Weights Of Criteria Research Articles

Green, resilient, and inclusive supplier selection using enhanced BWM-TOPSIS with scenario-varying Z-numbers and reversed PageRank

Firms need a reliable decision-making framework to select suppliers who are Green, Resilient, and embrace Inclusive Development (GRID). However, GRID supplier selection is challenged by events that are uncertain and often dependent. Such events affect supplier rankings in a fuzzy decision environment. This paper builds a GRID-based criteria system and applies an enhanced Best Worst Method (BWM) and Technique for Order Preferencing using the Similarity to the Ideal Solution (TOPSIS), with Scenario-Varying Z-numbers (SVZs) and reversed PageRank. The decision criteria are extracted from the literature and confirmed by expert judgement. The SVZs and SVZ Preference Relations (SVZPRs) are developed by combining the Concept of Stratification Theory (CST) with Z-numbers, to reflect the impact of Dependent Uncertain Events (DUEs). Reverse PageRank is then used to find the occurrence probability of the scenarios due to DUEs. BWM is applied in an SVZPR environment to obtain the weights of the decision criteria. TOPSIS, incorporating SVZs, is used to rank the suppliers. An example is used to highlight the enhanced BWM-TOPSIS, to find the priority ranking of the GRID weights. Comparative and sensitivity analyses are conducted to validate the proposed method.

Assessment of wave energy location, technology, and converter toward sustainability using integrated spherical fuzzy MCDM approach

Due to climate change, fossil fuel reduction, growing energy usage, and ecological impacts, the need for renewable energy sources is rising quickly. A large amount of energy comes from the ocean through the tide; renewable energy must be exploited. One important energy source is wave energy converters (WECs), which enhance efficiency and grow energy. This study presented a case study from three layers of the Red Sea in Egypt for selecting optimal wave energy location (WEL), wave energy technology (WET), and WEC. The spherical fuzzy set (SFS) framework deals with uncertainty and vague information in this selection process. The SFS is integrated with multi-criteria decision-making (MCDM) to deal with various criteria in each layer. This study uses two MCDM methods under the SFS environment: The Stepwise Weighted Assessment Ratio Analysis (SWARA) method and the Weighted Euclidean Distance Based Approach (WEDBA) method. The SF-SWARA method is used to compute the criteria weights in each layer. Then, the SF-WEDBA method ranks the alternatives in each layer. The main results in each layer show that the accessibility criterion, Economic factors, and energy production criterion have the highest weight in the three layers. Sensitivity analysis and comparative analysis are conducted in each layer.

A neutrosophic WENSLO-ARLON model for measuring sustainable brand equity performance

Sustainable brand equity denotes the perceived brand equity by consumers, influenced by companies' non-financial and environmental practices. To cultivate sustainable brand equity, corporations undertake green initiatives and engage in social responsibility endeavors, thereby communicating environmentally oriented messages to consumers. Companies feel the necessity to establish a competitive position in sustainability relative to their counterparts. Hence, the primary objective of this research is to conduct an inquiry aimed at identifying sustainable brand equity to meet this exigency of companies. In this context, the motivation of this study lies in treating the calculation of sustainable brand equity as a decision-making process and developing a decision support system for this purpose. The proposed decision support system for determining sustainable brand equity levels in this research has three fundamental inputsss: experts, criteria, and brands. The impact levels of experts on the decision-making process are quantified utilizing type-2 neutrosophic number (T2NN) sets. The weights of criteria are ascertained employing the weight by envelope and slope (WENSLO) method extended with T2NN sets. The ranking of sustainable brand equities is established through the innovative T2NN-based alternative ranking using two-step logarithmic normalization (ARLON) method. These methodologies are amalgamated, and the T2NN-WENSLO-ARLON hybrid model is posited as a decision support system for evaluating sustainable brand equity performance. An algorithm for T2NN-WENSLO-ARLON is formulated and elucidated alongside a case study focusing on companies operating in the cosmetics sector in Turkey. The case study identifies the “green product leadership” criterion as the most influential factor in determining sustainable brand equity. Furthermore, the cosmetic company “Misbahçe Inc.” is identified as the brand with the highest sustainable brand equity. Sensitivity and comparative analyses are conducted to ascertain the robustness of the results and the decision model. All scenario outcomes bolster the decision model. The research provides comprehensive insights into both the developed tool for calculating sustainable brand equity and develops implications for the cosmetics industry.

