Consumer Choice in the Presence of Incomplete Information with MCDM Method under Crisp Data

Abstract

Missing data (also often called incomplete information) is a common problem in consumer choice. While some authors claim that a tendency to give more weight to attributes on which all considered options have values relative to attributes for which not all options have values. Therefore, choosing from sets with missing information can affect buyers taste and purchase decisions. Moreover, it can lead to poor decision making by marketers and policymakers. In the present paper, to resolve this limitation, a new hybrid Multi Criteria Decision Making (MCDM/particularly, TOPSIS method) and mathematical approach (for finding the missing data) is proposed. Tamaddon, Jahanshahloo, Lotfi, Mozaffaari, and Gholami (2009) developed the mathematical formulations for finding the missing data in the Data Envelopment Analysis (DEA) environment. In this paper, we focus our attention on the innovative combination of TOPSIS and proposed method initiated by Tamaddon et al. (2009) and its uses in the consumer choice problem in the presence of incomplete information. In the proposed method, input and output factors play the role of cost and benefit respectively. In addition, comparative analysis has been performed, and the proposed method seems to be more satisfactory than the traditional method (ignoring and discarding missing data) in solving decision problem. The paper concludes with limitations and further research directions.

Keywords: MCDM, TOPSIS, Consumer Choice, Incomplete Information

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