A causal and effect decision making model of service quality expectation using grey-fuzzy DEMATEL approach

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Abstract

This research uses a solution based on a combined grey-fuzzy DEMATEL method to deal with the objective of the study. This study is aimed to present a perception approach to deal with real estate agent service quality expectation ranking with uncertainty. The ranking of best top five real estate agents might be a key strategic direction of other real estate agents prior to service quality expectation. The solving procedure is as follows: (i) the weights of criteria and alternatives are described in triangular fuzzy numbers; (ii) a grey possibility degree is used to result the ranking order for all alternatives; (iii) DEMATEL is used to resolve interdependency relationships among the criteria and (iv) an empirical example of real estate agent service quality ranking problem in customer expectation is used to resolve with this proposed method approach indicating that real estate agent R1 (CY real estate agent) is the best selection in terms of service quality in customer expectation.

Introduction

Understanding, building and maintaining service quality are the main concerns of business today. Delivery better service quality is important for service firms. Service quality always faces three major challenges: improving service quality, increasing assessment and reliability while competition ever increases, and trying to acquire and retain customers. Service quality has significantly impacted the operation of real estate agents, because service quality conditions influence a firm’s competitive advantage by retaining customer patronage and with this comes market share, and ultimately profitability (Morash & Ozment, 1994). It is now a leading firm strategy to develop a service quality model from customer expectation.

Service quality expectation can be interpreted as predictions of service, as an ideal standard, or as an attribute to importance. Smith (1995) contends that a number of service quality researchers have directly substituted important measures for expectation. Service quality affects all service activities and accelerates real estate agent sector growth. It has also fostered the collaboration of service quality by increasing the the number of professionals and by increasing transaction safety and other relevant activities. Real estate agents are usually an alternative for many customers to judge the best service quality. The agents can also acquire competences through service quality. Although service quality has been developing for several years, evaluating service quality expectation is critical as to whether the agents are aware of the importance of service quality. A considerable number of studies have been conducted emphasizing the factors to be considered for the evaluation (Liou and Tzeng, 2007, Tsaur et al., 2002, Tzeng et al., 2007). The criteria used for evaluating service quality effectiveness are numerous, and they influence one another (Parasuraman et al., 1985, Parasuraman et al., 1988, Parasuraman et al., 1991). Therefore, this research arises the imperative issue of how to enhance the real estate agent competitiveness in terms of a set of criteria with interdependency relationships from customer expectation. Unfortunately, very few works discuss this issue.

The evaluation models are deficient to solve this issue and they do not have an evaluation guideline. Since service quality can be evaluated according to the different criteria, the multi-criteria decision making (MCDM) approach is suitable for evaluating service quality expectation. Based on the various points of view or the suitable measuring method, the criteria can be categorized into distinct aspects. In real service quality assessment, the evaluation criteria in real situation are too large to determine the dependent or independent relation with others. The advantage of the grey-fuzzy DEMATEL approach is the evaluation of ranking problem in real world systems that are very often uncertain or lack information. Grey theory is a multi-disciplinary theory dealing with multi-criteria systems which lack information. It is one of the methods that is used to study uncertainty, and is superior in theoretical analysis of systems with imprecise information and incomplete samples. Service quality is measured to assess service performance, diagnose service problems, and manage service delivery (Parasuraman, 1995). The advantage of grey-fuzzy combination is that grey theory considers the condition of the fuzziness and deal flexibly with fuzziness situation (Bellman and Zadeh, 1970, Deng, 1982). However, the studied criteria are consider intertwined relations and synthetic utility. Criteria interrelations and components of independent factors are usually inter-affected. Applying the DEMATEL (Decision Making Trial and Evaluation Laboratory) method (Fontela and Gabus, 1976, Gabus and Fontela, 1974, Warfield, 1976) illustrates the interrelations among criteria, finds the central criteria to represent the effectiveness of attributes, and avoids the “overfitting” for evaluation. However, the degree of linking the two criteria together leads to DEMETAL that can be applied in exploring the degree of relationship among criteria. A robust MCDM method should consider the interactions among criteria. Hence, there is a need to combine the grey-fuzzy and DEMATEL for better decisions in uncertainty.

Therefore, the aim of this paper is to develop the grey-fuzzy DEMATEL method to rank the best real estate agent prior to service quality expectation criteria. This study presents a grey possibility degree to rank the service quality expectation in uncertainty. The research procedure is briefly as follows: (i) the weight and rating of criteria for five alternatives are described by linguistic information; (ii) a degree of grey possibility is proposed to determine the ranking order of all alternatives; in the alternative ranking process, the degree of uncertainty of criteria has to be taken into account and (iii) the experts judgment on the interrelationships using DEMATEL is integrated in the grey-fuzzy computation process. This approach is more suitable for MCDM system’s more uncertain environment than other approaches. This paper is organized as follows: The review of literatures is discussesed Section in 2, The research method that includes instruments’ validity and reliability, grey theory, fuzzy set theory and combined proposed method is described in Section 3, the proposed approach that is applied to real estate agent ranking problem in uncertainty and the cause and effect diagram of evaluated criteria are discussed in Section 4. Finally, conclusions are drawn in Section 5.

Section snippets

Literature review

This section aims to identify the theoretical composition that is considered as the objective of this study. To successfully manage the challenges of globalization and intensive competition, firms need to notify the service quality expectation. For assessing a competent service firm, there are several service quality criteria to be approached. Parasuraman et al. formulated a service quality model and identified 10 original determinants using in-depth interviews of executives and focus group

Research method

In this section, some essential definitions of the grey theory, fuzzy set theory, DEMATEL and proposed combined grey-fuzzy DEMATEL are briefly described in the following sections.

Empirical study analysis

A self-structured questionnaire is composed. As the first step, six experienced researchers are asked to critique the questionnaire for ambiguity, clarity and appropriateness of the items used to operationalize each attributes. Based on the feedback received from these experienced researchers, the instrument is modified to enhance clarity and appropriateness of the measures purporting.

Conclusions

This empirical study plans to enhance the service quality performance through the 21 service quality expectation. According to the evaluation results, in this study several implications are derived about business management as follows:

Firstly, valuable cues can be obtained for making profound decisions from the causal diagram (Fig. 3). For example, if the agents wanted to obtain high performances in terms of the effect group factors, it would be necessary to control and pay attention to the

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