A non-price-based tender evaluation methodology considering tenderers’ self-confidence with partial value functions and time decay analysis

The construction industry is characterized by complex environments, adversity, fierce competition, and heavy tasks, and faces many challenges during project execution, including cost acceleration, delays, and quality issues. Tendering is a competitive procurement process used to select the main contractor or supplier. The process is often associated with high risk, and its success is strongly influenced by how it is managed. These factors make selecting a qualified and suitable contractor for a construction project a challenging and complex task. The results of a selection can have an impact on a project's performance and success; selecting unsuitable contractors can cause delays, cost overruns, low-quality work, disputes, and additional costs for the owner. Research shows that public agencies, in particular, face the challenge of selecting the most appropriate tender. Generally, public construction projects are awarded at the lowest price for financial accountability, but it is not the best option because it may damage the balance between time, cost, and quality. However, as contracting authorities recognize the benefits of evaluating tenders based on non-price criteria, they are increasingly using them instead of just price. Therefore, this study proposes an innovative formulaic scoring model by evaluating the price offer as well as eleven non-price factors that have been examined in previous studies to select the most suitable contractor for the project. This method uses partial value functions differently, and tenderers' past performance scores are modified using the time decay correction function. The most unique aspect of this study is the requirement for tenderers to determine their own suitability and risks. To validate the proposed model, a hypothetical scenario based on a real case has been used. It is evident from the results that the optimal choice depends on the weights of criteria, contractors’ self-determined offers, and past performance. Construction firms, as well as contracting authorities, can benefit greatly from the application of the proposed model.

A multi-criteria decision-making model to determine the optimal share of variable renewable energy sources in a wholesale electricity market

In recent years, the development of renewable energy technologies has gotten increasing attention due to the realization of cleaner production and high energy efficiency. Therefore, the proportion of these resources in electricity production has experienced a dramatic increase. However, with the features of intermittent nature and merit order effects, it is challenging to effectively optimize the integration of renewable energy sources (RES) into the electricity markets. To deal with the complex problem of selecting the proportionate share of non-dispatchable RES for participation in the market, this paper establishes a multi-criteria decision-making (MCDM) framework, which combines the Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW). Five criteria are defined and the corresponding mathematical model is solved by the proposed AHP-SAW method. In this way, the optimal share of renewable resources for participation in the wholesale market can be determined based on the desired criteria. Also, to compare the results obtained by the AHP-SAW method, the criteria weights are determined by Shannon entropy, and the problem is solved by the TOPSIS method. To assert the effectiveness of the proposed framework, two case studies are done. According to the results, by increasing the RES penetration level, the average price of the market decreases mildly while price volatility increases significantly . Also, the amount of forward contracts is decreased when the RES share increases. Moreover, the results show that a high amount of RES is not necessarily desirable and depends on the type of renewable resources and policymakers' priorities. The results for systems 1 and 2 indicate that the maximum allowable amount of RES are 85.359 and 38.24 MW, respectively.

Aircraft type selection using fuzzy trigonometric based OPA and RAFSI model

The availability of numerous types of aircraft and their technical capabilities are offering a wide range of alternatives. As customers have different expectations, aircraft type selection is a business strategy for the airline companies. The choice on carriers should be made in accordance with disparate dimensions such as customers' expectations, profit of the company, capacity limitations and market conditions. This study formulates aircraft type selection as a Multi Criteria Decision Making (MCDM) problem and proposes a novel model that incorporates fuzzy trigonometric norms to solve. Being differentiated from the existing models in the literature, a two-stage model is identified. In the first stage, a fuzzy trigonometric-based Ordinal Priority Approach (OPA) determines the criteria weights. In the second stage, RAFSI (Ranking of Alternatives through Functional Mapping of Criteria Subintervals into Single Intervals) is integrated to determine the optimal aircraft type. The model simulated for a case of Turkish Airline company. Sensitivity tests justify robustness of the model. Results show that among the four options, medium-scale high-qualified but not luxury aircraft is the best option.

