Adaptive Self-Explication of Multi-Attribute Preferences

44 Pages Posted: 21 Dec 2007 Last revised: 7 Nov 2016

See all articles by Oded Netzer

Oded Netzer

Columbia Business School - Marketing

V. Seenu Srinivasan

Stanford University - Graduate School of Business

Date Written: November 1, 2007

Abstract

In this research we propose a web-based adaptive self-explicated approach for multi-attribute preference measurement (conjoint analysis) with a large number (ten or more) of attributes. In the empirical application reported here the proposed approach provides a substantial and significant improvement in predictive ability over current preference measurement methods designed for handling a large number of attributes. Our approach also overcomes some of the limitations of previous self-explicated approaches. Two methods are commonly used to estimate attribute importances in self-explicated studies: ratings and constant-sum allocation. A common problem with the ratings approach is that it does not explicitly capture the tradeoff between attributes; it is easy for respondents to say that every attribute is important. The constant-sum approach overcomes this limitation, but with a large number of product attributes it becomes difficult for the respondent to divide a constant sum among all the attributes. We developed a computer-based self-explicated approach that breaks down the attribute importance question into a sequence of constant-sum paired comparison questions. We first used a fixed design in which the set of questions is chosen from a balanced orthogonal design and then extend it to an adaptive design in which the questions are chosen adaptively for each respondent to maximize the information elicited from each paired comparison question. Unlike the traditional self-explicated approach, the proposed approach provides (approximate) standard errors for attribute importance. In a study involving digital cameras described on twelve attributes, we find that the predictive validity (correctly predicted top choices) of the proposed adaptive approach is 35%-52% higher than that of Adaptive Conjoint Analysis, the Fast Polyhedral approach, and the traditional self-explicated approach, irrespective of whether the part-worths were estimated using classical or hierarchical Bayes estimation. Additionally, the proposed adaptive approach reduces the respondents' burden by keeping the number of paired comparison questions small without significant loss of predictive validity.

Keywords: conjoint analysis, market research, multiattribute models

Suggested Citation

Netzer, Oded and Srinivasan, V. Seenu, Adaptive Self-Explication of Multi-Attribute Preferences (November 1, 2007). Netzer, Oded and V. Srinivasan (2011), “Adaptive Self-Explication of Multi-Attribute Preferences,” Journal of Marketing Research, 48 February (1), 140-156. doi: http://dx.doi.org/10.1509/jmkr.48.1.140. Available at SSRN: https://ssrn.com/abstract=1077434 or http://dx.doi.org/10.2139/ssrn.1077434

Oded Netzer (Contact Author)

Columbia Business School - Marketing ( email )

New York, NY 10027
United States

V. Seenu Srinivasan

Stanford University - Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States
650-723-8505 (Phone)
650-725-6152 (Fax)

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