Due to the advances in wireless network environments, consumers/end-user behaviors continue to expand in cyberspace. Similarly, university students (i.e. universities’ consumers) can easily shift from one university to another. In recent years, decision-makers in educational organizations have faced multi-criteria decision making (MCDM) problems in e-service adoption in order to improve quality standards and maintain students’ retention in highly competitive education environments. Generally, many required criteria in MCDM cannot be evaluated accurately since accurate data cannot be obtained from the decision makers’ assessments. Thus, this research aims to propose a decision-making model for identifying the factors that highly impact on e-service adoption in educational organizations. This new model combined the fuzzy Decision MAking Trial and Evaluation Laboratory (fuzzy DEMATEL) and fuzzy Techniques for Order of Preference by Similarity to Ideal Solution (fuzzy TOPSIS) to weight the interactions among the factors which were defined from a comprehensive review of literature and to determine the relative importance of these factors. The findings from our new proposed model: fuzzy DEMATEL-TOPSIS showed that environmental factors are the most important for effective e-service adoption among educational organizations in India. The proposed decision making model could guide educational organizations to improve their decisions related to technology adoption in their organizations. The conclusions and practical insights gleaned from this research could also hopefully be useful to school authorities in assisting with the adoption, acceptance, and usage of e-services.

E-commerce is one of the industries most affected by big data, from collection to analytics in the highly competitive market. Previous research on big data analytics in E-commerce focused only on particular applications, and there is still a gap in presenting a framework to evaluate big data applications from a challenges-values point of view. This study employs a three-phase methodology to evaluate big data applications in E-commerce with respect to big data challenges and values using a hybrid multi-criteria decision-making technique that combines BWM and fuzzy TOPSIS. The results showed process challenge and the strategic value obtained the highest weight for challenges and values criteria. Financial fraud detection is relatively the most challenging, and online review analytics is the most valuable application of big data in E-commerce among identified applications. Evaluating big data applications based on cost and benefit criteria is practical for E-commerce managers and experts to make decisions on implementation priorities to overcome the challenges and make the most of values. Moreover, the proposed approach is not only limited to big data analytics in E-commerce and can also be applied in other industries to evaluate emerging technology applications.

Due to the important environmental effects on human life and the conflict between the mining process and environmental factors, the enactment of protectionist environmental laws in the mineral industry has received extensive attention especially in countries with high mining potential. In this regard, using an extended fuzzy TOPSIS method, this study develops a novel conceptual framework to identify the key protection laws in Iran by considering the mineral successful laws and regulations in mineral-developed countries such as Australia, Chile, India, Turkey, Canada, South Africa, and China. This helps to apply the experiences of the abovementioned countries in resolving similar conflicts in Iran’s mineral industry. The key protection laws are selected based on their national and international environmental treaties, mines’ environmental protection laws, conflict solution methods, social responsibilities in mining activity, and laws for the abandoned mines. The proposed framework demonstrates that Iran’s mineral laws require revision and more transparency to avoid ambiguity and conflict with mineral property laws and governmental rights.

Variation exists in all processes. There is not even a single process that is completely true. Measuring the trueness of the process is itself a process which can also imitate the process variation. Therefore, measurement system should be strong enough to wager on the trueness of the process. This paper is an attempt to indicate the true method and substantiate the use of measurement system analysis (MSA) by using it in two different environments i.e. in manufacturing as well as process industry. Also, a comparison among various analyzing techniques has been drawn for authenticating the candid method followed by an evaluation using fuzzy TOPSIS for authenticating the results of comparison. The organization’s type, also, strongly influences the performance of MSA as revealed in the conclusion of the article. The results calculated by various methods and in both environments were discussed and as a result ANOVA comes out to be the best method. The application of correct MSA is highly required which ultimately results in increased organizations’ performance. The study is one of its type and will motivate the researchers and industrialists to use and explore the new and efficient ways of MSA.

One of the most promising strategies that will smooth out the maintainability issues of the software is refactoring. Due to lack of proper design approach, the code often inherits some bad smells which may lead to improper functioning of the code, especially when it is subject to change and requires some maintenance. A lot of studies have been performed to optimize the refactoring strategy which is also a very expensive process. In this paper, a component based system is considered, and a Fuzzy Multi Criteria Decision Making (FMCDM) model is proposed by combining subjective and objective weights to rank the components as per their urgency of refactoring. Jdeodorant tool is used to detect the code smells from the individual components of a software system. The objective method uses the Entropy approach to rank the component having the code smell. The subjective method uses the Fuzzy TOPSIS approach based on decision makers’ judgement, to identify the critically and dependency of these code smells on the overall software. The suggested approach is implemented on component-based software having 15 components. The constitute components are ranked based on refactoring requirements.