A Novel Hybrid Gray MCDM Model for Resilient Supplier Selection Problem

The current business climate has generated considerable uncertainty and disrupted supply chain processes. Suppliers have frequently been identified as the primary source of hazards responsible for supply chain disruptions. Using a strategic approach to supplier selection that prioritizes providers with resilience features, mitigating the risk exposure inherent in supply chains is possible. This study proposes a comprehensive gray multiple-criteria decision making (MCDM) method incorporating resilience attributes to supplier selection. To determine criteria weights, the gray PSI and gray BWM methodologies were used, and to evaluate and prioritize resilient providers, the gray MCRAT and gray COBRA methodologies were applied. According to the results obtained by the suggested methodology, the supplier that demonstrated the greatest degree of resilience was determined to be the provider categorized as SPIR 4. The sequential sequence of the SPIR numbers is as follows: SPIR 5, SPIR 1, SPIR 3, SPIR 2, and SPIR 6. The data demonstrate that the developed approach produced accurate results.

Evaluation of Ergonomic Risks for Construction Workers based on Multicriteria Decision Framework with the Integration of Spherical Fuzzy Set and Alternative Queuing Method

Ergonomic risks critically impact workers’ occupational health, safety, and productivity, and thereby the sustainability of a workforce. In the construction industry, the physical demands and dynamic environment exposes workers to various ergonomic hazards. While previous research has mainly focused on postural risks, there is a need to broaden the scope to include more relevant factors and assess them systematically. This study introduces a multi-criteria decision framework integrating the Spherical Fuzzy Sets (SFSs) and Alternative Queuing Method (AQM) to evaluate and prioritize ergonomic hazards. First, SFSs are employed to quantify the linguistic expressions of experts, addressing the inherent vagueness and uncertainty. Then, an entropy-based objective weighting method is adopted to determine the criteria weights. Finally, AQM is utilized to generate the risk priority. The proposed method has been implemented in a real-life construction project, where “overexertion due to unreasonable task organization”, “hypertension and heart diseases”, and “existing WMSD record” are identified as the top three ergonomic hazards. Then, a thorough discussion of intervention strategies regarding different risk categories is presented to facilitate ergonomic interventions. This proposed decision support system can promote effective ergonomic risk management, benefiting workers’ health and well-being and contributing to the sustainable workforce development of the construction industry.

Enabling organizations to strategically manage risks in circular supply chains

AbstractCircular supply chains (CSCs) prioritize resource efficiency by reusing, remanufacturing, and recycling materials, offering environmental benefits and competitive advantages. However, they are facing various risks and organizational challenges that hinder their efficiency. To ensure long‐term sustainability, this study identifies the key risks and introduces a prioritization model for risk management in CSC strategies proposing a framework for the risk evaluation based on the set of criteria and prioritization model using a combination of three multicriteria decision‐making (MCDM) methods. The model applies fuzzy Delphi‐based analytical hierarchy process (AHP) method to determine the criteria weights and fuzzy axial distance‐based aggregated measurement (ADAM) method for ranking the alternatives. The findings of this study allow for the identification of the most important risks associated with CSC enabling stakeholders to allocate resources strategically and focus efforts on crucial areas. Results highlight that the most important risks are supply chain complexity, resource availability, and quality and technological challenges. The most important contributions are the identification of the most relevant risks that threaten the resilience and sustainability of CSCs, the establishment of the framework for their evaluation, and the development of a novel hybrid MCDM model for their ranking. These contributions are also the main theoretical implications of the study. On a practical level, it enables organizations to strategically manage risks, standardize risk assessment, and improve their competitive advantage by enhancing resilience, reducing disruptions, and lowering operational costs.

