IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v25y2006i5p494-509.html
   My bibliography  Save this article

Estimating Heterogeneous EBA and Economic Screening Rule Choice Models

Author

Listed:
  • Timothy J. Gilbride

    (Mendoza College of Business, University of Notre Dame, Notre Dame, Indiana 46556)

  • Greg M. Allenby

    (Fisher College of Business, Ohio State University, Columbus, Ohio 43210)

Abstract

Consumer choice in surveys and in the marketplace reflects a complex process of screening and evaluating choice alternatives. Behavioral and economic models of choice processes are difficult to estimate when using stated and revealed preferences because the underlying process is latent. This paper introduces Bayesian methods for estimating two behavioral models that eliminate alternatives using specific attribute levels. The elimination by aspects theory postulates a sequential elimination of alternatives by attribute levels until a single one, the chosen alternative, remains. In the economic screening rule model, respondents screen out alternatives with certain attribute levels and then choose from the remaining alternatives, using a compensatory function of all the attributes. The economic screening rule model gives an economic justification as to why certain attributes are used to screen alternatives. A commercial conjoint study is used to illustrate the methods and assess their performance. In this data set, the economic screening rule model outperforms the EBA and other standard choice models and provides comparable results to an equivalent conjunctive screening rule model.

Suggested Citation

  • Timothy J. Gilbride & Greg M. Allenby, 2006. "Estimating Heterogeneous EBA and Economic Screening Rule Choice Models," Marketing Science, INFORMS, vol. 25(5), pages 494-509, September.
    • Handle: RePEc:inm:ormksc:v:25:y:2006:i:5:p:494-509
    DOI: 10.1287/mksc.1060.0211
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.1060.0211
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mksc.1060.0211?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
    ---><---

