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Adaptive Self-Explication of Multi-attribute Preferences

Author

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  • Srinivasan, V. Seenu

    (Stanford U)

  • Netzer, Oded

    (Columbia U)

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.

Suggested Citation

  • Srinivasan, V. Seenu & Netzer, Oded, 2007. "Adaptive Self-Explication of Multi-attribute Preferences," Research Papers 1979, Stanford University, Graduate School of Business.
  • Handle: RePEc:ecl:stabus:1979
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    References listed on IDEAS

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    1. 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.
    2. Olivier Toubia & Duncan I. Simester & John R. Hauser & Ely Dahan, 2003. "Fast Polyhedral Adaptive Conjoint Estimation," Marketing Science, INFORMS, vol. 22(3), pages 273-303.
    3. Jerry Wind & Paul E. Green & Douglas Shifflet & Marsha Scarbrough, 1989. "Courtyard by Marriott : Designing a Hotel Facility with Consumer-Based Marketing Models," Interfaces, INFORMS, vol. 19(1), pages 25-47, February.
    4. Green, Paul E & Srinivasan, V, 1978. " Conjoint Analysis in Consumer Research: Issues and Outlook," Journal of Consumer Research, Oxford University Press, vol. 5(2), pages 103-123, Se.
    5. repec:mes:challe:v:30:y:1987:i:4:p:55-56 is not listed on IDEAS
    6. 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.
    7. 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.
    8. 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, February.
    9. Qing Liu & Thomas Otter & Greg M. Allenby, 2007. "Investigating Endogeneity Bias in Marketing," Marketing Science, INFORMS, vol. 26(5), pages 642-650, 09-10.
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    Cited by:

    1. Jong Seok Kim & So Young Sohn, 2015. "An integrated hierarchical survey for a large-scale conjoint study for mobile phones," Journal of Global Scholars of Marketing Science, Taylor & Francis Journals, vol. 25(4), pages 314-337, September.
    2. Daniel R. Cavagnaro & Richard Gonzalez & Jay I. Myung & Mark A. Pitt, 2013. "Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach," Management Science, INFORMS, vol. 59(2), pages 358-375, February.
    3. repec:wsi:ijimxx:v:21:y:2017:i:02:n:s1363919617500165 is not listed on IDEAS

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