Rainfall Assessment Using Weighted Interval Type-2 Fuzzy Inference System

This study proposes an integrated model for assessing rainfall in Chennai using the Interval type-2 fuzzy reasoning technique and the Analytic Hierarchy Process (AHP). Interval Type-2 Fuzzy Inference System (IT2FIS) is employed to describe complex relationship between the rainfall variables and rainfall rate by considering individual weights of those variables through the aggregation of criteria weights using AHP. The suggested approach has an edge over its predecessors in terms of modeling the inter-personal and intra-personal uncertainty involved in rainfall rate classification. Finally, the developed rainfall model (RM) is applied on the historical dataset collected from Indian Meteorological Department (IMD). The actual data and the IT2FIS model’s performance are compared. Correlation coefficient ([Formula: see text]) and Bland–Altman Plot (BAP) have been used to evaluate the performance metrics of the proposed model. The outcome suggests that the AHP-IT2FIS model can accurately estimate rainfall over time, indicating its potential for long-term use.

Sistem Pendukung Keputusan Pemberian Insentif Kepada Karyawan Menggunakan Metode SMART

Providing employee incentives is a bonus given by the company to its employees, and is income outside of wages or salaries received every month. Giving incentives to employees at PT Gintar Pratama is done manually. However, the process of providing incentives to employees of PT Gintar Pratama is often not on target due to frequent miscalculations. This research was made to focus on these problems. The solution to the problem above is that the authors design a decision support system that can assist in solving the problem of giving incentives to employees of PT Gintar Pratama by using the SMART method in determining the weight of criteria and conducting rankings. This research produces a decision support system that can recommend giving incentives to PT Gintar Pratama employees use the SMART method. The results of this study are the creation of a decision support system for providing PT Gintar Pratama employee incentives using the SMART method. The method used is expected to create a very useful system, as well as a very accurate system. With the existence of a decision support system can provide recommendations to determine employees who are entitled to receive incentives.

Matrix‐phase material selection for shape memory polymer composites: A comparative analysis of multi‐criteria decision‐making

AbstractIn the present study, the selection of suitable shape memory polymers (SMPs) to be used as the matrix‐phase material with various (In)organic filler materials to achieve the required optimum multi‐stimuli response in shape memory polymer composites (SMPC) systems is analyzed. The selection of these materials is based on their mechanical and physical properties as well as other underlying factors such as cost, availability, shape recovery rate, and aesthetic characteristics. In this study, the entropy method was applied to estimate the weightages of the various criteria while the gray relation analysis (GRA) and the technique for order preference by similarity to ideal solution (TOPSIS). Multi‐criteria decision‐making (MCDM) approaches have been used to rank the suitable matrix‐phase polymer materials for manufacturing shape memory polymer composites (SMPC) system. A total of eight alternative SMP matrix‐phase materials based on a set of nine criteria were analyzed and ranked. The proposed methodology and the result obtained thereof have been illustrated in detail. The results obtained from TOPSIS and GRA methods have been compared to conclude the effects of the material properties on the ranking and the selection of the SMP materials. Among all the eight alternatives considered, thermoplastic polyurethane (TPU) was found to be the best material in both the MCDM methods. The material cost, resistivity, % elongation, and hydrophobicity present the most influencing properties on the SMP material selection, whereas density presents no effect on the SMP matrix material selection. The robustness of the results for the comparative analysis was verified using TOPSIS methodology to validate its reliability. It was revealed that the TPU, polycarbonate, polypropylene, and epoxy‐resin/poly(lactic acid) are the most dominant matrix‐phase SMP material alternatives when a deviation in the entropy weights of the primary evaluation criteria is applied. The novelty of this study is the exploration and application of statistical MCDM methods of engineering material selection problems based on a set of decision criteria, which can be time‐consuming and costly while using experimental analysis methods.Highlights SMPCs are applicable engineering materials thus making their research viable. SMPC systems can undergo various shape changes under external stimuli. Selection of matrix‐phase polymer is critical in achieving desired objectives. GRA and TOPSIS MCDM approaches have been applied in the selection process. TPU was found as the best material in both methods.

