IDEAS home Printed from https://ideas.repec.org/a/eee/ijrema/v26y2009i2p108-118.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167811609000214
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijresmar.2009.01.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    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. 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.
    4. Min Ding & Rajdeep Grewal & John Liechty, 2005. "Incentive-aligned conjoint analysis," Framed Field Experiments 00139, The Field Experiments Website.
    5. 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.
    6. 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.
    7. Kamel Jedidi & Z. John Zhang, 2002. "Augmenting Conjoint Analysis to Estimate Consumer Reservation Price," Management Science, INFORMS, vol. 48(10), pages 1350-1368, October.
    8. 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.
    9. 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.
    10. Louviere,Jordan J. & Hensher,David A. & Swait,Joffre D. With contributions by-Name:Adamowicz,Wiktor, 2000. "Stated Choice Methods," Cambridge Books, Cambridge University Press, number 9780521788304.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Andrew R. Kamwendo & Mandusha Maharaj, 2022. "The Preferences of Consumers When Selecting Clothing Detergent Products," International Review of Management and Marketing, Econjournals, vol. 12(6), pages 23-36, November.
    2. Tammo Bijmolt & Michel Velden, 2012. "Multiattribute perceptual mapping with idiosyncratic brand and attribute sets," Marketing Letters, Springer, vol. 23(3), pages 585-601, September.
    3. Jonas Schmidt & Tammo H. A. Bijmolt, 2020. "Accurately measuring willingness to pay for consumer goods: a meta-analysis of the hypothetical bias," Journal of the Academy of Marketing Science, Springer, vol. 48(3), pages 499-518, May.
    4. Gensler, Sonja & Hinz, Oliver & Skiera, Bernd & Theysohn, Sven, 2012. "Willingness-to-pay estimation with choice-based conjoint analysis: Addressing extreme response behavior with individually adapted designs," European Journal of Operational Research, Elsevier, vol. 219(2), pages 368-378.
    5. Schoenwitz, Manuel & Potter, Andrew & Gosling, Jonathan & Naim, Mohamed, 2017. "Product, process and customer preference alignment in prefabricated house building," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 79-90.
    6. Czakon, Wojciech & Niemand, Thomas & Gast, Johanna & Kraus, Sascha & Frühstück, Lisa, 2020. "Designing coopetition for radical innovation: An experimental study of managers' preferences for developing self-driving electric cars," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    7. Schlereth, Christian & Skiera, Bernd, 2012. "Measurement of consumer preferences for bucket pricing plans with different service attributes," International Journal of Research in Marketing, Elsevier, vol. 29(2), pages 167-180.
    8. Christian Schlereth & Christine Eckert & Bernd Skiera, 2012. "Using discrete choice experiments to estimate willingness-to-pay intervals," Marketing Letters, Springer, vol. 23(3), pages 761-776, September.
    9. Schlereth, Christian & Eckert, Christine & Schaaf, René & Skiera, Bernd, 2014. "Measurement of preferences with self-explicated approaches: A classification and merge of trade-off- and non-trade-off-based evaluation types," European Journal of Operational Research, Elsevier, vol. 238(1), pages 185-198.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. James Agarwal & Wayne DeSarbo & Naresh K. Malhotra & Vithala Rao, 2015. "An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 2(1), pages 19-40, March.
    2. Braun, Alexander & Schmeiser, Hato & Schreiber, Florian, 2016. "On consumer preferences and the willingness to pay for term life insurance," European Journal of Operational Research, Elsevier, vol. 253(3), pages 761-776.
    3. Gensler, Sonja & Hinz, Oliver & Skiera, Bernd & Theysohn, Sven, 2012. "Willingness-to-pay estimation with choice-based conjoint analysis: Addressing extreme response behavior with individually adapted designs," European Journal of Operational Research, Elsevier, vol. 219(2), pages 368-378.
    4. Oded Netzer & Olivier Toubia & Eric Bradlow & Ely Dahan & Theodoros Evgeniou & Fred Feinberg & Eleanor Feit & Sam Hui & Joseph Johnson & John Liechty & James Orlin & Vithala Rao, 2008. "Beyond conjoint analysis: Advances in preference measurement," Marketing Letters, Springer, vol. 19(3), pages 337-354, December.
    5. Dongling Huang & Lan Luo, 2016. "Consumer Preference Elicitation of Complex Products Using Fuzzy Support Vector Machine Active Learning," Marketing Science, INFORMS, vol. 35(3), pages 445-464, May.
    6. Hauser, John R., 2014. "Consideration-set heuristics," Journal of Business Research, Elsevier, vol. 67(8), pages 1688-1699.
    7. Olivier Toubia & John R. Hauser, 2007. "—On Managerially Efficient Experimental Designs," Marketing Science, INFORMS, vol. 26(6), pages 851-858, 11-12.
    8. John R. Hauser & Felix Eggers & Matthew Selove, 2019. "The Strategic Implications of Scale in Choice-Based Conjoint Analysis," Marketing Science, INFORMS, vol. 38(6), pages 1059-1081, November.
    9. Christian Schlereth & Bernd Skiera, 2009. "Schätzung von Zahlungsbereitschaftsintervallen mit der Choice-Based Conjoint-Analyse," Schmalenbach Journal of Business Research, Springer, vol. 61(8), pages 838-856, December.
    10. Helveston, John Paul & Feit, Elea McDonnell & Michalek, Jeremy J., 2018. "Pooling stated and revealed preference data in the presence of RP endogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 109(C), pages 70-89.
    11. 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.
    12. Theodoros Evgeniou & Massimiliano Pontil & Olivier Toubia, 2007. "A Convex Optimization Approach to Modeling Consumer Heterogeneity in Conjoint Estimation," Marketing Science, INFORMS, vol. 26(6), pages 805-818, 11-12.
    13. Mohammed H. Alemu & Søren B. Olsen, 2017. "Can a Repeated Opt-Out Reminder remove hypothetical bias in discrete choice experiments? An application to consumer valuation of novel food products," IFRO Working Paper 2017/05, University of Copenhagen, Department of Food and Resource Economics.
    14. Vishva Danthurebandara & Jie Yu & Martina Vandebroek, 2015. "Designing choice experiments by optimizing the complexity level to individual abilities," Quantitative Marketing and Economics (QME), Springer, vol. 13(1), pages 1-26, March.
    15. Jeffrey Meyer & Venkatesh Shankar & Leonard L. Berry, 2018. "Pricing hybrid bundles by understanding the drivers of willingness to pay," Journal of the Academy of Marketing Science, Springer, vol. 46(3), pages 497-515, May.
    16. Greg Allenby & Jeff Brazell & John Howell & Peter Rossi, 2014. "Economic valuation of product features," Quantitative Marketing and Economics (QME), Springer, vol. 12(4), pages 421-456, December.
    17. 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.
    18. Denis Sauré & Juan Pablo Vielma, 2019. "Ellipsoidal Methods for Adaptive Choice-Based Conjoint Analysis," Operations Research, INFORMS, vol. 67(2), pages 315-338, March.
    19. Jonas Schmidt & Tammo H. A. Bijmolt, 2020. "Accurately measuring willingness to pay for consumer goods: a meta-analysis of the hypothetical bias," Journal of the Academy of Marketing Science, Springer, vol. 48(3), pages 499-518, May.
    20. Srinivasan, V. Seenu & Netzer, Oded, 2007. "Adaptive Self-Explication of Multi-attribute Preferences," Research Papers 1979, Stanford University, Graduate School of Business.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ijrema:v:26:y:2009:i:2:p:108-118. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/international-journal-of-research-in-marketing/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.