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Strategic management has applications in many areas of social life. One of the basic steps in the process of strategic management is formulating a strategy by choosing the optimal strategy. Improving the process of selecting the optimal strategy with MCDM methods and theories that treat uncertainty well in this process, as well as the application of other and different selection criteria, is the basic idea and goal of this research. The improvement of the process of the aforementioned selection in the defense system was carried out by applying a hybrid model of multicriteria decision-making based on methods defining interrelationships between ranked criteria (DIBR) and multiattributive ideal-real comparative analysis (MAIRCA) modified by triangular fuzzy numbers–"DIBR–DOMBI–Fuzzy MAIRCA model.” The DIBR method was used to determine the weight coefficients of the criteria, while the selection of the optimal strategy, from the set of offered methods, was carried out by the MAIRCA method. This was done in a fuzzy environment with the aim of better treatment of imprecise information and better translation of quantitative data into qualitative data. In the research, an analysis of the model's sensitivity to changes in weight coefficients was performed. Additionally, a comparison of the obtained results with the results obtained using other multicriteria decision-making methods was conducted, which validated the model and confirmed stable results. In the end, it was concluded that the proposed MCDM methodology can be used for choosing a strategy in the defense system, that the results of the MCDM model are stable and valid, and that the process has been improved by making the choice easier for decision makers and by defining new and more comprehensive criteria for selection.
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Evaluation and selection of eco-innovation strategies is a significant and complex strategic decision, and despite the relevance and interest in the field of eco-innovation, the area of eco-innovation strategies has not been explored in depth in the scientific literature. Therefore, in this study, we propose an integrated approach to evaluating eco-innovation strategies from the perspective of strategic green transformation that helps decision-makers evaluate and select eco-innovation strategy aiming to achieve a competitive advantage. For this study, we adopted a validated multi-criteria decision-making methodology (MCDM) by combining Analytical Hierarchy Process (AHP) and The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The reliability of the proposed framework was tested and applied in the context of the Lithuanian furniture industry. This study offers three contributions and provides a comprehensive and profound insights into eco-innovation strategies. First, this study conceptualizes eco-innovation strategy from the perspective of strategic green transformation and proposed a novel definition and classification of eco-innovation strategies leading to competitive advantage. Second, this study proposes a novel approach to the evaluation of eco-innovation strategies taking into account micro-, meso-, and macro-level environmental factors. Third, the findings of this study provide implications for scholars and decision-makers in the field of eco-innovation strategy and set an agenda for future research.
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The application of Industry 4.0 (I4.0) in the field of logistics leads to the emergence and development of the concept of logistics 4.0. Many I4.0 technologies have been applied in the field of logistics. The goal of this research is to analyze the applicability of nine key I4.0 technologies in logistics centers (LC). For this purpose, an integrated MEREC (MEthod based on the Removal Effects of Criteria)—fuzzy MARCOS (Measurement of Alternatives and Ranking according to COmpromise Solution) model was developed. The applicability of nine I4.0 technologies was evaluated based on 15 subcriteria within three main groups of criteria, namely, technological, social and political, and economic and operative. Using the MEREC method, the weight values of the criteria and subcriteria were determined, while the technologies were ranked using the fuzzy MARCOS method. Based on the results obtained by applying this integrated MCDM (multicriteria decision-making) model, CC was identified as the best alternative, i.e., the technology that is most applicable in logistics centers, followed by IoT and big data. An analysis of the sensitivity of the obtained results to the change in the importance of the criteria was carried out, which shows certain changes in the ranking when the importance of the most important criterion changes.
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The shift to renewable sources of energy has become a critical economic priority in African countries due to energy challenges. However, investors in the development of renewable energy face problems with decision making due to the existence of multiple criteria, such as oil prices and the associated macroeconomic performance. This study aims to analyze the differential effects of international oil prices and other macroeconomic factors on the development of renewable energy in both oil-importing and oil-exporting countries in Africa. The study uses a panel vector error correction model (P-VECM) to analyze data from five net oil exporters (Algeria, Angola, Egypt, Libya and Nigeria) and five net oil importers (Kenya, Ethiopia, Congo, Mozambique and South Africa). The study finds that higher oil prices positively affect the development of renewable energy in oil-importing countries by making renewable energy more economically competitive. Economic growth is also identified as a major driver of the development of renewable energy. While high-interest rates negatively affect the development of renewable energy in oil-importing countries, it has positive effects in oil-exporting countries. Exchange rates play a crucial role in the development of renewable energy in both types of countries with a negative effect in oil-exporting countries and a positive effect in oil-importing countries. The findings of this study suggest that policymakers should take a holistic approach to the development of renewable energy that considers the complex interplay of factors, such as oil prices, economic growth, interest rates, and exchange rates.