A BWM-TOPSIS Linear Programming Model for Evaluating the Performance of Health-Promoting Hospitals with McKinsey 7s Framework in Organizational Management

This research addresses the critical aspect of evaluating operational performance in health promotion hospitals, which play a vital role in providing medical services to local communities. The research proposes an integrated method for performance assessment, utilizing the Best-Worst Method (BWM) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) linear programming model. Taking the case of health promotion hospitals in Maha Sarakham province, Thailand, and considering the McKinsey 7s framework's seven criteria, BWM is employed to determine the criteria weights. Subsequently, the TOPSIS linear programming model selects the ideal health promotion hospital based on these weights. The BWM analysis reveals criteria weights in the following order: system, staff, skill, style, structure, strategy, and shared value. The TOPSIS linear programming model identifies SH12 as the top-performing health promotion hospital with a closeness coefficient value of 0.8821. Additionally, a Spearman's rank correlation test validates this proposed method against the original TOPSIS approach, yielding a correlation value of 1.0. These findings provide valuable guidance for organizations, particularly in shaping strategic policies and resource allocation within medical service units, medical equipment, and personnel management in organizational settings. This study offers that the proposed method is simpler and will aid in the ongoing analysis of strengths and weaknesses in the improvement of organizations and development, helping organizations adapt to changes.

Sistem Rekomendasi Penerima Bantuan Program Indonesia Pintar Menggunakan Multi Criteria Decision Making

The government continues to strive to increase equality, equity and access to education through various programs, including the Smart Indonesia Program (PIP). Process PIP recipient students selection is quite complex process if carried out without adequate information technology support. This condition was also experienced by SMKN 6 Surakarta. The large number of students registering for PIP, and data processing that is still being done from piles of files are conditions that influence the slow PIP student selection committee performance. The committee must select, search and verify a lot of student data. Selection process requires a long time and a high level of accuracy so it is prone to errors caused by human error. In this research, a Decision Support System was created using the Analytical Hierarchy Process (AHP) method and the Multi-Objective Optimization by Ratio Analysis (MOORA) method. AHP method in this research aims to determine each criterion weight before the alternative ranking stage. The aim of using the MOORA method is to determine the ranking of alternatives after determining the weighting using AHP. The criteria used in this research are: Ownership of cards/letters, parents' income and number of siblings. Each criterion has sub-criteria and values that will be used in the AHP method calculations. From the results of the accuracy test by comparing data from 2020 to 2022, the accuracy results for 2020 were 94.4%, 95.5% for 2021 and 93.6% for 2022.

A spherical fuzzy-based DIBR II-AROMAN model for sustainability performance benchmarking of wind energy power plants

Wind energy power plants (WEPPs) are renewable energy generation facilities that are increasingly used today. The performance evaluations of these energy plants are generally based on technical performance. Although there are studies in the literature focusing on determining the technical performance of WEPPs, there is a lack of research on evaluating their sustainable performance. The primary motivation of this study is to develop a decision model for identifying the sustainable performance of WEPPs. The model incorporates fuzzy logic and multi-criteria decision-making (MCDM) approaches, incorporating expert opinions and both qualitative and quantitative criteria. To determine the influence levels of experts, spherical fuzzy (SF) sets are utilized. The weighting of criteria is achieved using the SF-defining interrelationships between ranked criteria II (DIBR II) method, which is a novel extension of the DIBR II method based on SF sets. Additionally, an SF-alternative ranking order method accounting for two-step normalization (AROMAN) method is developed for ranking the sustainable performance of WEPPs, building upon the AROMAN method, and adapting it to SF sets. This hybrid model is named the SF-DIBR II-AROMAN hybrid model. The step-by-step procedures of this model are presented in the research, and an algorithm for these procedures is developed. To demonstrate its practicality, a case study is conducted, focusing on WEPPs with a capacity ranging from 25 to 150 megawatts in Çanakkale, Turkey. According to the research results, among the fifteen WEPPs in the Çanakkale region, “Saros WEPP” is identified as having the best one. The robustness of the obtained results is ensured through sensitivity analyses. Based on the results of two sensitivity analysis scenarios, “Saros WEPP” is found to have the highest sustainable performance. The SF-DIBR II-AROMAN hybrid model results are compared with different alternative ranking method results, highlighting its superior aspects. Overall, SF-DIBR II-AROMAN is consistent and robust. The research also provides insights and managerial implications, as well as explains the benefits of the proposed model for evaluating WEPPs’ sustainable performance.

