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Individualized Hybrid Models for Conjoint Analysis

Author

Listed:
  • Paul E. Green

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Abba M. Krieger

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

Abstract

Increasingly, conjoint analysts are being asked to design and analyze studies involving large numbers of attributes and/or attribute levels. Various types of approaches, including attribute bridging, Adaptive Conjoint Analysis, and hybrid models have been proposed to deal with the problem. This paper describes recent developments in hybrid modeling. Four hybrid models are described and compared in terms of their performance in an industry-based study entailing 15 product attributes. Comparisons are made in terms of internal cross-validation, market share estimates, attribute importances clustering, and its relationship to exogenous background variables. The proposed models are also compared to selected models from the transportation science literature. The authors emphasize the point that comparative model performance may strongly depend on the ways in which the models are to be used.

Suggested Citation

  • Paul E. Green & Abba M. Krieger, 1996. "Individualized Hybrid Models for Conjoint Analysis," Management Science, INFORMS, vol. 42(6), pages 850-867, June.
    • Handle: RePEc:inm:ormnsc:v:42:y:1996:i:6:p:850-867
    DOI: 10.1287/mnsc.42.6.850
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    Citations

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    Cited by:

    1. Winfried Steiner & Harald Hruschka, 2002. "A Probabilistic One-Step Approach to the Optimal Product Line Design Problem Using Conjoint and Cost Data," Review of Marketing Science Working Papers 1-4-1003, Berkeley Electronic Press.
    2. Kyle D. Chen & Warren H. Hausman, 2000. "Technical Note: Mathematical Properties of the Optimal Product Line Selection Problem Using Choice-Based Conjoint Analysis," Management Science, INFORMS, vol. 46(2), pages 327-332, February.
    3. Frank Bodendorf & Manuel Lutz & Jörg Franke, 2021. "Valuation and pricing of software licenses to support supplier–buyer negotiations: A case study in the automotive industry," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(7), pages 1686-1702, October.
    4. Michalek, Jeremy J. & Ebbes, Peter & Adigüzel, Feray & Feinberg, Fred M. & Papalambros, Panos Y., 2011. "Enhancing marketing with engineering: Optimal product line design for heterogeneous markets," International Journal of Research in Marketing, Elsevier, vol. 28(1), pages 1-12.
    5. Dong, Songting & Ding, Min & Huber, Joel, 2010. "A simple mechanism to incentive-align conjoint experiments," International Journal of Research in Marketing, Elsevier, vol. 27(1), pages 25-32.
    6. 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.
    7. Burc Ulengin & Fusun Ulengin & Umit Guvenc, 1998. "Urban quality of life in Istanbul: Priorities and segmentation," ERSA conference papers ersa98p297, European Regional Science Association.
    8. Slavomir Bednar & Jan Modrak, 2015. "Product Variety Management As A Tool For Successful Mass Customized Product Structure," Polish Journal of Management Studies, Czestochowa Technical University, Department of Management, vol. 12(1), pages 16-25, DEcember.
    9. B. P. S. Murthi & Sumit Sarkar, 2003. "The Role of the Management Sciences in Research on Personalization," Management Science, INFORMS, vol. 49(10), pages 1344-1362, October.
    10. Jessica Coria & Pablo Marshall, 2001. "Valoracion De Atributos De Profesionales Universitarios," Abante, Escuela de Administracion. Pontificia Universidad Católica de Chile., vol. 4(2), pages 193-213.
    11. B. P. S. Murthi & Sumit Sarkar, 2003. "The Role of the Mangement Sciences in Research on Personalization," Review of Marketing Science Working Papers 2-2-1025, Berkeley Electronic Press.
    12. van der Rhee, Bo & Verma, Rohit & Plaschka, Gerhard, 2009. "Understanding trade-offs in the supplier selection process: The role of flexibility, delivery, and value-added services/support," International Journal of Production Economics, Elsevier, vol. 120(1), pages 30-41, July.
    13. Fahim Ullah & Mansoor K Khattak & Kang Min, 2018. "Experimental investigation of the comparison of compound parabolic concentrator and ordinary heat pipe-type solar concentrator," Energy & Environment, , vol. 29(5), pages 770-783, August.
    14. Ulengin, Burc & Ulengin, Fusun & Guvenc, Umit, 2001. "A multidimensional approach to urban quality of life: The case of Istanbul," European Journal of Operational Research, Elsevier, vol. 130(2), pages 361-374, April.
    15. Jong Seok Kim, 2017. "Empirical Analysis Of Consumer Willingness To Pay For Smart Phone Attributes In Multi-Countries," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 21(02), pages 1-37, February.
    16. S Tsafarakis & E Grigoroudis & N Matsatsinis, 2011. "Consumer choice behaviour and new product development: an integrated market simulation approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(7), pages 1253-1267, July.
    17. Ulengin, Fusun & Ilker Topcu, Y. & Sahin, Sule Onsel, 2001. "An integrated decision aid system for Bosphorus water-crossing problem," European Journal of Operational Research, Elsevier, vol. 134(1), pages 179-192, October.

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    Keywords

    conjoint analysis; computer simulation;

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