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Unpredictability and fuzziness coexist in decision-making analysis due to the complexity of the decision-making environment. "Pythagorean fuzzy numbers” (PFNs) outperform "intuitionistic fuzzy numbers” (IFNs) when dealing with unclear data. The "Pythagorean fuzzy set” (PFS) is a useful tool because it removes the restriction that the sum of membership degrees be less than or equal to one by substituting the square sum for the sum of membership degrees. This study proposes two aggregating operators (AOs). The recommended operators outperform the already specified PFN operators. The proposed operator is utilised in the multicriteria decision-making process to identify the best candidate for instruction (MCDM).
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This study has a novel approach to capture the attitude of Bottom of the Pyramid (BoP) consumers towards Packaging Influenced Purchase (PIP) during the Covid-19 crisis. Over the years, BoPs consumers have established themselves as an emerging market with ample growth and opportunities. The authors suggested a Multiple-Criteria Decision-Making (MCDM) based framework to assist marketers in targeting both urban and rural BoP consumers regarding PIP. Packaging elements and influence of family, extended family, peers have been included in the framework for gaining in-depth understanding. With a sample size of 100 from West Bengal, this focus group-based study can fulfil the BoP literature's existing prominent research gap. Results indicate the difference in attitude for urban and rural BoPs towards PIP during this crisis. The fusion of MCDM based approach and relevant machine learning-based technique aims to assist marketers in identifying, formulating, and redefining an action plan.
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The goal of this study is to examine and identify the factors influencing customer attitude toward and intention to use digital wallets (electronic wallets, e-wallets) during and after the COVID-19 pandemic. A total of 257 correctly fulfilled questionnaires from an online survey were summarized. The main features of e-wallet payment systems were classified with a focus on consumer satisfaction via the integration of classic and modern data analysis methods. Structural Equation Modeling (SEM) was preferred to reveal the dependencies between the variables from e-wallets users' perspective. The designed model can discover and explain the underlying relationships that determine the e-wallets' adoption mechanism. The obtained results lead to specific recommendations to stakeholders in the value chain of payment processing. Financial regulatory authorities could employ the presented results in planning the development of payment systems. E-commerce marketers could utilize the proposed methodology to assess, compare and select an alternative way for order payment. E-wallet service providers could establish a reliable multi-criteria system for the evaluation of digital wallet adoption. Being aware of the most important components of e-wallets value, managers can more effectively run and control payment platforms, enhance customer experience, and thus improve the company's competitiveness. As the perceived value of customer satisfaction is subjective and dynamic, measurements and data analysis should be conducted periodically.
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This paper aims to analyze the efficiency of the funds in technological, healthcare, and consumer cyclical sectors based on the U.S. News & World Report rankings. We employed a Principal Component Analysis to select the indicators to explain efficiency. Then, we have used an alternative approach that combines Data Envelopment Analysis (DEA) with Multiple Criteria Decision Aiding, the Value-Based DEA, to assess the efficiency of funds for 1 year (2020), 3 years (2018–2020), and 5 years (2016–2020). The results highlight that in 2020 the number of efficient funds is much smaller than in previous periods and this can be justified by the effect of the COVID-19 pandemic crisis. The sectors with the most efficient funds are technology and healthcare. The factors that determine the efficiency of funds in the health sector and the technology sector are quite different, although they have not undergone major changes in the three periods considered. For managers, health funds are seen as low risk and hardly consider the return factors in all analyzed periods, which is often considered as benchmarks for inefficient funds. In the technology sector, Beta and Alpha are generally the indicators with the greatest weight in fund efficiency, showing that these funds beat the market in terms of returns and are less risky than the benchmark. This study seeks to complete the scarce existing literature on the subject, namely in the sectors under analysis, seeking to identify the indicators that fund managers ponder most to consider a fund as efficient. As far as we know, the joint efficiency analysis of these sectors and the impact they suffered from the COVID-19 pandemic are new in the literature.