    References listed on IDEAS

    as
    1. Herbert A. Simon, 1955. "A Behavioral Model of Rational Choice," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 69(1), pages 99-118.
    2. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
    3. Timothy J. Gilbride & Greg M. Allenby, 2004. "A Choice Model with Conjunctive, Disjunctive, and Compensatory Screening Rules," Marketing Science, INFORMS, vol. 23(3), pages 391-406, October.
    4. Nitin Mehta & Surendra Rajiv & Kannan Srinivasan, 2003. "Price Uncertainty and Consumer Search: A Structural Model of Consideration Set Formation," Marketing Science, INFORMS, vol. 22(1), pages 58-84, June.
    5. John T. Gourville & Dilip Soman, 2005. "Overchoice and Assortment Type: When and Why Variety Backfires," Marketing Science, INFORMS, vol. 24(3), pages 382-395, July.
    6. John Rotondo, 1986. "Technical Note—Price as an Aspect of Choice in EBA," Marketing Science, INFORMS, vol. 5(4), pages 391-402.
    7. Hauser, John R & Wernerfelt, Birger, 1990. "An Evaluation Cost Model of Consideration Sets," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 16(4), pages 393-408, March.
    8. Peter E. Rossi & Greg M. Allenby, 2003. "Bayesian Statistics and Marketing," Marketing Science, INFORMS, vol. 22(3), pages 304-328, July.
    9. Bettman, James R & Luce, Mary Frances & Payne, John W, 1998. "Constructive Consumer Choice Processes," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 25(3), pages 187-217, December.
    10. Hess, Stephane & Train, Kenneth E. & Polak, John W., 2006. "On the use of a Modified Latin Hypercube Sampling (MLHS) method in the estimation of a Mixed Logit Model for vehicle choice," Transportation Research Part B: Methodological, Elsevier, vol. 40(2), pages 147-163, February.
    11. Shugan, Steven M, 1980. "The Cost of Thinking," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 7(2), pages 99-111, Se.
    12. Dapeng Cui & David Curry, 2005. "Prediction in Marketing Using the Support Vector Machine," Marketing Science, INFORMS, vol. 24(4), pages 595-615, January.
    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. Peter Stüttgen & Peter Boatwright & Robert T. Monroe, 2012. "A Satisficing Choice Model," Marketing Science, INFORMS, vol. 31(6), pages 878-899, November.
    2. Hauser, John R., 2014. "Consideration-set heuristics," Journal of Business Research, Elsevier, vol. 67(8), pages 1688-1699.
    3. Anocha Aribarg & Thomas Otter & Daniel Zantedeschi & Greg M. Allenby & Taylor Bentley & David J. Curry & Marc Dotson & Ty Henderson & Elisabeth Honka & Rajeev Kohli & Kamel Jedidi & Stephan Seiler & X, 2018. "Advancing Non-compensatory Choice Models in Marketing," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 82-92, March.
    4. Michael Yee & Ely Dahan & John R. Hauser & James Orlin, 2007. "Greedoid-Based Noncompensatory Inference," Marketing Science, INFORMS, vol. 26(4), pages 532-549, 07-08.
    5. Song Lin & Juanjuan Zhang & John R. Hauser, 2015. "Learning from Experience, Simply," Marketing Science, INFORMS, vol. 34(1), pages 1-19, January.
    6. Christina Schamp & Mark Heitmann & Robin Katzenstein, 2019. "Consideration of ethical attributes along the consumer decision-making journey," Journal of the Academy of Marketing Science, Springer, vol. 47(2), pages 328-348, March.
    7. J. Miguel Villas-Boas, 2009. "Product Variety and Endogenous Pricing with Evaluation Costs," Management Science, INFORMS, vol. 55(8), pages 1338-1346, August.
    8. Heiman, Amir & Lowengart, Oded, 2011. "The effects of information about health hazards in food on consumers' choice process," Journal of Econometrics, Elsevier, vol. 162(1), pages 140-147, May.
    9. Mark Heitmann & Andreas Herrmann & Christian Kaiser, 2007. "The effect of product variety on purchase probability," Review of Managerial Science, Springer, vol. 1(2), pages 111-131, August.
    10. Dmitri Kuksov & J. Miguel Villas-Boas, 2010. "When More Alternatives Lead to Less Choice," Marketing Science, INFORMS, vol. 29(3), pages 507-524, 05-06.
    11. Joffre Swait & Tülin Erdem, 2007. "Brand Effects on Choice and Choice Set Formation Under Uncertainty," Marketing Science, INFORMS, vol. 26(5), pages 679-697, 09-10.
    12. Timothy J. Gilbride & Greg M. Allenby, 2004. "A Choice Model with Conjunctive, Disjunctive, and Compensatory Screening Rules," Marketing Science, INFORMS, vol. 23(3), pages 391-406, October.
    13. Joseph Pancras, 2010. "A Framework to Determine the Value of Consumer Consideration Set Information for Firm Pricing Strategies," Computational Economics, Springer;Society for Computational Economics, vol. 35(3), pages 269-300, March.
    14. Bremer, Lucas & Heitmann, Mark & Schreiner, Thomas F., 2017. "When and how to infer heuristic consideration set rules of consumers," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 516-535.
    15. Hyowon Kim & Dong Soo Kim & Greg M. Allenby, 2020. "Benefit Formation and Enhancement," Quantitative Marketing and Economics (QME), Springer, vol. 18(4), pages 419-468, December.
    16. Nagler Matthew G., 2007. "Understanding the Internet's Relevance to Media Ownership Policy: A Model of Too Many Choices," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 7(1), pages 1-28, June.
    17. Rick L. Andrews & Andrew Ainslie & Imran S. Currim, 2008. "On the Recoverability of Choice Behaviors with Random Coefficients Choice Models in the Context of Limited Data and Unobserved Effects," Management Science, INFORMS, vol. 54(1), pages 83-99, January.
    18. Joonwook Park & Wayne DeSarbo & John Liechty, 2008. "A Hierarchical Bayesian Multidimensional Scaling Methodology for Accommodating Both Structural and Preference Heterogeneity," Psychometrika, Springer;The Psychometric Society, vol. 73(3), pages 451-472, September.
    19. Tonna Emenuga, 2023. "Filtering Down to Size: A Theory of Consideration," Papers 2301.05649, arXiv.org.
    20. Samuel D. Bond & Kurt A. Carlson & Ralph L. Keeney, 2008. "Generating Objectives: Can Decision Makers Articulate What They Want?," Management Science, INFORMS, vol. 54(1), pages 56-70, January.

    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:inm:ormksc:v:25:y:2006:i:5:p:494-509. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.