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With the advent of healthy visions, two of the trends that have become extremely important in the supply chain in recent decades are corporate social responsibility (CSR) and sustainability, which have affected the activities of buyers and suppliers. The next trend that is emerging is the vision of creating shared value (CSV), which wants to move the supply chain toward solving social problems in a completely strategic way. This research intends to develop a step-by-step framework for evaluating and segmenting suppliers based on CSV criteria in the supply chain. In the first stage, the criteria for creating sustainable shared value (CSSV) are obtained through existing activities in the field of CSR. The obtained criteria are then divided into two categories, strategic and critical, and then the weight of each criterion is obtained using the best–worst method (BWM). In the next step, based on the Kraljic model, the suppliers are divided into four clusters using the preference ranking organization method for enrichment evaluation (PROMETHEE) technique. This framework helps the buyer to conclude and select purchasing decisions and relationships with suppliers through the lenses of CSV and sustainability.
ABSTRACT
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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Multi-Criteria Decision Making (MCDM) methods help researchers in solving many prob-lems in terms of numerical analysis. However, MCDM methods have not been very popular in the health sector. In this study, five ones of Turkey's most intense and highly populated cities were selected and the risk of the spread of Covid-19 disease was evaluated on the basis of seven criteria. The PROMETHEE and the ELECTRE methods were conducted to rank the cities in terms of the spread of Covid-19. The PROMETHEE method correctly ranked the most risky city as Istanbul, but ELECTRE ranked Istanbul the second most risky. The results of the meth-ods are compared with real data. PROMETHEE gave more convenient results than ELECTRE. Also, this paper offers a new field of study to the literature.Cite this article as: Pekel ozmen E, Demir B. The analysis of risk assessment for the trans-mission of COVID-19 by using PROMETHEE and ELECTRE methods. Sigma J Eng Nat Sci 2023;41:2:232-242.
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This paper presents a group decision-making mechanism to properly manage ranking problems in an intuitionistic fuzzy environment. TOPSIS ranking multi-criteria decision-making (MCDM) methods is utilized under the intuitionistic fuzzy set theory. This solution technique examines the sets of criteria employed in decision-making problems, the preferences of a group of decision-makers, and the importance levels of decision-makers. Managers use the ranking methods as a reliable technique for making supplier evaluation decisions. Furthermore, the supply chain suffers from the shortage of materials, transportation problems, etc. In the post COVID-19 era, the need for a practical and exhaustive tool is explicit. An illustrative case on a supplier selection problem considering sustainability and risks in the post-COVID-19 era is used to demonstrate the applicability of the proposed technique by detailing the procedure step by step. A comparative analysis of the results is carried out. The results are compared with the results of the MARCOS method. The results show that the presented methodology is applicable to the other areas as well. (English) [ FROM AUTHOR] Bu makale, sezgisel bulanık bir ortamda sıralama problemlerini düzgün bir şekilde yönetmek için bir grup karar verme mekanizması sunmaktadır. Sezgisel bulanık küme teorisi kapsamında çok kriterli karar verme (ÇKKV) yöntemi olan TOPSIS kullanılmaktadır. Bu çözüm tekniğinde karar verme problemlerinde kullanılan birtakım kriterler, karar vericiler grubunun tercihleri ve karar vericilerin önem düzeyleri incelenmektedir. Yöneticiler, sıralama yöntemlerini tedarikçi değerlendirme kararlarını vermek için güvenilir bir teknik olarak kullanır. Ayrıca, COVID-19 döneminden sonra tedarik zinciri malzeme sıkıntısı, ulaşım sorunları vb. sıkıntılardan muzdariptir, pratik ve kapsamlı bir araca olan ihtiyaç açıktır. Prosedürü adım adım detaylandırarak önerilen tekniğin uygulanabilirliğini göstermek için, COVID-19 sonrası dönemde sürdürülebilirliği ve riskleri dikkate alan bir tedarikçi seçimi sorununa ilişkin örnek bir vaka kullanılmıştır. Sonuçların karşılaştırmalı analizi gerçekleştirilmiştir. Sonuçlar, MARCOS yönteminin sonuçları ile karşılaştırılmıştır. Sonuçlar, sunulan metodolojinin diğer alanlara da uygulanabilir olduğunu göstermektedir. (Turkish) [ FROM AUTHOR] Copyright of Pamukkale University Journal of Social Sciences Institute / Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi is the property of Pamukkale University, Social Sciences Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Wearing a face mask can help reduce the spread of infection and contamination from airborne harmful germs. The requirement to wear a face mask is perhaps one of the most noticeable lifestyle changes brought on by the COVID-19 pandemic. COVID-19 transmission can be slowed down by wearing a mask, especially while in close contact with others. Choosing the best face mask is a cumbersome task from the available alternatives in India. Several multi-criteria decision-making (MCDM) techniques and approaches have been suggested to choose the optimally probable options. The purpose of this article is to deliver an entropy-distance measure for Pythagorean fuzzy sets. To validate these measures, some of the properties were also proved. A multi-criteria decision-making approach is used to rank and hence select the best face mask for wearing. The proposed research allows the ranking of face masks based on specified criteria in a Pythagorean fuzzy environment to aid in the selection process. The results suggest that the proposed model provides a realistic way to select the best mask in the pool of considered brands. A case study on the selection process and its experimental results using Pythagorean fuzzy sets are discussed. © 2023 River Publishers. All rights reserved.
