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Three-Way Multivariate Conjoint Analysis

Author

Listed:
  • Wayne S. DeSarbo

    (Bell Laboratories, Murray Hill, New Jersey 07974)

  • J. Douglas Carroll

    (Bell Laboratories, Murray Hill, New Jersey 07974)

  • Donald R. Lehmann

    (Graduate School of Business, Columbia University, New York, New York 10027)

  • John O'Shaughnessy

    (Graduate School of Business, Columbia University, New York, New York 10027)

Abstract

Three-Way Multivariate Conjoint Analysis is developed as an extension of traditional metric conjoint analysis allowing one to examine several dependent variables simultaneously, as well as individual differences in response. Four nested models are developed to examine the effects of the experimental design, the dependent variables, and individual differences. An illustration concerning the relationship of product characteristics to the importance of various decision-making criteria for industrial purchasing is provided. Finally, extensions of the model(s) to other marketing applications and nonmetric analyses are discussed.

Suggested Citation

  • Wayne S. DeSarbo & J. Douglas Carroll & Donald R. Lehmann & John O'Shaughnessy, 1982. "Three-Way Multivariate Conjoint Analysis," Marketing Science, INFORMS, vol. 1(4), pages 323-350.
    • Handle: RePEc:inm:ormksc:v:1:y:1982:i:4:p:323-350
    DOI: 10.1287/mksc.1.4.323
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    Citations

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    Cited by:

    1. Wayne DeSarbo & Vithala Rao, 1984. "GENFOLD2: A set of models and algorithms for the general UnFOLDing analysis of preference/dominance data," Journal of Classification, Springer;The Classification Society, vol. 1(1), pages 147-186, December.
    2. Pieter C. Schoonees & Patrick J. F. Groenen & Michel Velden, 2022. "Least-squares bilinear clustering of three-way data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(4), pages 1001-1037, December.
    3. Kamel Jedidi & Wayne DeSarbo, 1991. "A stochastic multidimensional scaling procedure for the spatial representation of three-mode, three-way pick any/J data," Psychometrika, Springer;The Psychometric Society, vol. 56(3), pages 471-494, September.
    4. Wayne DeSarbo & Kamel Jedidi & Joel Steckel, 1991. "A stochastic multidimensional scaling procedure for the empirical determination of convex indifference curves for preference/choice analysis," Psychometrika, Springer;The Psychometric Society, vol. 56(2), pages 279-307, June.
    5. Yoshio Takane & Tadashi Shibayama, 1991. "Principal component analysis with external information on both subjects and variables," Psychometrika, Springer;The Psychometric Society, vol. 56(1), pages 97-120, March.
    6. DeSarbo Wayne S., 2010. "A Spatial Multidimensional Unfolding Choice Model for Examining the Heterogeneous Expressions of Sports Fan Avidity," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(2), pages 1-24, April.
    7. Paul E. Green & Abba M. Krieger & Yoram Wind, 2001. "Thirty Years of Conjoint Analysis: Reflections and Prospects," Interfaces, INFORMS, vol. 31(3_supplem), pages 56-73, June.
    8. Wayne DeSarbo & Robert Madrigal, 2012. "Exploring the Demand Aspects of Sports Consumption and Fan Avidity," Interfaces, INFORMS, vol. 42(2), pages 199-212, April.
    9. Yoshio Takane & Haruo Yanai & Shinichi Mayekawa, 1991. "Relationships among several methods of linearly constrained correspondence analysis," Psychometrika, Springer;The Psychometric Society, vol. 56(4), pages 667-684, December.
    10. Wayne DeSarbo & Michael Johnson & Ajay Manrai & Lalita Manrai & Elizabeth Edwards, 1992. "Tscale: A new multidimensional scaling procedure based on tversky's contrast model," Psychometrika, Springer;The Psychometric Society, vol. 57(1), pages 43-69, March.
    11. Rick L. Andrews & Ajay K. Manrai, 1999. "MDS Maps for Product Attributes and Market Response: An Application to Scanner Panel Data," Marketing Science, INFORMS, vol. 18(4), pages 584-604.
    12. Wayne DeSarbo & Jaewun Cho, 1989. "A stochastic multidimensional scaling vector threshold model for the spatial representation of “pick any/n” data," Psychometrika, Springer;The Psychometric Society, vol. 54(1), pages 105-129, March.

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