Nowadays, traffic safety is one of the primary principles of traffic engineering and transportation planning. Road accidents have been major problems that cause human losses, social and economic challenges. The first step to improve road traffic safety is to identify black spots, which preserve high potential of road accidents. Therefore, identifying, analyzing, prioritizing and refining these places play important role in improving transportation safety. This paper applies fuzzy Technique for Order Preference by Similarity to Ideal (TOPSIS) for ordering different black spots for a case study of highway between two cities of Qazvin and Saveh, Iran. The study performs the ranking based on two categories of tangent distance and horizontal curve. The ranking is performed according to Roadway width, Shoulder width, Traffic volume, Percentage of vehicle, Load and utility, Network access, The number of horizontal curves and Equivalent Property Damage Only (EPDO). The results are compared with frequencies of incidents and analyzed.

Supplier selection is a complex multi-criteria decision making (MCDM) problem. There are literally various methods for choosing appropriate supplier but there are several criteria involved in complex decision making process. The classical MCDM methods cannot effectively solve real-world problems however fuzzy MCDM methods facilitate the solution fairly and enable the decision-makers to reach accurate decisions in this selection process. In this study, a supplier selection problem is handled, in a firm in automotive industry of Turkey. Fuzzy TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) and generalized Choquet integral are used individually in the solution of the problem.

Flow of information in supply chain is as prominent as material and financial flows and among these three aspects of a supply chain, information flow could be of great importance since it provides a basis for a steady flow of goods and finance as well. To help managers control the flow of information in an effective way, first, the parameters which affect the flow of information should be determined. Then, if the parameters can be controlled carefully, the information will be shared correctly and in a timely manner among supply chain members. This paper identifies the influencing parameters on proper flow of information in supply chain and provides a list of parameters based on the literature as well as the industrial and academic experts’ opinions. Afterwards, in order to define the degree of importance for each parameter from the experts’ perspective, fuzzy TOPSIS method is employed and the parameters are ranked based on three criteria, namely “measurability”, “being illustrative” and “parameters relevancy” to the issue of information flow. The research findings show that “Supply Chain Hardware Capabilities”, “Supply Chain Network Infrastructure”, “Information Software Capabilities”, “Information Sharing Timeliness”, “Information Recency” and “Organizational Rewards” received the highest priorities, while “Power of Internal and Inter-personal Communications”, “Users’ Trust” and “Users’ Tendency” were standing at the bottom of this ranking. The results of this research could be employed as an input for strategy development process for supply chain information management activities. Thus, the awareness of each parameter’s importance in proper flow of information, helps us make appropriate strategies to improve information management in the supply chain.

The advent of Industry 4.0 has revolutionized manufacturing, integrating advanced technologies to enhance efficiency and sustainability. However, the transition to sustainable green manufacturing presents numerous challenges. This paper analyzes these challenges using the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). By incorporating expert opinions and fuzzy logic, various obstacles are evaluated and prioritized in the implementation of green manufacturing practices in the context of Industry 4.0. The analysis reveals that market uncertainty in the economic landscape ranks as the top challenge, followed by high costs of implementation, maintenance, security, and integration. Uncertain benefits and trade-offs are also found as significant barriers. Key factors include the need for substantial investments, cybersecurity concerns, integration difficulties, and the complexities of predicting returns on investment. From the study, it is also evident that the impact of Industry 4.0 on supply chains and emissions from Electronics manufacturing is also a critical issue. The study provides actionable insights and strategic recommendations for policymakers and industry leaders to facilitate the adoption of sustainable green manufacturing practices in the era of Industry 4.0.

Extension of product’s useful life through product recovery options can help to reduce the consumption of natural resource and environmental pollution. Remanufacturing operation is considered as the most suitable product recovery option for automotive parts. The objective of this paper is to identify the barriers and the critical success factors in automotive engine remanufacturing. A case study is carried out in an automotive engine remanufacturing plant, situated in the south-western part of India. In this case study, the strategic, tactical and operational characteristics of the remanufacturing process are explored, what are the barriers they have faced while remanufacturing and to overcome those barriers, what strategies they have adopted are also analyzed. A fuzzy technique for order performance by similarity to ideal solutions (TOPSIS) is used to identify the critical success factor to overcome the roadblocks of remanufacturing. As this study is directed by the experts’ opinion from the remanufacturing plant which proves its practical implication. This case study is also useful for the other OEM remanufacturers in India.