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In a multi-objective setting, a portfolio manager's highly consequential decisions can benefit from assessing alternative forecasting models of stock index movement. The present investigation proposes a new approach to identify a set of non-dominated neural network models for further selection by the decision-maker. A new co-evolution approach is proposed to simultaneously select the features and topology of neural networks (collectively referred to as neural architecture), where the features are viewed from a topological perspective as input neurons. Further, the co-evolution is posed as a multi-criteria problem to evolve sparse and efficacious neural architectures. The well-known dominance and decomposition based multi-objective evolutionary algorithms are augmented with a non-geometric crossover operator to diversify and balance the search for neural architectures across conflicting criteria. Moreover, the co-evolution is augmented to accommodate the data-based implications of distinct market behaviors prior to and during the ongoing COVID-19 pandemic. A detailed comparative evaluation is carried out with the conventional sequential approach of feature selection followed by neural topology design, as well as a scalarized co-evolution approach. The results on three market indices (NASDAQ, NYSE, and S&P500) in pre- and peri-COVID time windows convincingly demonstrate that the proposed co-evolution approach can evolve a set of non-dominated neural forecasting models with better generalization capabilities.
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Online education has been still a common way for teaching and learning in the post epidemic era. However, the related research on service quality for the online Yue kiln celadon art education industry is still a vital research gap during this period. Thus, a hybrid method of FANP and GRA is proposed in this study to analyse and evaluate the key factors for providing and maintaining high service quality of online Yue kiln celadon art education industry in the post coronavirus era. In this research, whether in the model of FANP and GRA, factors such as safety mechanism of transaction and education, personnel quality, and the ability of customer need handling are essential conditions for providing excellent service quality in the post-COVID-19 era. The main contribution of this study is to propose an integrated method of FANP and GRA to calculate and rank potential solutions of online Yue kiln celadon art education service quality in the post-COVID-19 era under fuzzy environment and discrete conditions. Finally, the research findings of this study have a guiding role, thereby becoming a guide for the industries related to online Yue kiln celadon art education to maintain good service quality in similar scenarios in the future. © 2023 by the authors.
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Supply chains have been severely disrupted globally due to the COVID-19 pandemic. The paper examined the strategic responses of automobile firms for meeting supply chain challenges they face post-pandemic. Data were collected using a specifically designed structured questionnaire from supply chain experts working with leading automobile manufacturing firms in India. The fuzzy analytic hierarchy process (FAHP), as a part of a multi-criteria decision-making model using R programming, was applied to identify and rank the choice of supply strategies using various criteria, such as lead time, logistics cost (holding cost, carrying cost, warehousing cost, handling cost), and the need of products. Two-wheeler and four-wheeler manufacturing firms were selected for the study. Logistics cost was found to be a dominant criterion, followed by a demand for products and lead time, which helped select an appropriate supply chain strategy. Buffering was observed to be the best strategic choice, and automation and robotics applications were the least preferred ones both for two-wheelers and four-wheeler manufacturing companies. The findings would be helpful to both practitioners and researchers in evaluating diverse strategic choices, especially under the risk and disruptions faced by business firms in the supply chain. © 2023, Associated Management Consultants Pvt. Ltd.. All rights reserved.
