A non-comparative Likert scaling technique was used in this survey

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 … Continue reading “A non-comparative Likert scaling technique was used in this survey”

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.

In the theoretical account of decision making

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).

The project ranking problem is

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).

The ELECTRE method has several unique features not found in other

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.

With increasing globalization

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.

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