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2009 yılında piyasaya giriş yapan kripto paralar, âdemi merkeziyetçi bir yapıda mübadele işlevini yerine getiren sanal para birimleridir. Kripto paralar, blockchain teknolojisine bağlı olarak ticari işlemlere konu olması ve düşük transfer maliyeti ile hızlı işlemler gerçekleştirilmesi gibi niteliksel özelliklere sahiptir. Kripto paralar, bir yatırım aracı olarak son yıllarda büyük ilgi görmektedir. Bu çalışmanın temel amacı, yatırımcıların yatırım kararı vermesini kolaylaştıracak bir karar destek mekanizması sunmaktır. Bu amaçla Entropi-ARAS çok kriterli modeli kullanılarak finansal piyasalarda en yüksek işlem hacmine sahip ilk on kripto para değerlendirilmiştir. Öncelikle Entropi yöntemi kullanılarak yıllık ortalama getiri, işlem hacmi, toplam piyasa değeri, işlem gördüğü borsa sayısı, işlem gördüğü yıl sayısı, işlem hızı, en yüksek değerden değişim ve volatilite kriterinin ağırlıkları belirlenmiştir. Ardından ARAS yönteminde kullanılarak, kripto para alternatiflerinin performans sıralaması yapılmıştır. Çalışmanın bulgularına göre, işlem hacmi yatırım yaparken dikkate alınması gereken en önemli faktördür. Ayrıca, alternatif kripto paralar içinde ilk sırada Bitcoin, Tether ve Usd Coin yer alırken, Dogecoin son sırada yer almaktadır. Kripto paraların işlem hacmi ile performans sıralamalarının yüksek derecede pozitif korelasyona sahip olduğu sonucuna da ulaşılmıştır. Son olarak önerilen modelin sağlamlığı ve geçerliliği duyarlılık analiziyle de kontrol edilmiştir.

PurposeNowadays, companies have required new alternatives and strategies to handle environmental sustainability difficulties, primarily as ecological and social awareness has grown. In this context, the aim is to determine the green transportation indicators in companies with corporate identity and logistics activities at the international level in Giresun, Ordu, Gümüshane, Artvin, Rize, and Trabzon in the Eastern Black Sea Region in Turkey. At the same time, the study contributes to providing an effective and applicable solution to decision-making problems involving the assessment of green transportation indicators and smart network strategies in the logistics sector, which is a critical sector for countries. The purpose of this paper is to address these issues.Design/methodology/approachThis study aims to propose a model for the selection of smart network strategy and to determine the criteria weights used in green transportation indicators, and establish an ideal smart network strategy. In achieving the outlined goals of the study, the authors believe that the model proposed in the study will draw the focus to green logistics which will aid the environmental, economic and social efforts of businesses and governments through the provision of efficient use of scarce resources, which will, in turn, ensure that we leave a sustainable environment for future generations and businesses enjoy a competitive advantage. At the same time, different smart network strategies and green transportation indicators in companies show the success rate of social, economic and environmental indicators in green logistics practices. In addition to providing innovative, reliable and sustainable transportation systems, smart network strategies are critical for businesses to create cost advantages. Through the green transportation indicators and smart network strategies selection model outlined in this study, it is clear that the contribution will not only be limited to businesses, as the society and governments will also benefit from the important indicators on sustainability, as well as the protection of the environment and nature.FindingsAccording to the findings, “economic indicators” is the essential green transportation indicator in logistics companies with a corporate identity and worldwide transportation operations. Besides, the “mixed access model strategy” is the most appropriate smart network strategy in logistics firms with corporate identity and worldwide transportation activities. Currently, it is possible to assume that logistics organizations prefer to profit from all smart network strategies in terms of cost optimization and competitiveness rather than from just one. The study, on the other hand, which is a road map that will help sustainability practices in the logistics sector due to green transportation, also examines the similarities and differences of green transportation practices in companies in the relevant sector and to what extent they can be reflected. As a result, the study provides a practical road map for selecting green transport indicators and a smart network strategy process for the logistics industry.Originality/valueThis study examined logistics companies with a corporate identity and international transportation activities in provinces in the Eastern Black Sea Region such as Ordu, Giresun, Trabzon, Rize, Artvin and Gümüshane. Novel picture fuzzy level based weight assessment (PF-LBWA) and picture fuzzy combined compromise solution (PF-CoCoSo) methods are developed to solve the decision-making problem.