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Artificial intelligence development and research leaders in business, industry, and nations gain a major competitive edge. Additionally, it is clear that nations with a well-established national artificial intelligence policy have an edge over others, both technologically and economically. To further their artificial intelligence capability, nations also seek to develop a strategy, vision, structure, and working environment that encourages collaboration between the public sector, private industry, and educational institutions. Artificial intelligence is thought to be a tool that will help bridge the gap between powerful and developing countries growing in the future. Using data from "The Global AI Index” for 2021, this study aims to examine and analyze the present state of artificial intelligence management in 62 nations in terms of talent, infrastructure, business environment, development and research government policy, and commercial efforts. The research used PROMETHEE, which is widely used in multi-criteria decision-making evaluations, and its geometric representation, the GAIA plane. This study also found that the United States of America is the world leader in artificial intelligence (AI) research and development as well as AI investment. The United Kingdom, China, Israel, Canada, the Netherlands, South Korea, and Germany all rank well. China is rapidly catching up to the USA. At the very bottom of the list are nations such as Armenia, Kenya, Egypt, South Africa, and Pakistan. Turkey's values are more similar to those of nations towards the bottom of the list than of those in the top half. There is a significant gap between the top three countries and the rest of the world in all parameters included in the survey. Except for the ‘State Strategy' category, Turkey scores quite low compared to the top-performing countries. Decision makers are expected to address the identified global challenges of the study by creating a more comprehensive national AI strategy, both financially and in terms of content.
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Nowadays, we live in the era of Big Data. Companies have realized by now that transitioning to a datadriven business is strategic for their growth and competitiveness. To achieve that, companies should get the most value out of their data through Big Data technologies. In the previous work, we showed how maturity models are essential to assess the ability of companies to start Big Data projects and prevent eventual failures. We also explored literature and editors' offerings in this field and proposed an exhaustive maturity model that includes Methodology, a maturity domain of high importance and impact. In the present work, we aim to provide a detailed picture of the proposed global Maturity Model design by exploring the temporal domains and explaining how they evolve through time. In addition, we aim to introduce the assessment framework, a tool we made available for North African companies to be able to evaluate their Big Data maturity. Unlike the currently available models, which are usually detailed and complex, the global assessment tool is quick and easy. We enriched the assessment questionnaire with best practices, and more importantly, the assessment tool suggests a list of shortcomings that companies should avoid in order to succeed in their Big Data adoption journey. Moreover, we present the technologies used to implement the global assessment tool. We also show an example of a company's assessment results via visualizations. Furthermore, we demonstrate the importance of the methodology domain through assessment results;and we analyze which sector has mature companies and which maturity domains are more mature by industry sector. Finally, we conclude with an opening on using Multiple Criteria Decision-Making techniques to calculate companies' maturity accurate scores.
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Senegal has been investing in the development of its energy sector for decades. By using a novel multi-criteria decision analysis (MCDA) based on the principal component analysis (PCA) method, this paper develops an approach to determine the effectiveness of Senegal's policies in supporting low-carbon development. This was determined using six criteria (C1 to C6) and 17 policies selected from the review of Senegal's energy system. In order to determine the optimal weighting of the six criteria, a PCA is performed. In our approach, the best weighted factor is the normalized version of the best linear combination of the initial criteria with the maximum summarized information. Proper weighted factors are determined through the percentage of the information provided by the six criteria kept by the principal components. The percentage of information is statistically a fit of goodness of a principal component. The higher it is, the more statistically important the corresponding principal component is. Among the six principal components obtained, the first principal component (comp1) best summarizes the values of criteria C1 to C6 for each policy. It contains 81.15% of the information on energy policies presented by the six criteria and was used to rank the policies. Future research should take into account that when the number of criteria is high, the share of information explained by the first principal component could be lower (less than 50% of the total variance). In this case, the use of a single principal component would be detrimental to the analysis. For such cases, we recommend a higher dimensional visualization (using two or three components), or a new PCA should be performed on the principal components. This approach presented in our study can serve as an important benchmark for energy projects and policy evaluation.
