Adaptive Self-Explication of Multi-attribute Preferences
AbstractIn 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Stanford University, Graduate School of Business in its series Research Papers with number 1979.
Date of creation: Nov 2007
Date of revision:
Contact details of provider:
Postal: Stanford University, Stanford, CA 94305-5015
Phone: (650) 723-2146
Web page: http://gsbapps.stanford.edu/researchpapers/
More information through EDIRC
This paper has been announced in the following NEP Reports:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Elisabeth Deutskens & Ko de Ruyter & Martin Wetzels & Paul Oosterveld, 2004. "Response Rate and Response Quality of Internet-Based Surveys: An Experimental Study," Marketing Letters, Springer, vol. 15(1), pages 21-36, 02.
- Paul E. Green & Abba M. Krieger, 1995. "Attribute Importance Weights Modification in Assessing a Brand's Competitive Potential," Marketing Science, INFORMS, vol. 14(3), pages 253-270.
- John R. Hauser & Olivier Toubia, 2005. "The Impact of Utility Balance and Endogeneity in Conjoint Analysis," Marketing Science, INFORMS, vol. 24(3), pages 498-507, August.
- Toubia, Olivier & Simester, Duncan & Hauser, John & Dahan, Ely, 2003.
"Fast Polyhedral Adaptive Conjoint Estimation,"
4279-02, Massachusetts Institute of Technology (MIT), Sloan School of Management.
- Green, Paul E & Srinivasan, V, 1978. " Conjoint Analysis in Consumer Research: Issues and Outlook," Journal of Consumer Research, University of Chicago Press, vol. 5(2), pages 103-23, Se.
- Toubia, Olivier & Hauser, John & Simester, Duncan, 2003. "Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis," Working papers 4285-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ().
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.