Innovation can be the greatest hope of overcoming economic challenges. This paper aims to evaluate countries concerning their innovation performances. We introduce an innovation performance evaluation methodology by considering objective factors and applying seven reliable MCDM methods: MEREC, CODAS, MABAC, MARCOS, CoCoSo, WASPAS, and MAIRCA. MEREC calculates the relative weights of indicators considered, while the other techniques decide the ranking order of G7 countries. The Borda rule is then employed to gain an aggregated ranking order. “Business sophistication” is the most critical indicator, whereas the US has the best position regarding the overall ranking. Sensitivity control is as well conducted.

İnternetin keşfedilmesiyle birlikte insanların yaşam tarzlarında ve alışkanlıklarında önemli ölçüde değişimler meydana gelmiştir. İnternetle birlikte işletmeler satışlarını internet üzerinden yapma eğilimine girmiş ve insanlar giderek daha fazla ihtiyacını internet üzerinden karşılamaya başlamışlardır. 2019 yılında ortaya çıkan Covid-19 ile internet tüketicilerin hayatında daha da önemli bir yere sahip olmaya başlamıştır. Tüketiciler hastalık korkusuyla ihtiyaçlarını sanal alışveriş platformları üzerinden gidermeye yönelmişlerdir. Bu çerçevede bu çalışmada beş ana kriter ve bunlara ilişkin yirmi üç alt kriter doğrultusunda sanal alışveriş platformlarının salgın dönemindeki performansları AHP ve MAIRCA bütünleşik modeliyle analiz edilmiştir. Bu bağlamda AHP yöntemiyle kriterlerin önem ağırlıkları tespit edilmiştir. MAIRCA yöntemi ise sanal alışveriş sitelerinin sıralanması amacıyla kullanılmıştır AHP sonuçları sırasıyla “uygun fiyat”, “karantina süreci” ve “enfekte olma korkusu” kriterlerinin sanal alışveriş platformlarının performanslarının en önemli belirleyicileri olduğunu göstermiştir. Ayrıca Trendyol, en başarılı alışveriş sitesi olarak belirlenmiştir. Bu çalışma sanal alışveriş platformları için salgın dönemindeki kendi performans değerlendirmelerinde kullanabilecekleri bir kılavuz niteliğinde olabilir.

This paper proposes a new multi-criteria group decision-making (MCGDM) method utilizing q-rung orthopair fuzzy (qROF) sets, improved power weighted operators and improved power weighted Maclaurin symmetric mean (MSM) operators. The power weighted averaging operator and power weighted Maclaurin symmetric mean (MSM) operator used in the existing MCGDM methods have the drawback of being unable to distinguish the priority order of alternatives in some scenarios, especially when one of the qROF numbers being considered has a non-belongingness grade of 0 or a belongingness grade of 1. To address this limitation of existing MCGDM methods, four operators, namely qROF improved power weighted averaging (qROFIPWA), qROF improved power weighted geometric (qROFIPWG), qROF improved power weighted averaging MSM (qROFIPWAMSM) and qROF improved power weighted geometric MSM (qROFIPWGMSM), are proposed in this paper. These operators mitigate the effects of erroneous assessment of information from some biased decision-makers, making the decision-making process more reliable. Following that, a group decision-making methodology is developed that is capable of generating a reasonable ranking order of alternatives when one of the qROF numbers considered has a non-belongingness grade of 0 or a belongingness grade of 1. To investigate the applicability of the proposed approach, a case study is also presented and a comparison-based investigation is used to demonstrate the superiority of the approach.