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Environmental pollution has been a major concern for researchers and policymakers. A number of studies have been conducted to enquire the causes of environmental pollution which suggested numerous policies and techniques as remedial measures. One such major source of environmental pollution, as reported by previous studies, has been the garbage resulting from disposed hospital wastes. The recent outbreak of the COVID-19 pandemic has resulted into mass generation of medical waste which seems to have further deteriorated the issue of environmental pollution. This necessitates active attention from both the researchers and policymakers for effective management of medical waste to prevent the harm to environment and human health. The issue of medical waste management is more important for countries lacking sophisticated medical infrastructure. Accordingly, the purpose of this study is to propose a novel application for identification and classification of 10 hospitals in Iraq which generated more medical waste during the COVID-19 pandemic than others in order to address the issue more effectively. We used the Multi-Criteria Decision Making (MCDM) method to this end. We integrated MCDM with other techniques including the Analytic Hierarchy Process (AHP), linear Diophantine fuzzy set decision by opinion score method (LDFN-FDOSM), and Artificial Neural Network (ANN) analysis to generate more robust results. We classified medical waste into five categories, i.e., general waste, sharp waste, pharmaceutical waste, infectious waste, and pathological waste. We consulted 313 experts to help in identifying the best and the worst medical waste management technique within the perspectives of circular economy using the neural network approach. The findings revealed that incineration technique, microwave technique, pyrolysis technique, autoclave chemical technique, vaporized hydrogen peroxide, dry heat, ozone, and ultraviolet light were the most effective methods to dispose of medical waste during the pandemic. Additionally, ozone was identified as the most suitable technique among all to serve the purpose of circular economy of medical waste. We conclude by discussing the practical implications to guide governments and policy makers to benefit from the circular economy of medical waste to turn pollutant hospitals into sustainable ones.
Subject(s)
COVID-19 , Medical Waste , Waste Management , Humans , Pandemics , IncinerationABSTRACT
Coronavirus diseases 2019 (COVID-19) pandemic is an essential challenge to the health and safety of people, medical members, and treatment systems worldwide. Digital technologies (DTs) have been universally introduced to improve the treatment of patients during the pandemic. Nevertheless, only a few governments have been partly successful in executing the DT strategies. In this regard, it is critical to demonstrate a suitable strategy for the governments. This problem is built based on the experts' opinions with some conflicting criteria to evaluate various types of alternatives. Hence, this research presents a new multi-criteria decision-making (MCDM) model under uncertain conditions. For this reason, interval-valued intuitionistic fuzzy sets (IVIFSs) are employed to help decision-makers (DMs) evaluate in a broader area and cope with uncertain information. Moreover, a new extended weighting method based on weighted distance-based approximation (WDBA) and a new combined ranking approach are proposed to determine the DMs' weights and rank the alternatives under IVIF conditions. The developed weighting method is constructed based on computing the DMs' weights with objective criteria weights. Furthermore, a new ranking approach is proposed by obtaining two ranking indexes separately: The first and second ranking indexes are calculated according to the positive and negative ideal solutions distances and the nature of criteria weights, respectively. Afterward, the final values of rankings are computed by considering a new aggregating procedure. The results of the proposed model represent the first alternative as the best strategy. Comparisons between the IVIF-TOPSIS and IVIF-VIKOR methods are also provided to investigate the proposed model to determine the rankings of main alternatives. Sensitivity analyses are conducted to check the reliability and the robustness of the model. For this purpose, criteria weights are analyzed to compute the dependencies' degree of the new extended weighting method. The dependencies of the ranking model are discussed on the criteria weights as well.