Development of a decision support tool to compare diagnostic strategies for establishing the herd status for infectious diseases: An example with Salmonella Dublin infection in dairies

The diagnosis of infectious diseases at herd level can be challenging as different stakeholders can have conflicting priorities. The current study proposes a “proof of concept” of an approach that considers a reasonable number of criteria to rank plausible diagnostic strategies using multi-criteria decision analysis (MCDA) methods. The example of Salmonella Dublin diagnostic in Québec dairy herds is presented according to two epidemiological contexts: (i) in herds with no history of S. Dublin infection and absence of clinical signs, (ii) in herds with a previous history of infection, but absence of clinical signs at the moment of testing. Multiple multiparty exchanges were conducted to determine: 1) stakeholders’ groups; 2) the decision problem; 3) solutions to the problem (options) or diagnostic strategies to be ordered; 4) criteria and indicators; 5) criteria weights; 6) the construction of a performance matrix for each option; 7) the multi-criteria analyses using the visual preference ranking organization method for enrichment of evaluations approach; 8) the sensitivity analyses, and 9) the final decision. A total of nine people from four Québec’s organizations (the dairy producers provincial association along with the DHI company, the ministry of agriculture, the association of veterinary practitioners, and experts in epidemiology) composed the MCDA team. The decision problem was “What is the optimal diagnostic strategy for establishing the status of a dairy herd for S. Dublin infection when there are no clinical signs of infection?”. Fourteen diagnostic strategies composed of the three following parameters were considered: 1) biological samples (bulk tank milk or blood from 10 heifers aged over three months); 2) sampling frequencies (one to three samples collection visits); 3) case definitions to conclude to a positive status using imperfect milk- or blood-ELISA tests. The top-ranking diagnostic strategy was the same in the two contexts: testing the bulk tank milk and the blood samples, all samples collected during one visit and the herd being assigned a S. Dublin positive status if one sample is ELISA-positive. The final decision favored the top-ranking option for both contexts. This MCDA approach and its application to S. Dublin infection in dairy herds allowed a consensual, rational, and transparent ranking of feasible diagnostic strategies while taking into account the diagnostic tests accuracy, socio-economic, logistic, and perception considerations of the key actors in the dairy industry. This promising tool can be applied to other infectious diseases that lack a well-established diagnostic procedure to define a herd status.

Application of the full consistency method (FUCOM) - Cosine similarity framework in 5G infrastructure investment planning: An approach for telecommunication quality improvements

In the rapidly evolving telecommunications landscape, the shift towards advanced communication technologies marks a critical milestone. This transition promises to revolutionize connectivity by enabling seamless data downloads, high-quality video streaming, and instant access to applications. However, adapting to these advanced technologies poses significant challenges for infrastructure expansion, requiring innovative investment and deployment strategies. These strategies aim not only to enhance service quality but also to ensure extensive network coverage. To address the need for systematic planning in infrastructure investment, this paper presents a novel methodology that combines the Full Consistency Method (FUCOM) with cosine similarity analysis. This integrated approach effectively prioritizes service areas for the deployment of 5G technology, emphasizing the importance of detailed planning in mobile strategy development. By leveraging FUCOM to determine the weights of various criteria and employing cosine similarity analysis to rank service areas, the methodology facilitates efficient resource allocation and service quality enhancements. Empirical validation using real data from a Turkish telecommunications company confirmed the effectiveness of the proposed algorithm. The results indicate that this integrated approach can significantly advance the telecommunications industry by providing essential insights for companies seeking to improve service quality amidst the transition to 5G and beyond. The successful implementation of the proposed algorithm demonstrates its effectiveness in addressing the challenges faced by telecommunications companies and underscores the importance of a data-driven approach in strategic decision-making and resource allocation. Furthermore, the findings suggest that the integrated FUCOM and cosine similarity analysis approach can offer a valuable tool for telecommunications companies worldwide, offering a systematic method for prioritizing infrastructure investments and enhancing network performance.