PurposeThis paper proposes a new multi-criteria decision-making method, called the vital-immaterial-mediocre method (VIMM), to determine the weight of multiple conflicting and subjective criteria in a decision-making problem.Design/methodology/approachThe novel method utilizes pairwise comparisons, vector-based procedures and a scoring approach to determine weights of criteria. The VIMM compares alternatives by the three crucial components, namely the vital, immaterial and mediocre criteria. The vital criterion has the largest effect on the final results, followed by the mediocre criterion and then the immaterial criterion, which is the least impactful on the prioritization of alternatives. VIMM is developed in two forms where the first scenario is designed to solve one-goal decision-making problems, while the second scenario embraces multiple goals.FindingsTo validate the method’s performance and applicability, VIMM is applied to a problem of sustainable supplier selection. Comparisons between VIMM, analytic hierarchy process (AHP) and best-worst method (BWM) reveal that VIMM significantly requires fewer comparisons. Moreover, VIMM works well with both fractional and integer numbers in its comparison procedures.Research limitations/implicationsAs an implication for research, we have added the development of the VIMM under fuzzy and grey environments as the direction for optimization of the method.Practical implicationsAs managerial implications, VIMM not only provides less complex process for the evaluation of the criteria in the managerial decision-making process, but it also generates consistent results, which make VIMM a reliable tool to apply to a large number of potential decision-making problems.Originality/valueAs a novel subjective weighting method, there exist five major values that VIMM brings over AHP and BWM methods: VIMM requires fewer comparisons compared with AHP and BWM; it is not sensitive to the number of criteria; as a goal-oriented method, it exclusively takes the decision-making goals into account; it keeps the validity and reliability of the Decision-Makers’ (DMs’) opinions and works well with integer and fractional numbers.

PurposePharmaceutical supply chains (PSCs) need a well-operating and faultless logistics system to successfully store and distribute their medicines. Hospitals, health institutes, and pharmacies must maintain extra stock to respond requirements of the patients. Nevertheless, there is an inverse correlation between the level of medicine stock and logistics service level. The high stock level held by health institutions indicates that we have not sufficiently excellent logistics systems presently. As such, selecting appropriate logistics service providers (drug distributors) is crucial and strategic for PSCs. However, this is difficult for decision-makers, as highly complex situations and conflicting criteria influence such evaluation processes. So, a robust, applicable, and strong methodological frame is required to solve these decision-making problems.Design/methodology/approachTo achieve this challenging issue, the authors develop and apply an integrated entropy-WASPAS methodology with Fermatean fuzzy sets for the first time in the literature. The evaluation process takes place in two stages, as in traditional multi-criteria problems. In the first stage, the importance levels of the criteria are determined by the FF-entropy method. Afterwards, the FF-WASPAS approach ranks the alternatives.FindingsThe feasibility of the proposed model is also supported by a case study where six companies are evaluated comprehensively regarding ten criteria. Herewith, total warehouse capacity, number of refrigerated vehicles, and personnel are the top three criteria that significantly influence the evaluation of pharmaceutical distribution and warehousing companies. Further, a comprehensive sensitivity analysis proves the robustness and effectiveness of the proposed approach.Practical implicationsThe proposed multi-attribute decision model quantitatively aids managers in selecting logistics service providers considering imprecisions in the multi-criteria decision-making process.Originality/valueA new model has been developed to present a sound mathematical model for selecting logistics service providers consisting of Fermatean fuzzy entropy and WASPAS methods. The paper's main contribution is presenting a comprehensive and more robust model for the ex ante evaluation and ranking of providers.

