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Deriving Consensus Rankings Via Multicriteria Decision Making Methodology

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Purpose – This paper takes a cautionary stance to the impact of marketing mix on customer satisfaction, via a case study deriving consensus rankings for benchmarking on selected retail stores in Malaysia. Design/methodology/approach – ELECTRE I model is used in deriving consensus rankings via multicriteria decision making method for benchmarking base on the marketing mix model 4Ps. Descriptive analysis is used to analyze the best practice among the four marketing tactics. Findings – Outranking methods in consequence constitute a strong base on which to found the entire structure of the behavioral theory of benchmarking applied to development of marketing strategy. Research limitations/implications – This study has looked only at a limited part of the puzzle of how consumer satisfaction translates into behavioral outcomes. Practical implications – The study provides managers with guidance on how to generate rough outline of potential marketing activities that can be used to take advantage of capabilities and convert weaknesses and threats. Originality/value – This paper interestingly portrays the effective usage of multicriteria decision making and ranking method to help marketing manager predict their marketing trend. Keywords: Marketing mix, Customer satisfaction, Retailing, Benchmarking, Multicriteria decision-making, ELECTRE I method Type of paper: Research Paper

With increasing globalization, local retailers find themselves having to compete with large foreign players by targeting niche markets. To excel and flaunt as a market leader in an ultramodern era and a globalize world, the organizations must strive to harvest from its marketing strategies, benchmarking and company quality policy. Ranking and selecting projects is a relatively common, yet often difficult task. It is complicated because there is usually more than one dimension for measuring the impact of each criteria and more than one decision maker. This paper considers a real application of project selection for the marketing mix element, using an approach called ELECTRE. The ELECTRE method has several unique features not found in other solution methods; these are the concepts of outranking and indifference and preference thresholds. The ELECTRE method applied to the project selection problem using SPSS (Statistical Package for the Social Sciences) application. Our contribution is to show the potential of Marketing mix model in deriving a consensus ranking for benchmarking. According to the feedback from the respondents, we dynamically rank out the best element to be benchmark.

The decision problem faced by management has been translated into our market research problem in the form of questions that define the information that is required to make the decision and how this information obtained. The corresponding research problem is to assess whether the market would accept the consensus rankings derive from benchmarking result from the impact of marketing mix on customer satisfaction using a multi-criteria decision making outranking methodology.

The project ranking problem is, like many decision problems, challenging for at least two reasons. First, there is no single criterion in marketing mix model which adequately captures the effect or impact of each element; in other words, it is a multiple criteria problem. Second, there is no single decision maker; instead the project ranking requires a consensus from a group of decision makers. (Henig and Buchanan and Buchanan et al.) Buchanan et al. have debated that effective decisions come from effective decision process and proposed that where potential the subjective and objective parts of the decision process should be branched. The relationship between the alternatives and the criteria is portrayed using attributes, which are the objective and measurable character of alternatives. Attributes form the bridge within the alternatives and the criteria. Often, marketing management is looking and interesting on the solution rather than the outlines criteria.

Referring to the statement of Simon (1977), analysis decisions ex post cannot accurately be done due to human memory has some known biases. Through observation, we noticed that in many cases, decision is treated as a one shot game whereas most decisions are more or less repetitious. A decision maker can learn the effect of the assignment he has distributed to the weights. Likewise, the decision maker can learn to modify concordance and discordance factors in outranking methods (Roy and Skalka, 1985; Vetschera, 1986). In the theoretical account of decision making, we remember that, the subjective and contextual data play an important role due to the prominent look-ahead component (Pomerol, 1995). Moreover, due to the rawness of the framework, particularly during the evaluation stages (Lévine and Pomerol, 1995), explanations and contextual knowledge are among the elements facilitating the cooperation, and the need to make them explicit and shared both by the system and the user (Brezillon and Abu-Hakima, 1995) and Brézillon (1996).

