methodology of Additive Weights (LMAW) in Fuzzy surrounding for determination of weight coefficients of criteria, while for the selection of the optimal alternative (for a dump truck) it was used the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method, modified by interval grey numbers. Input data for this methodology were obtained by engaging experts. Finally, the analysis was made of the sensitivity of output results of the proposed MCDM methodology to the change of weight coefficients of criteria, as well as the comparison of the obtained results with the results of other methodologies. In the conclusion, the proposed model showed stability but it was sensitive to weight coefficients change which should be taken into account by defining the same by experts.

Abstract The COVID-19 virus pandemic had an impact on all segments of life, including dally operations of companies. Companies had to adapt to market developments and change their business philosophy in order to survive in the market. This paper showed how the crisis caused by the COVID-19 virus pandemic affects the business of companies. This study aims to examine the business performance indicators (BPI) of companies listed on the Warsaw Stock Exchange (WSE) before and during the COVID-19 pandemic with a focus on corporate social responsibility (CSR). In order to examine this, a multivariate regression analysis was conducted. The findings show that there were no changes in the BPIs before and during the COVID-19 pandemic in companies in relation to CSR strategy. The only exception was found in the group of CSR companies that reported a lower profit margin during the COVID-19 pandemic. Companies with better BPIs may be willing to introduce CSR principles into their strategy and during the pandemic, intangibles influenced CSR strategy in a negative way. The limitations of the article are related to the study of only one market. Identified relationships allowed for a better understanding of the application of social responsibility principles among enterprises in Central Europe.

Urban logistics implementation causes environmental pollution; therefore, it is necessary to consider the impact on the environment when carrying out such logistics. Electric vehicles are alternative vehicles that reduce the impact on the environment. For this reason, this study investigated which electric vehicle has the best indicators for urban logistics. An innovative approach when selecting such vehicles is the application of a fuzzy–rough method based on expert decision making, whereby the decision-making process is adapted to the decision makers. In this case, two methods of multi-criteria decision making (MCDM) were used: SWARA (stepwise weight assessment ratio analysis) and MARCOS (measurement alternatives and ranking according to compromise solution). By applying the fuzzy–rough approach, uncertainty is included when making a decision, and it is possible to use linguistic values. The results obtained by the fuzzy–rough SWARA method showed that the range and price of electric vehicles have the greatest influence on the selection of an electric delivery vehicle. The results of applying the fuzzy–rough MARCOS method indicated that the Kangoo E-Tech Electric vehicle has the best characteristics according to experts’ estimates. These results were confirmed by validation and the application of sensitivity analysis. In urban logistics, the selection of an electric delivery vehicle helps to reduce the impact on the environment. By applying the fuzzy–rough approach, the decision-making problem is adjusted to the preferences of the decision makers who play a major role in purchasing a vehicle.

The selection of unmanned aerial vehicles for different purposes is a frequent topic of research. This paper presents a hybrid model of an unmanned aerial vehicle (UAV) selection using the Defining Interrelationships Between Ranked criteria (DIBR), Full Consistency Method (FUCOM), Logarithm Methodology of Additive Weights (LMAW) and grey - Evaluation based on Distance from Average Solution (G-EDAS) methods. The above-mentioned model is tested and confirmed in a case study. First of all, in the paper are defined the criteria conditioning the selection, and then with the help of experts and by applying the DIBR, FUCOM and LMAW methods, the weight coefficients of the criteria are determined. The final values of the weight coefficients are obtained by aggregating the values of the criteria weights from all the three methods using the Bonferroni aggregator. Ranking and selection of the optimal UAV from twenty-three defined alternatives is carried out using the G-EDAS method. Sensitivity analysis confirmed a high degree of consistency of the solutions obtained using other MCDM methods, as well as changing the criteria weight coefficients. The proposed model has proved to be stable; its application is also possible in other areas and it is a reliable tool for decision-makers during the selection process.