ABSTRACT
The tourism industry has developed rapidly in recent decades. This boom has led to overtourism in some tourist areas of Spain. The COVID-19, on the other hand, has paralysed the sector and must be reactivated without reproducing past errors. Overtourism remains open to subjective interpretations due to the paucity of precise measurements. This emanates from the fact that overtourism lacks a realistic set of indicators to quantitatively measure its negative impact. The main objective of this paper is to provide a set of indicators that allow reviewing and redirecting decision-making at the business and political levels. An integrated MCDM approach has been applied namely the CRITIC method for calculating the weights of the indicators, and the VIKOR method for assessing the alternatives. The case study of Spain provides useful insights, and the obtained results demonstrated that the economic indicators hinder sustainable development and contribute to worsening the tourist attitude. Copyright © 2022 Inderscience Enterprises Ltd.
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The blended educational method has become a common way of teaching and learning in the post-COVID-19 era. However, the related research on the selection model for the blended design teaching service quality solution is still an important research gap during this period. Therefore, this study proposed a hybrid method of fuzzy analytic network process (FANP) and technique for order preference by similarity to ideal solution (TOPSIS) to analyse the dimensions, indicators and alternatives of blended design teaching service quality. As for the findings of this research, the dimension of assurance is the most vital factor, followed by responsiveness, reliability and empathy. Meanwhile, this research discovered that the top three significant alternatives are "Employees are trustworthy”, "Safe transaction mechanism and environment” and "Personalised needs of customers”. Also, we found that dimensions utilised to evaluate the quality of education service are similar whether in the post COVID-19 era, in the COVID-19 epidemic or prior to the COVID-19 pandemic. The main contribution of this study is to establish a multi-criteria decision-making (MCDM) model for the ranking of the blended design teaching service quality index and solution under a fuzzy environment. Finally, the research findings of this study have a guiding role, thereby becoming a guide for the industries related to hybrid design education to maintain good service quality in similar scenarios in the future. © 2023 by the authors.
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Purpose: The purpose of this study is to improve supplier performance and strategic sourcing decisions by integrating jobshop scheduling, inventory management and agile new product development. During the COVID-19 pandemic, the organizations have struggled a lot to maintain the supplier performance and strategic sourcing decisions in the organizational benefit. However, in this context, the organization's agile new product development (ANPD) process must be aligned with this requirement by maintaining the inventory and jobshop scheduling. As a result, identifying ANPD indicators, performance metrics and developing a structural framework to guide practitioners at various stages for smooth adoption is essential to improve the overall performance. Design/methodology/approach: A comprehensive literature review is conducted to identify jobshop scheduling, inventory management and ANPD indicators along with the performance metrics, and the hierarchical structure is developed with the help of expert opinion. The modified stepwise weight assessment ratio analysis (SWARA) and weighted aggregated sum product assurance (WASPAS) techniques, along with expert judgement, are used in this study to calculate the weights of the indicators and the ranking of the performance metrics. Findings: As per the weight computation by SWARA method, the strategy indicators have the highest relative weight, followed by the product design indicators, management indicators, technical indicators, supply chain indicators and organization culture indicators. According to the ranking of performance metrics obtained through WASPAS, the "frequency of new product development is at the top”, followed by "advances in product design and development” and "estimated versus actual time to market”. Research limitations/implications: It is believed that the framework developed will help industrial practitioners to plan effectively to improve supplier performance. The indicators identified may guide the ANPD penetration, and performance metrics may be useful for evaluation and comparison. Practical implications: The outcomes of the present study will be extremely beneficial for the industry practitioners to improve the supplier performance. The indicators identified may guide the ANPD penetration, and performance metrics may be useful for evaluation and comparison. Originality/value: A unique combination of modified SWARA–WASPAS technique has been used in this study which would be beneficial for organizations willing to adopt the jobshop scheduling and inventory management and ANPD for improving supply chain performance. © 2023, Emerald Publishing Limited.
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Since the beginning of 2020, "Covid-19” has affected the whole world in an unprecedented way in modern times. It is inevitable that this pandemic, which has negatively affected many fields, will also have an impact on academic journals. The aim of this study is to determine the effect of the Covid-19 pandemic on the performance of academic journals. In our study, a "Data Envelopment Analysis” methodology with 3 inputs and 3 outputs was used to determine the relative "performance of the journals”. Within the scope of the study, 109 journals published in "Turkey” and scanned in "Web of Science” indexes were examined. Results show that eleven journals were efficient in 2019, while in 2020 this number decreased to seven. Four fields have been positively affected by the pandemic and journals publishing in these fields have increased their efficiencies. Eighteen fields were adversely affected by the pandemic and the efficiency of journal publishing in these fields decreased. Eleven fields and journals publishing in these fields maintained their efficiency both before and during the pandemic. As the Covid-19 pandemic is not over yet, our data is limited. In the coming years, more detailed and comprehensive studies can be carried out with more extensive data and a further number of journals from different countries. © 2022 Pontificia Universidade Catolica de Campinas. All rights reserved.
