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Hybrid individualized two-level choice-based conjoint (HIT-CBC): A new method for measuring preference structures with many attribute levels

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  • Eggers, Felix
  • Sattler, Henrik

Abstract

The authors introduce hybrid individualized two-level choice-based conjoint (HIT-CBC), which combines self-explicated preference measurement (SE) with choice-based conjoint analysis (CBC). The CBC part is adapted individually to a choice design that uses only the best and worst levels of each attribute identified in the SE phase. Prior knowledge about the best and worst levels allows HIT-CBC to generate an adaptive efficient (i.e., Pareto-optimal, balanced, orthogonal, minimally overlapping) choice design that is easy to implement. Whereas existing conjoint measurement approaches suffer from the number-of-levels effect, HIT-CBC avoids this problem because it reduces every attribute to two levels. Thus, HIT-CBC is appropriate for problems with many and imbalanced attribute levels. Furthermore, the transformation to the best and worst levels exemplifies a new and favorable way to account for consumer heterogeneity. In addition, HIT-CBC introduces the possibility of using individualized willingness-to-pay measures as price levels, which results in more flexibility for modeling demand functions (e.g., identifying price thresholds). A simulation study and an empirical study show the robust predictive validity of HIT-CBC compared with a standard CBC approach, and illustrate the advantages of HIT-CBC with a pricing study.

Suggested Citation

  • Eggers, Felix & Sattler, Henrik, 2009. "Hybrid individualized two-level choice-based conjoint (HIT-CBC): A new method for measuring preference structures with many attribute levels," International Journal of Research in Marketing, Elsevier, vol. 26(2), pages 108-118.
    • Handle: RePEc:eee:ijrema:v:26:y:2009:i:2:p:108-118
    DOI: 10.1016/j.ijresmar.2009.01.002
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    1. Currim, Imran S & Weinberg, Charles B & Wittink, Dick R, 1981. "Design of Subscription Programs for a Performing Arts Series," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 8(1), pages 67-75, June.
    2. John C. Liechty & Duncan K. H. Fong & Wayne S. DeSarbo, 2005. "Dynamic Models Incorporating Individual Heterogeneity: Utility Evolution in Conjoint Analysis," Marketing Science, INFORMS, vol. 24(2), pages 285-293, November.
    3. Arora, Neeraj & Huber, Joel, 2001. "Improving Parameter Estimates and Model Prediction by Aggregate Customization in Choice Experiments," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 28(2), pages 273-283, September.
    4. 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.
    5. Louviere,Jordan J. & Hensher,David A. & Swait,Joffre D., 2000. "Stated Choice Methods," Cambridge Books, Cambridge University Press, number 9780521788304, January.
    6. Elie Ofek & V. Srinivasan, 2002. "How Much Does the Market Value an Improvement in a Product Attribute?," Marketing Science, INFORMS, vol. 21(4), pages 398-411, June.
    7. Min Ding & Rajdeep Grewal & John Liechty, 2005. "Incentive-aligned conjoint analysis," Framed Field Experiments 00139, The Field Experiments Website.
    8. Garrett Sonnier & Andrew Ainslie & Thomas Otter, 2007. "Heterogeneity distributions of willingness-to-pay in choice models," Quantitative Marketing and Economics (QME), Springer, vol. 5(3), pages 313-331, September.
    9. Wittink, Dick R & Krishnamurthi, Lakshman & Nutter, Julia B, 1982. "Comparing Derived Importance Weights Across Attributes," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 8(4), pages 471-474, March.
    10. Kamel Jedidi & Z. John Zhang, 2002. "Augmenting Conjoint Analysis to Estimate Consumer Reservation Price," Management Science, INFORMS, vol. 48(10), pages 1350-1368, October.
    11. Olivier Toubia & John Hauser & Rosanna Garcia, 2007. "Probabilistic Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis: Theory and Application," Marketing Science, INFORMS, vol. 26(5), pages 596-610, 09-10.
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