IDEAS home Printed from https://ideas.repec.org/a/kap/qmktec/v20y2022i4d10.1007_s11129-022-09256-3.html
   My bibliography  Save this article

Non-linear pricing effects in conjoint analysis

Author

Listed:
  • YiChun Miriam Liu

    (Towson University)

  • Jeff D. Brazell

    (University of Utah)

  • Greg M. Allenby

    (The™ Ohio State University)

Abstract

The application of conjoint analysis to new product development is challenged in studies of complex products that simultaneously examine the major drivers of a purchase decision and the composition of product components. Demands on data increase as more product features are included in an analysis, and at some point it becomes necessary to study the components separately. This paper presents evidence of a non-linear pricing effect that complicates the analysis of large conjoint studies when multiple conjoint exercises are integrated, or bridged into a single analysis. Our model is illustrated with data from the automotive industry showing that option packages are under-valued without accounting for the non-linear effects of price.

Suggested Citation

  • YiChun Miriam Liu & Jeff D. Brazell & Greg M. Allenby, 2022. "Non-linear pricing effects in conjoint analysis," Quantitative Marketing and Economics (QME), Springer, vol. 20(4), pages 397-430, December.
    • Handle: RePEc:kap:qmktec:v:20:y:2022:i:4:d:10.1007_s11129-022-09256-3
    DOI: 10.1007/s11129-022-09256-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11129-022-09256-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11129-022-09256-3?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. 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.
    2. Tetyana Kosyakova & Thomas Otter & Sanjog Misra & Christian Neuerburg, 2020. "Exact MCMC for Choices from Menus—Measuring Substitution and Complementarity Among Menu Items," Marketing Science, INFORMS, vol. 39(2), pages 427-447, March.
    3. P. V. (Sundar) Balakrishnan & Varghese S. Jacob, 1996. "Genetic Algorithms for Product Design," Management Science, INFORMS, vol. 42(8), pages 1105-1117, August.
    4. 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.
    5. Richard H. Thaler, 2008. "Mental Accounting and Consumer Choice," Marketing Science, INFORMS, vol. 27(1), pages 15-25, 01-02.
    6. Greg M. Allenby & Jeff Brazell & John R. Howell & Peter E. Rossi, 2014. "Valuation of Patented Product Features," Journal of Law and Economics, University of Chicago Press, vol. 57(3), pages 629-663.
    7. Rajeev Kohli & Ramesh Krishnamurti, 1987. "A Heuristic Approach to Product Design," Management Science, INFORMS, vol. 33(12), pages 1523-1533, December.
    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. 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.
    10. Marshall P. & Bradlow E.T., 2002. "A Unified Approach to Conjoint Analysis Models," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 674-682, September.
    11. Denzil G. Fiebig & Michael P. Keane & Jordan Louviere & Nada Wasi, 2010. "The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity," Marketing Science, INFORMS, vol. 29(3), pages 393-421, 05-06.
    12. Mingyu Joo & Michael L. Thompson & Greg M. Allenby6, 2019. "Optimal Product Design by Sequential Experiments in High Dimensions," Management Science, INFORMS, vol. 65(7), pages 3235-3254, July.
    13. Jeff Brazell & Christopher Diener & Ekaterina Karniouchina & William Moore & Válerie Séverin & Pierre-Francois Uldry, 2006. "The no-choice option and dual response choice designs," Marketing Letters, Springer, vol. 17(4), pages 255-268, December.
    14. 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.
    15. Peter J. Lenk & Wayne S. DeSarbo & Paul E. Green & Martin R. Young, 1996. "Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs," Marketing Science, INFORMS, vol. 15(2), pages 173-191.
    16. Jeffrey D. Camm & James J. Cochran & David J. Curry & Sriram Kannan, 2006. "Conjoint Optimization: An Exact Branch-and-Bound Algorithm for the Share-of-Choice Problem," Management Science, INFORMS, vol. 52(3), pages 435-447, March.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Jeffrey D. Camm & James J. Cochran & David J. Curry & Sriram Kannan, 2006. "Conjoint Optimization: An Exact Branch-and-Bound Algorithm for the Share-of-Choice Problem," Management Science, INFORMS, vol. 52(3), pages 435-447, March.
    3. Wang, Xinfang (Jocelyn) & Curry, David J., 2012. "A robust approach to the share-of-choice product design problem," Omega, Elsevier, vol. 40(6), pages 818-826.
    4. Carsten Herbes & Johannes Dahlin & Peter Kurz, 2020. "Consumer Willingness To Pay for Proenvironmental Attributes of Biogas Digestate-Based Potting Soil," Sustainability, MDPI, vol. 12(16), pages 1-19, August.
    5. 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.
    6. Lynd Bacon & Peter Lenk, 2012. "Augmenting discrete-choice data to identify common preference scales for inter-subject analyses," Quantitative Marketing and Economics (QME), Springer, vol. 10(4), pages 453-474, December.
    7. Max J. Pachali & Peter Kurz & Thomas Otter, 2020. "How to generalize from a hierarchical model?," Quantitative Marketing and Economics (QME), Springer, vol. 18(4), pages 343-380, December.
    8. Stich, Lucas & Spann, Martin & Schmidt, Klaus M., 2022. "Paying for open access," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 273-286.
    9. James Cochran & David Curry & Rajesh Radhakrishnan & Jon Pinnell, 2014. "Political engineering: optimizing a U.S. Presidential candidate’s platform," Annals of Operations Research, Springer, vol. 215(1), pages 63-87, April.
    10. 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.
    11. Max J. Pachali & Peter Kurz & Thomas Otter, 0. "How to generalize from a hierarchical model?," Quantitative Marketing and Economics (QME), Springer, vol. 0, pages 1-38.
    12. 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.
    13. Alexandre Belloni & Robert Freund & Matthew Selove & Duncan Simester, 2008. "Optimizing Product Line Designs: Efficient Methods and Comparisons," Management Science, INFORMS, vol. 54(9), pages 1544-1552, September.
    14. Anjulie Hähnchen & Bernhard Baumgartner, 2020. "The Impact of Price Bundling on the Evaluation of Bundled Products: Does It Matter How You Frame It?," Schmalenbach Business Review, Springer;Schmalenbach-Gesellschaft, vol. 72(1), pages 39-63, February.
    15. 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.
    16. Pengyuan Wang & Eric Bradlow & Edward George, 2014. "Meta-analyses using information reweighting: An application to online advertising," Quantitative Marketing and Economics (QME), Springer, vol. 12(2), pages 209-233, June.
    17. 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.
    18. Greg M. Allenby & Jeff Brazell & John R. Howell & Peter E. Rossi, 2014. "Valuation of Patented Product Features," Journal of Law and Economics, University of Chicago Press, vol. 57(3), pages 629-663.
    19. Lehmann, Nico & Sloot, Daniel & Schüle, Christopher & Ardone, Armin & Fichtner, Wolf, 2023. "The motivational drivers behind consumer preferences for regional electricity – Results of a choice experiment in Southern Germany," Energy Economics, Elsevier, vol. 120(C).
    20. Daria Dzyabura & Srikanth Jagabathula, 2018. "Offline Assortment Optimization in the Presence of an Online Channel," Management Science, INFORMS, vol. 64(6), pages 2767-2786, June.

    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:kap:qmktec:v:20:y:2022:i:4:d:10.1007_s11129-022-09256-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.