Validation, adaptation and application of a multi‐criteria decision analysis‐based framework for chemotherapeutic agents in Egypt

AbstractHealth technology assessment is a systematic, transparent, robust, and unbiased assessment of healthcare technology that links evidence and decisions. Healthcare decisions usually require trading‐off, and using a structured approach, such as multi‐criteria decision analysis (MCDA), could improve decision quality. This study aimed to adapt an MCDA‐based framework for oncological drug evaluation in an Egyptian clinical setting to explore the perspectives of different stakeholders. The 10th edition of the Evidence and Value: Impact on decision making framework was translated, validated, adapted, and applied to explore key differences between the priorities of different stakeholders for coverage decisions in the oncology field in Egypt. A direct rating scale or hierarchical point allocation was used to elicit criteria weights. Normalized weights were calculated and compared using the preset cut‐off value of 0.008. Understandability, willingness to use the adopted weighting techniques, and cognitive burden were assessed and compared. Ninety participants completed the criteria‐weighting interview for the adapted tool. The elicited criteria weights differed between the two adopted weighting techniques when comparing stakeholders' priorities using the preset cut‐off (0.008). However, there was no significant difference in the weighting technique understandability, future use willingness, and cognitive burden parameters on categorization by either the weighting technique or stakeholder group. This study provides a specially tailored, straightforward procedure for Egyptian stakeholders to evaluate oncologic drugs in a standardized manner by combining various inputs. It also highlights the main variations across Egyptian stakeholder preferences.

International Market Selection Based on Integrated MCDM Methods: A Case Study of Iron and Steel Sector

International Market Selection (IMS) is crucial for companies seeking growth and expansion beyond their domestic boundaries. IMS is a strategic decision that requires careful evaluation of several factors to minimize risks and achieve long-term success in international markets. Accordingly, this study aims to find out optimal market alternatives for exporter companies operated in iron and steel sector. A total of twenty-three market alternatives were evaluated based on ten criteria. The alternatives and criteria were determined by literature review and expert opinions. The weight of criteria was calculated by the FUCOM and LOPCOW methods. Once the weight of criteria was determined, the alternatives were ranked using the SPOTIS, RSMVC, CoCoSo and Borda Count methods. The results showed that balance of trade and GDP were the most and least important criteria, respectively. Overall results revealed that Canada, the UAE, Germany, Japan and Malaysia were the best market alternatives, while Venezuela, Mexico, Peru, Colombia and the UK were the worst market alternatives for iron and steel exporter companies. Additionally, the sensitivity analysis was carried out to observe the robustness of the results.

Disparities of Central and Eastern European Countries of European Union in innovation potential: A multi-criteria assessment

The primary objective of the article is to evaluate the innovation potential of Central and Eastern European Union countries. The Technique for Order of Preference by Similarity to Ideal Solution method was employed to assess and rank this potential. The research was based on 10 indicators that characterize the main areas related to innovation, namely intellectual capital (as a measure of social innovative capacity), financial capital (as a measure of the potential for financing research and development) as well as research and innovation activities. Three objective approaches were used to calculate weights of these evaluation criteria (in the TOPSIS method): Shanon–Entropy, criteria importance through intercriteria correlation and equal weights. As a evaluate of the level of innovation potential of studied CEE countries between 2013 and 2022, the determined values of the Innovation Potential Index were used to evaluate the studied countries and divide them into four groups. With the use of the two non-parametric test, the relationships of this index with selected parameters were also measured. The research results show that the innovation potential level of CEE countries is highly differentiated. Slovenia, the Czech Republic and Estonia, countries investing in R&D and social development, were found to have a high level of innovation. The remaining countries need to take more decisive action in the areas studied to improve their level of innovation. The methodology developed for the research, the conducted study, and the resulting findings offer opportunities to analyze the indicators and areas studied, and through this to implement strategies for building an innovative knowledge-based economy both by the countries in question and the EU as a whole.