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Covid-19 has posed difficult and challenging situations to the supply chains and companies are in fix how to choose the vendors under the uncertainty and complexity in recent years. Therefore, this research aims to incorporate structural transformation of the fuzzy analytical hierarchy process (FAHP) that is most appropriate for the uncertainty and disruption caused by Covid-19 like situation for ensuring supplies from vendors. The conventional approaches for vendor selection and evaluation use numerous multi-criteria decision-making tools that may not ensure reliability in a dynamic situation caused due to Covid-19. In this research, Fleiss' Kappa method ensures the reliability of responses from eight respondents by using pairwise comparisons and assigning weights as envisaged in FAHP. In addition to determine the reliability of responses, a step under FAHP has been altered. This alteration is demonstrated in the vendor selection case in the Covid-19 scenario. The research suggests a plausible system required to address the uncertainties associated with Covid-19 to select and evaluate vendors by modifying a FAHP. The proposed altered mechanism can be incorporated in a similar type of other decision-making circumstances such as Covid-19, where the decision-makers are more than one, and the situation is very dynamic. The study is likely to facilitate information management, algorithmic development in decision making, or machine-driven decisions in uncertain conditions. The study offers managerial implications to purchase managers to accommodate and combine multiple factors and responses concerning the vendor performances for their evaluation, thus making a process more reliable.
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PurposeDue to the introduction of new vaccines in the child immunization program and inefficient vaccine supply chain (VSC), the universal immunization program (UIP), India is struggling to provide a full schedule of vaccination to the targeted children. In this paper, the authors investigate the critical factors for improving the performance of the existing VSC system by implementing the next-generation vaccine supply chain (NGVSC) in India.Design/methodology/approachThe authors design a fuzzy multi-criteria framework using a fuzzy analytical hierarchical process (FAHP) and fuzzy multi-objective optimization on the basis of ratio analysis (FMOORA) to identify and analyze the critical barriers and enablers for the implementation of NGVSC. Further, the authors carry out a numerical simulation to validate the model.FindingsThe outcome of the analysis contends that demand forecasting is the topmost supply chain barrier and sustainable financing is the most important/critical enabler to facilitate the implementation of the NGVSC. In addition, the simulation reveals that the results of the study are reliable.Social implicationsThe findings of the study can be useful for the child immunization policymakers of India and other developing countries to design appropriate strategies for improving existing VSC performance by implementing the NGVSC.Originality/valueTo the best of the authors' knowledge, the study is the first empirical study to propose the improvement of VSC performance by designing the NGVSC.
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Covid-19 pandemisi ilk günden günümüze dünyayı etkisi altına almış ve ülkeleri birçok farklı alanda etkilemiştir. Ülkelerin pandemi ile mücadele performanslarının belirleyicileri arasında mevcut saǧlık sistemlerinin gücü, ekonomik yapıları, demografik yapıları, uygulanan önlemler ve yapılan destekler gibi kriterler sayılabilir. Bu süreçte ülkelerin dâhil olduǧu uluslararası organizasyonların aldıǧı ortak kararlar da pandemi ile mücadele aşamasında ülkeleri desteklemektedir. Çalışmanın temel amacı söz konusu uluslararası organizasyonlardan G20 topluluǧundaki ülkelerin pandemi ile mücadele performanslarının çok kriterli karar verme yöntemleri (ÇKKV) aracılıǧıyla deǧerlendirilmesidir. Çalışmada öncelikle kriterler için CRITIC yöntemi ile aǧırlıklandırma işlemi gerçekleştirilmiştir. En önemli kriterler sırasıyla vaka sayısı, ölüm sayısı, likidite destekleri ve saǧlık sektörüne yapılan ek harcamalar olarak saptanmıştır. Sonrasında ÇKKV yöntemlerinden TOPSIS, COPRAS, ARAS, WASPAS, MOORA, MABAC yöntemleri ile analiz gerçekleştirilerek ülkelere ilişkin sıralamalar elde edilmiştir. Nihai olarak ortak bir sıralama için COPELAND yöntemi kullanılmıştır. Sonuç olarak en başarılı ülkeler sırasıyla Avusturalya, Japonya ve Çin olarak belirlenirken son sıraları Brezilya, Meksika ve Güney Afrika paylaşmaktadır.Alternate :The COVID-19 pandemic has affected countries around the whole world in many different areas. The main determinants of a country's performance against the pandemic can be summarized through criteria such as the strength of its current health system, economic structures, demographic structures, restrictions, and support. Countries' strategies also involve the consensus that has been reached by international organizations. The main purpose of this study is to evaluate the performance of G20 countries using multiple criteria decision-making (MCDM) methods. The criteria were first weighted using the CRITIC method, with the number of cases, number of deaths, liquidity supports, and additional expenditures in the health sector having been determined as the most important criteria. The data were then analyzed using MCDM methods to obtain countries' rankings. As a result, the most successful countries were respectively determined as Australia, Japan, and China, while Brazil, Mexico, and South Africa came in the respective last three places.