This research focuses on the use of electric vehicles (EVs) to transport visitors and cargo within Bosnia and Herzegovina’s Kozara National Park. Reduced air pollution and the preservation of natural resources are required to help protect this aerial spa. Together with the expert employees of this NP, the EV that would best suit their needs was chosen. The process of decision-making combines subjective and objective methods. Employees first chose the criteria and alternatives and then weighed their importance. On that occasion, Z-numbers were used to include uncertainty in the decision, because it is not always possible to make decisions with complete certainty. Furthermore, the weight of these criteria was determined using the fuzzy PIPRECIA (PIvot Pairwise Relative Criteria Importance Assessment) method. Range (C1) became the most important criterion, followed by vehicle cost (C2), and the technical specifications of these EVs were used to compare them. Because these specifications vary, a rough set was used in which the minimum and maximum EV characteristics were taken based on specific criteria. To rank the alternatives, the R-CRADIS (Rough Compromise Ranking of Alternatives with Distance to Ideal Solution) method was used. According to the research results, the Mercedes eVito Tourer 90 kWh is the highest ranked EV and the validation of the results confirmed these findings. According to the research results, the Mercedes eVito Tourer 90 kWh is the highest ranked EV and the validation of the results confirmed these findings. The sensitivity analysis revealed that if criterion C1 is not as important, the other EVs are ranked higher. This research`s methodology has demonstrated flexibility, therefore it is recommended for use in similar research.

This research paper aims to assess the sustainable competitiveness of Balkan countries. Sustainable competitiveness was measured based on the indicators in The Sustainable Competitiveness Report from 2022, published by Solability. According to this report, sustainable competitiveness is evaluated using six grouped criteria. In this paper, the competitiveness of the Balkan countries was assessed through the application of the multicriteria analysis methods Entropy and MARCOS (Measurement Alternatives and Ranking according to the Compromise Solution). The weight of each criterion was determined using the Entropy method. The results highlighted that the most significant criteria were Natural Capital and Resource Efficiency & Intensity, which carried the highest weight, whereas the Social Cohesion criterion was of lesser importance, represented by the lowest weight. Using the MARCOS method, the Balkan countries were ranked, with Greece securing the top position, closely followed by Albania, while North Macedonia exhibited the weakest performance. Sensitivity analysis further substantiated these findings. The outcomes of this study significantly contribute to the academic understanding of sustainable competitiveness and provide valuable practical insights for policymakers and stakeholders interested in advancing sustainable development efforts in the Balkan region.

The purpose of the present study is to propose an interval-valued type 2 fuzzy set (IT2FS)-based analytic hierarchy process (AHP) framework to unfold the critical challenging factors influencing the sustenance and growth of the Indian tea industry. The current work follows an expert opinion-based group decision-making approach. The challenging factors have been identified through a literature review and finalized after a pilot study based on the opinions of professionals, consumers, and experts. Finally, the critical challenging factors and sub-factors have been figured out through analysis of the responses of the experts. To offset the subjective bias, an IT2FS-based granular analysis has been carried out. The findings reveal that market diversification and productivity are the central issues. Additionally, it is important to give attention to improving the quality of the products, increasing the use of modern technology and organic farming, and developing a variety of products. The result shows a considerable level of consistency in the group decision-making (CR < 0.1) for all pairwise comparisons. The present work shall be of use to formulate appropriate strategies and policy decisions. It shows a robust application of IT2FS-AHP for complex decision-making in real life.

The sustainable development of mountain tourism is crucial for preserving the delicate ecosystems and resources found in these unique landscapes. This research paper investigates the sustainability of mountain lodges, which serve as essential facilities for delivering mountain tourism services. To assess sustainability, expert decision making involving eight selected experts was employed. A hybrid approach combining the IMF SWARA (IMproved Fuzzy Step-wise Weight Assessment Ratio Analysis) method with Fuzzy Dombi Aggregation Operators was utilized to determine the weights of various sustainability criteria. The IMF SWARA method assigned initial weights based on expert input, which were subsequently adjusted using Fuzzy Dombi Aggregation Operators. The findings highlight the significance of two key criteria as per expert evaluations: the quality of the services offered (C21) and the preservation of natural resources (C15). To rank and evaluate the mountain lodges, the fuzzy CRADIS (Compromise Ranking of Alternatives from Distance to Ideal Solution) method was employed, ultimately identifying Zabrana (ML6) as the top-ranked mountain lodge. The validity of these results was confirmed through result validation and sensitivity analysis. This research contributes by providing insights into the current state of mountain tourism and offering guidelines for enhancing the overall mountain tourism experience through the integration of fuzzy methods.

This paper presents a Fuzzy Inference System (FIS) designed to comprehensively assess challenges, risks, and threats. In the realm of security and defense, defining these elements is inherently uncertain and complex. The paper addresses this challenge by integrating fuzzy logic into the model. As a pivotal instrument for decision-making, the model not only facilitates the precise identification of challenges, risks, and threats but also provides vital support for the strategic and doctrinal document development process. The methodology proves instrumental in reconciling divergent perspectives, aligning theoretical intricacies with practical applications. By effectively capturing the nuanced interplay between variables, the model offers a dynamic framework that enhances the accuracy and efficiency of security-related decision-making.

