Fuzzy multiple-criteria decision making in the determination of critical criteria for assessing service quality of travel websites

Travel websites provide customers diverse services including travel information and products through the internet. More and more people are used to plan their itineraries and acquire travel information by visiting travel websites. In the face of the strong competition environment, in order to profit by making customers proceed with transactions on the websites, travel websites should pay more attention to the improve the electronic service quality (e-SQ). e-SQ evaluation for travel websites can be treated as a multiple-criteria decision making (MCDM) problem modeled by the given hierarchical structure. As for evaluating e-SQ, since the traditional Likert scales cannot deal with uncertain assessments according to human intuition for the service quality evaluation, the fuzzy number is employed to measure decision-makers’ subjective preference. This paper aims to develop a genetic-algorithm-based method to automatically determine the degrees of importance of respective criteria. Then, critical criteria for evaluating service quality can be easily identified. In the empirical study, four representative travel websites in Taiwan are selected to determine the critical criteria concerned by customers. The findings provide useful information to travel websites for improving e-SQ.     

Evaluation of e-commerce websites using fuzzy hierarchical TOPSIS based on E-S-QUAL

In the wake of the rapid emergence of e-commerce, its evaluation is of great theoretical and practical importance. Among the various types of e-commerce, business-to-consumer (B2C) e-commerce has become a key, and especially influential, retailing channel. Its ascendancy raises core issues with respect to how the customer is to be satisfied by, and therefore inclined to trust, e-commerce websites. B2C e-commerce website evaluation, therefore, is an important related issue. First, while a number of studies have studied B2C e-commerce website evaluation using various multiple-criteria-decision-making (MCDM) methods, they have focused only on the perceived service quality of B2C e-commerce websites. In fact, it is generally recognized that service quality is determined by the difference between the expected service level (which expectation is derived from information obtained before the service experience) and the actual, perceived service level; this concept, then, should be also be afforded due consideration in B2C e-commerce website evaluation. Second, among the various MCDM approaches, TOPSIS, which involves the consideration of both the positive-ideal solution (PIS) and the negative-ideal solution (NIS), is especially pertinent to the complex decision-making entailed in the evaluation of B2C e-commerce websites. Third, the human element of subjectivity in the evaluation of B2C e-commerce websites needs to be considered in order to enable modeling of real-life website-evaluation situations. Finally, the hierarchical structure of the evaluation criteria between the main dimensions and their sub-criteria should be considered. To reflect these issues, in this paper, we present a fuzzy hierarchical TOPSIS based on E-SERVQUAL (E-S-QUAL). This approach effectively considers the raised issues and preserves the core concept of E-S-QUAL (the extended version of SERVQUAL) for measurement of electronic service quality in the e-commerce environment. The empirical case study of B2C e-commerce provides the researchers and practitioners to understand in a better way the evaluation process from a practical point of view. In addition, core finding of this study is that the comparison results with other MCDM methods further verify the robustness of the proposed approach. It implies that this method potentially can be applied to performance evaluation of similar service sectors.     

Evaluating emergency response capacity by fuzzy AHP and 2-tuple fuzzy linguistic approach

Emergency management (EM) is a very important issue with various kinds of emergency events frequently taking place. One of the most important components of EM is to evaluate the emergency response capacity (ERC) of emergency department or emergency alternative. Because of time pressure, lack of experience and data, experts often evaluate the importance and the ratings of qualitative criteria in the form of linguistic variable. This paper presents a hybrid fuzzy method consisting fuzzy AHP and 2-tuple fuzzy linguistic approach to evaluate emergency response capacity. This study has been done in three stages. In the first stage we present a hierarchy of the evaluation index system for emergency response capacity. In the second stage we use fuzzy AHP to analyze the structure of the emergency response capacity evaluation problem. Using linguistic variables, pairwise comparisons for the evaluation criteria and sub-criteria are made to determine the weights of the criteria and sub-criteria. In the third stage, the ratings of sub-criteria are assessed in linguistic values represented by triangular fuzzy numbers to express the qualitative evaluation of experts’ subjective opinions, and the linguistic values are transformed into 2-tuples. Use the 2-tuple linguistic weighted average operator (LWAO) to compute the aggregated ratings of criteria and the overall emergency response capacity (OERC) of the emergency alternative. Finally, we demonstrate the validity and feasibility of the proposed hybrid fuzzy approach by means of comparing the emergency response capacity of three emergency alternatives.     