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To find a parking space, valet parking drivers have to travel a lot, which leads to carbon dioxide (CO2) emissions. In order to reduce these emissions, it is essential to understand a user's needs and criteria when searching for a parking space. Several selection criteria are considered when allocating a parking space. Recent research on parking space management mentions several parameters that have an impact on the choice of a parking space: namely, the traffic situation, the availability of each parking lot in question, and the cost of parking, etc. In this article, we discuss a new criterion: the physical condition of the driver in the management of parking spaces;the identification of the driver's bodily fragility. We also propose MCDM as a parking space allocation model that best meets the cost–benefit convention. This reflection leads us to evaluate MCDM methods in the field of intelligent parking management. Therefore, we conducted a comparison between the most recent multi-criteria decision making methods used by researchers, namely, CODA, EDAS, TOPSIS, and WASPAS. The CRITIC method was used in this paper to objectively determine the weight of each criterion. A new approach is proposed to evaluate and select the best MCDM method. Indeed, we propose a method that computes the "average inter-item correlation SW”, a combination of the "average inter-item correlation” and the SW coefficient. This approach allows us to efficiently compute the correlation between a method and the set of methods while favoring the cells with the best ranking. A case study is presented to illustrate the MCDM approach to parking space allocation and evaluation. The proposed system provides drivers with services such as intelligent parking decisions, taking into account the human aspect while reducing energy consumption, driving time, and traffic congestion caused by searching for available parking spaces.
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The emergence, spread, and outbreak of the COVID-19 pandemic, first in the Wuhan city of China but later in the rest of the world, has affected the lives of people all over the world. This inevitable influence has not left the transportation sector unaffected. Therefore, there is a need to examine whether the choices of citizens have been influenced in terms of their mobility. The choice of the optimal solution for each citizen depends on many factors, such as the price of transportation fuel, the safety of going to work, the availability of public transport, the possibility of risk of infection, etc. The mobility choices of the citizens in the pre-COVID-19 era in comparison with the post-COVID-19 era have changed due to the increased vigilance of the citizens. In addition to citizens, other stakeholders are infectious disease experts/epidemiologists, transportation engineers, etc. This is, therefore, a problem that is offered for analysis using Multi-Actor Multi-Criteria Analysis (MAMCA). This research investigates the mobility choices of people in the post-COVID-19 era using the different stakeholder groups and the MAMCA methodology. Useful results arise concerning the influencing factors of the mobility choices of different stakeholder groups in the post-COVID-19 era.
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IntroductionMulti-criteria decision analysis (MCDA) is a useful tool in complex decision-making situations and has been used in medical fields to evaluate treatment options and drug selection. We aimed to provide valuable insights on the use of MCDA in health care through examining the research focus of existing studies, major fields, major applications, most productive authors and countries, and most common journals in the domain using a scientometric and bibliometric analysis.MethodsPublications related to MCDA in health care were identified by searching the Web of Science Core Collection on 14 July 2021. Three bibliometric software programs (VOSviewer, Bibliometrix, and CiteSpace) were used to conduct the analysis.ResultsA total of 410 publications were identified from 196 academic journals (average yearly growth rate of 32% from 1999 to 2021), with 23,637 co-cited references by 871 institutions from 70 countries or regions. The USA was the most productive country (n=80), while the Universiti Pendidikan Sultan Idris (n=16), Université de Montréal (n= 13), and Syreon Research Institute (n=12) were the most productive institutions. The biggest nodes in every cluster of author networks were Aos Alaa Zaidan, Mireille Goetghebeur, and Zoltan Kalo. The top journals in terms of number of articles (n=17) and citations (n=1,673) were Value in Health and the Journal of Medical Systems, respectively. The research hotspots mainly included the analytic hierarchy process (AHP), decision-making, health technology assessment, and healthcare waste management. In the recent literature there was more emphasis on coronavirus disease 2019 (COVID-19) and fuzzy Technique for Order Preference by Similarities to Ideal Solution (TOPSIS). Big data, telemedicine, TOPSIS, and the fuzzy AHP, which are well-developed and important themes, may be the trends in future research.ConclusionsThis study provides a holistic picture of the MCDA-related literature published in health care. MCDA has a broad application in different topic areas and would be helpful for practitioners, researchers, and decision makers working in health care when faced with complex decisions. It can be argued that the door is still open for improving the role of MCDA in health care, both in its technologies and its application.