The Analytic Hierarchy Process (AHP) method is one of the oldest and mostly used multi-criteria decision-making methods. In addition to the development of a large number of other methods, the AHP method is still widely applied. More and more often, this method is being modified by the application of various mathematical tools dealing with the consideration of uncertainty and indeterminacy. This paper presents an approach to the modification of the AHP method using triangular interval fuzzy numbers. In this approach, the confidence interval of the fuzzy number describing the comparison of criteria differs from one comparison to another. It depends on the opinion of the decision makers/experts, respectively, on their certainty in the comparison they make. The modification of the method is presented on the problem of selecting the course of navigation of vessels in flooded areas, based on the risk assessment of each predicted course.

As technology continues to shape the landscape of education, the need for effective evaluation frameworks for sustainable technology-enhanced learning (TEL) becomes increasingly vital. This study presents an expert-opinion-based evaluation framework, utilizing Z-numbers and the fuzzy logarithm methodology of additive weights (LMAW), to assess the sustainability of TEL approaches. This framework focuses on four main criteria: cloud services compliance, cloud M-Learning essentials, system and technological advancement, and organizations management readiness. Additionally, it incorporates 17 sub-criteria to provide a comprehensive evaluation of the system. Drawing on the expertise of subject matter specialists, the evaluation framework utilizes Z-numbers to account for the inherent uncertainty and imprecision in expert judgments. The fuzzy LMAW is applied to calculate the overall scores for each criterion and sub-criterion, enabling a quantitative measure of their importance in the evaluation process. The findings of this study will contribute to the development of a robust and scientifically rigorous evaluation framework for sustainable TEL. By incorporating expert opinions and employing Z-LMAW, decision-makers and stakeholders can objectively assess the sustainability of TEL systems. This framework holds promise for informing the design and implementation of strategies to enhance the quality, compliance, and technological advancements in TEL environments.

The evolving customer demands have significantly influenced the operational landscape of agricultural companies, including the transformation of their supply chains. As a response, many organizations are increasingly adopting green supply chain practices. This paper focuses on the initial step of selecting a green supplier, using the case study of the Semberka Company. The objective is to align the company with customer requirements and market trends. Expert decision making, grounded in linguistic values, was employed to facilitate the transformation of these values into fuzzy numbers and subsequently derive rough number boundaries. Ten economic-environmental criteria were identified, and six suppliers were evaluated against these criteria. The fuzzy rough LMAW (Logarithm Methodology of Additive Weights) method was employed to determine the criteria weights, with emphasis placed on the quality criterion. The fuzzy rough MABAC (Multi-Attributive Border Approximation Area Comparison) method was then utilized to rank the suppliers and identify the top performer. The validity of the results was established through validation techniques and sensitivity analysis. This research contributes a novel approach to green supplier selection, employing the powerful tool of fuzzy rough sets. The flexible nature of this approach suggests its potential application in future investigations. The limitation of this study is more complicated calculations for the decision maker. However, this approach is adapted to human thinking and minimizes ambiguity and uncertainty in decision making, and in future research, it is necessary to combine this approach with other methods of multi-criteria analysis.

Green supplier selection is always one of the most important challenges in all of supply chain management, especially for production companies. The purpose is to have reliable suppliers which can fulfill all requests and be flexible in any supply chain stage. The aim of this paper is to create an adequate and strong MCDM (multicriteria decision making) model for the evaluation and selection of suppliers in a real environment. The main contribution of this study is proposing a novel fuzzy–rough MCDM model containing extension stepwise weight assessment ratio analysis (SWARA) and additive ratio assessment (ARAS) methods with fuzzy–rough numbers (FRN). The integrated FRN SWARA–FRN ARAS model was implemented in a case study of eco-friendly material production. The FRN SWARA method was used to calculate the weights of 10 green criteria, while using FRN ARAS, 6 suppliers were evaluated. The results of the applied model show that supplier S3 received the highest ranking, followed by supplier S2, while supplier S5 performed the poorest. In order to verify the strengths of the developed fuzzy–rough approach, we created a comparative analysis, sensitivity analysis, and dynamic matrix, which confirm the robustness of our model.