Electronic service quality of Facebook social commerce and collaborative learning

The burgeoning growth of Web 2.0 and social media (i.e., social commerce) has changed the purchase behavior of consumers and businesses’ marketing strategies. However, there are no electronic service quality (e-SQ) criteria for this new phenomenon in the Internet environment. This article aims to utilize the dimensions of e-SQ to measure the service quality of commercial activities on social media (e.g., Facebook (FB)). It also applies an analytic hierarchy process (AHP) questionnaire of 50 students who have experience of using FB ads linkage and then utilizes the fuzzy analytic hierarchy process (FAHP) to analyze the weighting of the e-SQ evaluation. Moreover, this study shows the effectiveness of collaborative learning in exploring e-SQ on social networks among participating students from different countries. Additionally, the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is used to find the optimum dimensions of e-SQ and ideal commercial activities on FB. These findings demonstrate not only the dimensions of e-SQ that Facebook users prefer and which brands are ‘liked’ by them, but also highlight the opportunity for developing new e-SQ criteria of social commerce.     

An intelligent approach to supplier evaluation in automotive sector

During the process of supplier evaluation, selecting the best desirable supplier is one of the most critical problems of companies since improperly selected suppliers may cause losing time, cost and market share of a company. For this multiple-criteria decision making selection problem, in this paper, a fuzzy extension of analytic network process (ANP), which uses uncertain human preferences as input information in the decision-making process, is applied since conventional methods such as analytic hierarchy process cannot accommodate the variety of interactions, dependencies and feedback between higher and lower level elements. The resulting fuzzy ANP enhances the potential of the conventional ANP for dealing with imprecise and uncertain human comparison judgments. In short, in this paper, an intelligent approach to supplier selection problem through fuzzy ANP is proposed by taking into consideration quantitative and qualitative elements to evaluate supplier alternatives, and a case study in automotive sector is presented.     

Choosing knowledge management strategies by using a combined ANP and DEMATEL approach

Numerous companies are expecting their knowledge management (KM) to be performed effectively in order to leverage and transform the knowledge into competitive advantages. However, here raises a critical issue of how companies can better evaluate and select a favorable KM strategy prior to a successful KM implementation. The KM strategy selection is a kind of multiple criteria decision-making (MCDM) problem, which requires considering a large number of complex factors as multiple evaluation criteria. A robust MCDM method should consider the interactions among criteria. The analytic network process (ANP) is a relatively new MCDM method which can deal with all kinds of interactions systematically. Moreover, the Decision Making Trial and Evaluation Laboratory (DEMATEL) not only can convert the relations between cause and effect of criteria into a visual structural model, but also can be used as a way to handle the inner dependences within a set of criteria. Hence, this paper proposes an effective solution based on a combined ANP and DEMATEL approach to help companies that need to evaluate and select KM strategies. Additionally, an empirical study is presented to illustrate the application of the proposed method.     