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The daily traffic congestion faced by residents who live in the Greater Jakarta area has popularized the Jabodetabek Electric Train (KRL) as a mode of transportation for commuters to work and school. However, satisfactory service has not matched the increasing number of KRL users. Therefore, this study aims to develop a sustainability policy for urban rail services in Indonesia as an alternative strategy to overcome KRL service problems, such as overcapacity and inconvenience. The Multipol method is used to develop KRL service sustainability policies. Multipol is a multi-criteria decision-making method through the opinion of transportation experts. This study found that controlling the spread of COVID in the KRL is one of the priorities that must be implemented to ensure the continuity of the train. Then, the safety and security of the KRL are among the most critical policies determining the sustainability of urban railways in Indonesia. Therefore, safety and security can be improved by developing the competence of railway personnel through training, and protection, guaranteeing the rights of persons with disabilities and building safe and secure transfer spaces for onward journeys. The Ministry of Transportation and Indonesian commuter rail companies must improve supervision, assessment, and sanctions to meet service standards.
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Introducción: esta investigación analiza la comunicación digital llevada a cabo por 21 organismos públicos del ámbito sanitario en la plataforma Twitter en el periodo de la pandemia de la COVID-19. Se centra en examinar los criterios de éxito de las estrategias de comunicación en esta red social digital y en conocer su peso ponderado en el modelo propuesto. Metodología: el registro de los datos se ha elaborado a través de la aplicación de acceso libre Twitonomy que analiza los últimos 3.200 Tweets de cada perfil. El análisis de la información se realiza aplicando el Analytic Hierarchy Process (AHP) o Proceso de Análisis Jerárquico como método para la toma de decisiones multicriterio. A través del software Expert Choice se estudian 17 criterios correspondientes a los clústeres actividad de la cuenta e impacto. Resultados: se determina la alternativa más adecuada en el ámbito de la comunicación de la salud pública en redes sociales y el peso de los criterios que benefician esta comunicación en la plataforma Twitter. Discusión y Conclusiones: este estudio confirma que es conveniente que los responsables de la toma de decisiones en materia de comunicación digital tengan presente que el clúster impacto tiene un mayor peso en la red social Twitter que la actividad de la cuenta.Alternate :Introduction: this research analyses the digital communication carried out by 21 public health institutions on the Twitter platform during the period of the COVID-19 pandemic. It focuses on examining the criteria for the success of communication strategies on this digital social network and their weighted weight in the proposed model. Methodology: the data was recorded using the open-access application Twitonomy, which analyses the last 3,200 Tweets from each profile. The analysis of the information is carried out by applying the Analytic Hierarchy Process (AHP) as a method for multi-criteria decision-making. Using Expert Choice software, 17 criteria corresponding to the account activity and impact clusters are studied. Results: The most appropriate alternative in the field of public health communication in social networks and the weight of the criteria that benefit this communication on the Twitter platform are determined. Discussion and Conclusions: this study confirms that it is advisable for decision-makers in digital communication to bear in mind that the impact cluster has a greater weight on the Twitter social network than account activity.
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The travel and tourism industry has numerous components that contribute to the economy and create new jobs since it is a service sector that incorporates other service networks. Furthermore, it acts as a catalyst in sustaining investment attractiveness and economic indicators such as closing the current account deficit. The Travel and Tourism Competition Index utilized in this research has four dimensions and fourteen indicators. In this research, the Entropy-based VIKOR approach, which is a Multi-Criteria Decision-Making method, Spearman Correlation analysis, and K-means clustering analysis were employed to propose a methodological novelty in this field. The study analyzed the competitiveness of significant European and Eurasian nations based on key indicators. According to country evaluations, Spain, France, Germany, the United Kingdom, Italy, and Switzerland differ from other countries in a positive sense and with a significant difference. Eastern European and Balkan nations are often at the bottom of the table. As a consequence of this study, it is expected that the results of future studies using other methodologies or methods will be compared with this study. At the same time, it is aimed to explain the relevant indicators and their dimensions.