PurposeThe present study aims to provide a practical and robust assessment technique for assessing countries' investability in global supply chains to practitioners. Thus, the proposed approach can help decision-makers evaluate and select appropriate countries in the expansion process of the global supply chains and reduce risks concerning country (market) selection.Design/methodology/approachThe present study proposes a novel decision-making approach, namely the REF-Sort technique. The proposed approach has many valuable contributions to the literature. First, it has an efficient basic algorithm and can be applied to solve highly complicated decision-making problems without requiring advanced mathematical knowledge. Besides, some characteristics differentiate REF-Sort apart from other techniques. REF-Sort employs the value or value range that reflects the most typical characteristic of the relevant class in assignment processes. The reference values in REF-Sort and center profiles are similar in this regard. On the other hand, class references can be defined as ranges in REF-Sort. Secondary values, called successors, can also be employed to assign a value to the appropriate class. REF-Sort can also determine the reference and successor values/ranges independently of the decision matrix. In addition, the proposed model is a maximally stable and consistent decision-making tool, as it is resistant to the rank reversal problem.FindingsThe current papers' findings indicate that countries have different features concerning investment. Hence, the current paper pointed out that only 22% of the 95 countries are investable, whereas 19% are risky. Thus, decision-makers should make detailed evaluations using robust, powerful, and practical decision-making tools to make more reasonable and logical decisions concerning country selection.Originality/valueThe current paper proposes a novel decision-making approach to evaluate. According to the authors' information, the proposed model has been applied to evaluate investable countries for the global supply chains for the first time.

Waste in fast-moving consumer goods (FMCGs) is a tremendous economic and ethical issue for retailers and the rest of society. Due to methodological weaknesses, previous studies are inadequate in prioritizing fundamental causes and drivers of retail food waste (RFW) in this context. This research explores the peculiar causes and drivers of RFW concerning different perishable FMCG categories. This research employs the fuzzy level-based weight assessment (F-LBWA) methodology to provide a robust and effective decision-making tool to retailers responsible for preventing waste in their stores. This research categorizes the causes and drivers into different product categories giving insight into the reasons and drivers that need more attention than others for each product category. The findings reveal that inappropriate buying/delivery is the most significant cause of waste for fruit and vegetables, dairy products, fresh meat, fish and seafood, and baked products, whereas improper storage is the most critical cause of waste for frozen food. The present work ensures practical implications for developing product category-specific waste management policies to improve retailers' efficiency, competitiveness, and profitability. For a developing country like Turkey, the applicable insights of this research can also serve all supply chain members and policymakers to prevent food waste through partnership.

Sustainable transport in cities has been gaining a lot of attraction recently and a core focus of engineering management. Internet of Things (IoT) is seen as a widely accepted technology that promotes sustainability through the interconnection of diverse computing sources for solving environmental problems. Previous studies on IoT have discussed interesting factors toward its adoption, but selecting a suitable IoT service provider (IoTSP) is an open challenge due to a diverse set of factors in practice. Driven by the challenge, in this article, a generalized fuzzy-based decision model is put forward for IoTSP selection, which is the prime objective of the study. Initially, a strength, weakness, opportunity, threat (SWOT) analysis is adopted to identify the crucial challenges in IoT adoption. Later, the relative significance of these challenges is calculated by adopting the regret/rejoice approach. Due to uncertainty, certain rating information of IoTSPs is missing that are rationally imputed by proposing an analytical approach. Rating matrices from experts are transformed into opinion vectors, and a prioritization algorithm is developed with query vector for rational personalized ordering of IoTSPs. Data for the study are acquired via questionnaire, which is filled by experts. The efficacy of the developed model is exemplified by using a real case study of IoTSP selection for pollution management in Chennai. Concerning the findings, mobility, security, and connectivity are the most vital factors for IoTSP selection. Results show that the proposed model is a viable tool for IoTSP selection and it is robust, unique, and stable compared to its counterparts.