A Novel IoT Application Recommendation System Using Metaheuristic Multi-Criteria Analysis

There are a variety of Internet of Things (IoT) applications that cover different aspects of daily life. Each of these applications has different criteria and sub-criteria, making it difficult for the user to choose. This requires an automated approach to select IoT applications by considering criteria. This paper presents a novel recommendation system for presenting applications on the IoT. First, using the analytic hierarchy process (AHP), a multi-layer architecture of the criteria and sub-criteria in IoT applications is presented. This architecture is used to evaluate and rank IoT applications. As a result, finding the weight of the criteria and sub-criteria requires a metaheuristic approach. In this paper, a sequential quadratic programming algorithm is used to find the optimal weight of the criteria and sub-criteria automatically. To the best of our knowledge, this is the first study to use an analysis of metaheuristic criteria and sub-criteria to design an IoT application recommendation system. The evaluations and comparisons in the experimental results section show that the proposed method is a comprehensive and reliable model for the construction of an IoT applications recommendation system.     

An integrated DEMATEL and Fuzzy ANP techniques for evaluation and selection of outsourcing provider for a telecommunication company

In recent years, strategy aspects related to core competency, risk analysis and organizational flexibility especially have been growing. This trend has led researchers and industries to become more interested in the multi-criteria decision making (MCDM) models for selecting outsourcing providers. The efficiency of decision-making mostly depends on the ability of decision-makers analyzing the complex cause and effect relationship between criteria and taking effective actions based on the analysis. Using an analytical method to select the most eligible outsourcing provider is significant for a company which desires to improve its competitiveness. In this study, a fuzzy integrated multi-criteria decision making method for evaluation and determination of an outsourcing provider for a telecommunication company is analyzed by using DEMATEL and Fuzzy ANP multi-criteria decision making techniques. First, DEMATEL method is used in order to put forward the interrelationship among the main criteria which are determined in the study for outsourcing selection process. Then, local weights of the sub-criteria and sub-subcriteria are calculated by Fuzzy ANP approach on the basis of cause-effect relationships that are exposed through DEMATEL method. The local weights are put into ANP supermatrix, and calculations are implemented to select out the most eligible outsourcing provider.     

Close-loop or open hierarchical structures in green supply chain management under uncertainty

This study aims to explore the differences between close-loop and open hierarchical structures, which are used in the analytical network process (ANP) analysis of green supply chain management (GSCM) under uncertainty. Specifically, this study examines the interdependence among the proposed aspects and criteria used to assess GSCM in two hierarchical structures and compares these results to those of a real situation. Additionally, the aspects and criteria involve qualitative preferences in GSCM evaluation. Hence, this study proposes to use both hybrid fuzzy set theory and ANP methods. Examining two types of hierarchical structure provides the ability to examine study problems that have similar aspects and criteria with respect to situations that occur in GSCM. This study argues that while many of the commonly employed close-loop and open hierarchical structures display numerous worthwhile operational processes, further refinement of the criteria and methods are needed to improve the field. The results indicate that the close-loop hierarchical structure more closely resembles existing applications. The managerial implications of this result are discussed, and concluding remarks are provided.     

Multi-class classification using hybrid soft decision model for agriculture crop selection

A hybrid soft decision model has been developed in this paper to take decision on agriculture crop that can be cultivated in a given experimental land by integrating few soft computing techniques. The proposed model comprises of three parts, namely weight calculation, classification and prediction. Twenty-seven input criteria were categorized into seven broad criteria, namely soil (11 sub-criteria), water (2 sub-criteria), season (no sub-criterion), input (6 sub-criteria), support (2 sub-criteria), facilities (3 sub-criteria) and risk (2 sub-criteria). In the proposed model, relative weights of main criteria were calculated using Shannon’s Entropy method and relative weights of sub-criteria in each main criterion were calculated using rough set approach. As VIKOR method is effective in sorting the alternatives, it is used to determine the ranking index of main criteria in this study. A soft decision system was constructed from the results of rough set method, VIKOR method and Shannon’s Entropy method. Classification rules were generated for five agriculture crops, namely paddy, groundnut, sugarcane, cumbu and ragi based on the soft decision system using bijective soft set approach. The developed model predicts each site in the validation dataset into one of the five crops. The performance of the proposed model has been sanity checked by agriculture experts.