Compensatory versus noncompensatory models for predicting consumer preferences
Standard preference models in consumer research assume that people weigh and add all attributes of the available options to derive a decision, while there is growing evidence for the use of simplifying heuristics. Recently, a greedoid algorithm has been developed (Yee, Dahan, Hauser \& Orlin, 2007; Kohli \& Jedidi, 2007) to model lexicographic heuristics from preference data. We compare predictive accuracies of the greedoid approach and standard conjoint analysis in an online study with a rating and a ranking task. The lexicographic model derived from the greedoid algorithm was better at predicting ranking compared to rating data, but overall, it achieved lower predictive accuracy for hold-out data than the compensatory model estimated by conjoint analysis. However, a considerable minority of participants was better predicted by lexicographic strategies. We conclude that the new algorithm will not replace standard tools for analyzing preferences, but can boost the study of situational and individual differences in preferential choice processes.
Volume (Year): 4 (2009)
Issue (Month): 3 (April)
|Contact details of provider:|| |
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Nils Reisen & Ulrich Hoffrage & Fred W. Mast, 2008. "Identifying decision strategies in a consumer choice situation," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 3(8), pages 641-658, December.
- Green, Paul E & Srinivasan, V, 1978. " Conjoint Analysis in Consumer Research: Issues and Outlook," Journal of Consumer Research, Oxford University Press, vol. 5(2), pages 103-123, Se.
- Gensch, Dennis H & Javalgi, Rajshekhar G, 1987. " The Influence of Involvement on Disaggregate Attribute Choice Models," Journal of Consumer Research, Oxford University Press, vol. 14(1), pages 71-82, June.
- 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.
- Peter E. Rossi & Greg M. Allenby, 2003. "Bayesian Statistics and Marketing," Marketing Science, INFORMS, vol. 22(3), pages 304-328, July.
- Laura Martignon & Ulrich Hoffrage, 2002. "Fast, frugal, and fit: Simple heuristics for paired comparison," Theory and Decision, Springer, vol. 52(1), pages 29-71, February.
- Rajeev Kohli & Kamel Jedidi, 2007. "Representation and Inference of Lexicographic Preference Models and Their Variants," Marketing Science, INFORMS, vol. 26(3), pages 380-399, 05-06.
- 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.
- Bettman, James R & Park, C Whan, 1980. " Effects of Prior Knowledge and Experience and Phase of the Choice Process on Consumer Decision Processes: A Protocol Analysis," Journal of Consumer Research, Oxford University Press, vol. 7(3), pages 234-248, December.
- Stanley F. Biggs & Jean C. Bedard & Brian G. Gaber & Thomas J. Linsmeier, 1985. "The Effects of Task Size and Similarity on the Decision Behavior of Bank Loan Officers," Management Science, INFORMS, vol. 31(8), pages 970-987, August.
When requesting a correction, please mention this item's handle: RePEc:jdm:journl:v:4:y:2009:i:3:p:200-213. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Jonathan Baron)
If references are entirely missing, you can add them using this form.