The COVID-19 pandemic has created unexpected demand for air cargo in terms of rapid mobility of critical basic needs. Air cargo carriers aim to maximize their profits by taking advantage of the current demand and using their limited capacity in the right place. At this point, some of the qualifications of the airports in the places where demand plays a crucial role in this decision of the carriers. Thus, evaluating the factors considered in the airport selection for air cargo carriers during the COVID-19 period is curious. This study proposes a triangular fuzzy Dombi-Bonferroni best-worst method (BWM) framework with vast flexibility to establish the priority preferences of factors considered in selecting airports. The fuzzy BWM model becomes a superior decision support system by combining the Bonferroni mean operator's ability to consider interrelationships between attributes and the flexibility of the Dombi operator. In this sense, we highlight eighteen criteria based on five airport aspects: location, physical features, performance, costs, and reputation. Findings reveal that the foremost aspects are location and costs, whereas the most crucial factors are airport charges and handling charges. The study suggests that airports should follow a low-price policy for airport-related charges without compromising their sustainability to have a share of the increasing number of air cargo flights, especially during the COVID-19 period, when airline passenger flights are decreased. The study is crucial in deciding the strategy and policy of air cargo carriers and airports during the pandemic period.

Blockchain technologies, which form the basis of Industry 4.0, paved the way for cryptocurrencies to emerge as technological innovation in the technology age. Recently, investors worldwide have been interested in cryptocurrencies with increasing acceleration due to high earning expectations though they have no backing and intrinsic value. As such, this paper seeks to identify the most proper cryptocurrencies from an investment standpoint in our technological era. Fifteen well-known cryptocurrencies with the highest market capitalization are evaluated as per sixteen factors. An intuitionistic fuzzy set-driven methodology incorporating Evaluation Based on Distance from Average Solution (EDAS), Multi-Attributive Ideal Real Comparative Analysis (MAIRCA, and Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS), which is the study’s prominent novelty, has been applied to provide a strong group decision vehicle for cryptocurrency selection. Notwithstanding, although the results obtained with the three approaches are highly consistent, investors would not like to doubt the instrument they will invest in. The Borda count is then applied to obtain a compromise for the rankings obtained from each approach. As per our findings, Ethereum, Tether, and Bitcoin are the most suitable cryptocurrencies, whereas reliable software, ease of inclusion in the wallet, and stability are the foremost factors to consider when investing in cryptocurrencies. The findings are further discussed in detail from a financial perspective. The proposed approach could be employed to select different investment instruments in future studies.

In this study, an integrated multi-criteria framework is developed to evaluate a healthcare sector which is one of the main infrastructures for any country. Healthcare sector plays a significant role in economic development and social sustainability of countries. To improve performance of healthcare sectors, it is essentially required to evaluate the healthcare systems based on their specific characteristics in order to resolve their performance related issues based on sustainable development principles under social aspect. For this purpose, the proposed integrated framework applies a novel hybrid weight determination model using best-worst method (BWM) and level based weight assessment (LBWA) to determine the weights of healthcare indicators and subsequently, combined compromise solution (CoCoSo) method is further applied to evaluate healthcare performances of several countries according to the pre-determined indicator weights. To show applicability of the proposed framework, a real time case study for seven countries in Eastern Europe is considered based on the data set of Organisation for Economic Co-operation and Development (OECD). Results show that Lithuania and Slovakia have the best healthcare systems in comparison to countries like Poland and Estonia.

Cryptocurrencies have brought many innovations and discussions to economic life. Digital assets, which are very popular by investors, are frequently used for many purposes such as store of value, exchange, and speculation. It creates a research area that intentions cryptocurrency experts prioritize in crypto investments. In this paper, therefore, the fuzzy Full Consistency Method-Bonferroni (FUCOM-F’B) model is conducted to determine the priorities of drivers for investing in cryptocurrencies. The selected twenty-three drivers are classified based on five aspects, including functionality, financial, legal infrastructure, technology, and security. Based on the findings, “strong electronic encryption” and “use of digital signature” are the most significant drivers for preferring a cryptocurrency. A validation check is performed to verify the reliability, usefulness, and stability of the proposed approach. Further, the introduced approach allows taking the ambiguities and subjectivity into account which exist in the decision-making procedure. The suggested framework can be a helpful decision support tool for regulators, policymakers, practitioners, and cryptocurrency investors.