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This chapter discusses an important topic in factory management, that of improving the understandability of AI applications for group multi-criteria decision making in manufacturing systems. Due to its long-term and cross-functional impact, decision making may be more critical to the competitiveness and sustainability of manufacturing systems than production planning and control. This chapter uses the example of choosing the right smart and automation technologies for factories during the COVID-19 pandemic. This topic is of particular importance as many factories are forced to close or operate on a smaller scale (using a smaller workforce), thus pursuing further automation. Artificial intelligence and Industry 4.0 technologies have many applications in this area, most of which can also be applied for other decision-making purposes in manufacturing systems. First, a systematic procedure was established to guide the group multi-criteria decision-making process. Applications of AI and XAI to identify targets are first reviewed. Subsequently, the application of AI and XAI to selection factors and development of criteria is presented. Artificial intelligence techniques are widely used to derive criteria priorities. Therefore, it is particularly important to explain XAI techniques and tools for such AI applications. Aggregating the judgments of multiple decision makers is the next focus, followed by the introduction of AI and XAI applications to evaluate the overall performance of each alternative. Taking fuzzy ranking preference based on similarity to ideal solution (FTOPSIS) as an example, the application of XAI techniques and tools in explaining comparison results using FTOPSIS is illustrated. Another AI technology used for the same purpose is fuzzy VIKOR. XAI techniques and tools for interpreting fuzzy VIKOR are also presented. Finally, several metrics are proposed to evaluate the effectiveness of XAI techniques or tools for decision making in the manufacturing domain. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Covid-19 pandemisinin tüm dünyaya yayılması sonucunda tüm sektörler ekonomik olarak bu krizden etkilenmiştir. Bu krizden etkilen önemli sektörlerden birisi de otomotiv sektörü olmuştur. Küresel ekonominin %4'ünü oluşturan otomotiv sektörü bu süreçte birçok sorunla mücadele etmek durumunda kalmıştır. Bu bağlamda bu çalışma Covid-19 döneminde (2020-2021) ve öncesinde (2018-2019) BÍST'te faaliyet gösteren otomotiv firmalarının finansal performanslarını analiz etmeyi amaçlamıştır. Bu noktadan hareketle firmaların finansal performanslarını analiz etmede çok kriterli karar verme yöntemlerinden birisi olan CRITIC tabanlı CoCoSo yöntemi kullanılmıştır. CRITIC yöntemiyle yapılan analiz sonucunda en fazla önem ağırlığına sahip kriter, özsermaye devir hızı rasyosu olurken;en az önem ağırlığına sahip kriter ise ekonomik rantabilite rasyosu olmuştur. CoCoSo yöntemiyle yapılan analiz sonucunda ise finansal performans sıralamasında FMÍZP ve FROTO firmaları ilk iki sırayı alırken KATMR ve BALAT firmaları ise son sıralarda yer almışlardır. Pandeminin etkisiyle elde edilen analiz sonuçları birlikte incelendiğinde pandemi öncesi dönemle pandemi döneminde finansal performans sıralamasında genel olarak bir değişiklik olmadığı, pandeminin firmaların finansal performans sıralamalarını değiştirmediği görülmektedir.Alternate : As a result of the spread of the Covid-19 pandemic worldwide, all sectors have been economically affected by this crisis. One of these sectors is automotive sector. The automotive sector, which constitutes 4% of the global economy, had to struggle with many problems in this process. In this context, this study aimed to analyse the financial performances of automotive companies operating in the BIST during the Covid-19 period (2020-2021) and before (2018-2019). From this point of view, the CRITIC-based COCOSO method, one of the multi-criteria decision-making methods, was used to analyse the financial performance of companies. As a result of the analysis conducted by the CRITIC method, the most important criterion was the equity turnover ratio. The criterion with the least important was the economic profitability ratio. As a result of the analysis made with the CoCoSo method, FMÍZP and FROTO companies took the first two places in the financial performance ranking, while KATMR and BALAT companies took the last place. When the analysis results obtained with the effect of the pandemic are examined together, it is seen that there was no general change in the financial performance rankings between the pre-pandemic period and the pandemic period, and the pandemic did not change the financial performance rankings of the companies.