3.1 Recognizance Survey
This section takes into consideration sites in Selangor area, geographical position in the center of Peninsular Malaysia, contributed to the state’s rapid development as Malaysia’s transportation and industrial hub, with a population of 4,736,100 (2005 estimate). The selected data collection sites are Tesco Saujana Impian Kajang, Carrefour Alamanda Putrajaya, Giant Bukit Tinggi and Mydin Kajang. 3.2 Research Instrument

A non-comparative Likert scaling technique was used in this survey. The questionnaire is divided into 4 sections: customer information, marketing mix model, customer perception and motivating factor. The demography variables measured at a nominal level in Section 1 include gender, ethnic, marital status, age and how often do the respondents shop at the specific retail store. A typical test item in a Likert scale is a statement. The respondent is asked to indicate his or her degree of agreement with the statement or any kind of subjective or objective evaluation of the statement. In Section 2, a six-point scale is used in a forced choice method where the middle option of “Neither agree nor disagree” is not available. The questions comprise four attributes such as product, price, promotions, place/distribution; six questions are allocated for each of the 4Ps. Section 3 evaluates customer‟s perception using the same scale as practice in Section 2 whereas Section 4, the last part of the questionnaire measure the factor that motivates respondents the most to patronize the specific retail store using the nominal measurement. Simple random sampling technique is used in the research.

3.3 Illustration of Research Framework
FIGURE 1. Attribute – 4P‟s – Retail Stores Mapping
The illustration of Attribute – 4P‟s – Retail Stores Mapping in Figure 1 was built to sprout a better understanding on our study framework. Figure 1 elucidates the main idea of how we determine the targeted attribute of the 4Ps and generate it in the questionnaire to meet out objectives. The relationship between the marketing mix, 4ps with the criteria lies in each P element were clearly linking to the four selected retail stores.

When all are agreed on the category of criteria, to examine each alternative concordance to the attribute, we presuming that the options are known, it remains to complete the decision matrix. The assessment is generally independent of the aggregation procedure; it was due to the fact that examination theoretical counts on the posterior aggregation operation are generally ignored by the designers. The location of the respective alternatives or transforming a pair wise comparison into a numerical (normalized) scale as, for example, in the so-called “Analytical Hierarchical Process”(AHP) (Saaty, 1980). The utilities of a prearranged option, in the structure of multi-attribute utility, regarding each attribute, are jointly cardinal. They have therefore to be jointly evaluated (Pomerol & Barba-Romero, 1993). The support of a Multicriteria Decision Making methodology should be very useful in the case considering the difficulty either to validate the probabilistic independence or to aid the decision maker to jointly measure the options by solvability or by the mid-preference point method. 3.4 DATA COLLECTION

The data were collected by means of questionnaire. First appointment was conducted with the personal in-charge in each retail store to request cooperation and approval for data collection and survey respond via formal letters from the Department of Mathematical Sciences, Faculty of Science and technology, National University of Malaysia. Field research was conducted in Tesco Saujana Impian Kajang, Carrefour Alamanda Putrajaya, Giant Bukit Tinggi and Mydin Mart Kajang. A simple random sample of 214 household‟s respondents was obtained from each of the four retail stores; sum up a total of 856 respondents data. 3.5 Data Analysis and Interpretation

The retail market place promotes continuous improvement to survive in a turbulent atmosphere. For that, benchmarking is the exploration for industry best practices that leads to superior performance (Camp, 7 1989). The benchmarking dimension of the retail stores conceives a set of indicators and for this reason assumes the configuration of a multi-criteria analysis. The literature on retail stores and marketing mix model has identified four major underlying criteria essential to take place in the market place. They are as follows:

1 ATT : Product Attribute
2 ATT : Price Attribute
3 ATT : Promotions Attribute
4 ATT : Place/Distribution Attribute
An organization will show better performance on the basis of some indicators and worse performance on the basis of some others: “there is no single performance management enterprise system which is best in class across all areas” (Sharif, 2002).

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