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This research article proposes the service quality measures for four automotive lead logistics providers (LLPs) to improve the parts export business based at emerging markets such as India, China and Thailand. These measures have emerged from the pre-COVID-19 perception study on service quality from 117 global automotive professionals from auto original equipment manufacturers (OEMs). Multi-criteria decision-making (MCDM) framework and questionnaire survey method was applied. It concludes with the results of different methods such as service quality (SERVQUAL), importance-performance analysis (IPA) and improvement factor (IF), to explain the significance of export business and where to improve their service quality post-COVID-19. The result shows that the service quality factors of IPA model had wide-ranging reflection for LLPs to focus in comparison with SERVQUAL and IF to gauge the perception of automotive OEMs concerning the service quality of LLPs in the industry post-COVID-19. Copyright © 2022 Inderscience Enterprises Ltd.
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COVID-19 is a worldwide health threat that has resulted in a significant number of deaths and complicated healthcare management issues. To prevent the COVID-19 pandemic, there is a need to choose a safe and most effective vaccine. Several Multi-criteria Decision-Making (MADM) techniques and approaches have been selected to choose the optimal probable options. The purpose of this article is to deliver divergence measures for fuzzy sets. To validate these measures, some of the properties were also proved. The Multi-criteria Decision-Making method is employed to rank and hence select the best vaccine out of available alternatives. The proposed research allows the ranking of different vaccines based on specified criteria in a fuzzy environment to aid in the selection process. The results suggest that the proposed model provides a realistic way to select the best vaccine from the vaccines available. A case study on the selection of the best COVID-19 vaccine and its experimental results using fuzzy sets are discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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A mobile application (app) recommender system needs to support both developers and users. Existing recommender systems in the literature are based on single-criterion analysis, which is insufficient for producing better recommendations. Moreover, recommendations do not reflect the user's perspectives. To address these issues, in this paper, we present a Multi-Criteria Mobile App Recommender System (MCMARS) that assists developers in improving their apps and recommends the top-performing apps to users. We define the performance score of an app based on four criteria attributes: risk assessment score, functionality score, user rating, and the app's memory size. We define the risk assessment score for each app using multi-perspective analysis and the functionality score by assigning preference weights to the services of apps in the same category. We evaluate optimal weights of the criteria by integrating the entropy method and the extended Best-Worst method (BWM) using Hesitant-Triangular-Fuzzy information with group-decisions. Finally, the TOPSIS uses these weights to assess the app's performance. To validate our MCMARS, we prepared a dataset of 124 government-approved COVID-19 Android apps from 80 countries and made it available on GitHub for the research community. Finally, we perform a fine-grained analysis of the app's performance based on the criteria attributes that help the developers to improve their apps. The experimental results show that two independent attributes, "risk assessment score” and "functionality score”, significantly measure the app's performance. According to our findings, only 12.5% of the apps in the experimental dataset provide high-performance, high-functionality, and low-risk. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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E-commerce, which is defined as making commercial transactions in an electronic environment, is becoming widespread with the increase of the use of internet and mobile devices. COVID-19 has greatly changed the consumption habits of individuals, increasing interest in electronic sales channels. Regardless of their size, most companies and retailers are currently looking for ways to engage their customers through electronic channels due to the effect of COVID-19. In this process, the rapidly increasing trend of electronic commerce raises an important question for companies, "In which e-marketplace should we sell?” In this study, five criteria that are important in the choice of the right e-marketplace were determined and eight online alternative e-marketplaces were evaluated. The study was carried out using the neutrosophic fuzzy AHP and EDAS methods, which are multi-criteria decision making techniques, and a framework was established for choosing the right e-commerce marketplace for sellers. © 2022,International Journal of the Analytic Hierarchy Process. All Rights Reserved.