Because consumers are believed to use simplifying heuristics to screen alternatives before choosing one to purchase, the authors formulate and estimate an elimination by aspects model at the individual household level, using UPC scanner data collected from purchases of regular ground coffee. The proposed model fits and predicts the data as well as its compensatory approximation. The parameters of the proposed model are reasonably stable, unbiased, and different in interesting ways from their compensatory counterparts.
Get full access to this article
View all access options for this article.
References
1.
BatsellRichard R., and PolkingJohn C. (1985), “A New Class of Market Share Models,”Marketing Science, 4 (Summer), 177–98.
2.
BettmanJames R. (1970), “Information Processing Models of Consumer Behavior,”Journal of Marketing Research, 1 (August), 370–6.
3.
BettmanJames R. (1979), An Information Processing Theory of Consumer Choice.Reading, MA: Addison-Wesley Publishing Company.
4.
BettmanJames R., and JacobyJacob (1976), “Patterns of Processing in Consumer Information Acquisition,” in Advances in Consumer Research, Vol. 3, AndersonBeverlee B., ed. Ann Arbor, MI: Association for Consumer Research, 315–20.
5.
BettmanJames R., JohnsonEric J., and PayneJohn W. (1989), “Consumer Decision Making,” in Handbook of Consumer Theory and Research, KassarjianHarold, and RobertsonTom, eds. Englewood Cliffs, NJ: Prentice-Hall, Inc.
6.
ChapmanRandall G., and StaelinRichard (1982), “Exploiting Rank Ordered Choice Set Data Within the Stochastic Utility Model,”Journal of Marketing Research, 19 (August), 288–301.
7.
CurrimImran S., MeyerRobert J., and LeNhan T. (1988), “Disaggregate Tree-Structured Modeling of Consumer Choice,”Journal of Marketing Research, 25 (August), 153–65.
8.
DavisHarry L. (1976), “Decision Making Within the Household,”Journal of Consumer Research, 2 (March), 241–60.
9.
DawesRobin M., and CorriganBernard (1974), “Linear Models in Decision Making,”Psychological Bulletin, 81 (March), 95–106.
GuadagniPeter M., and LittleJohn D. C. (1983), “A Logit Model of Brand Choice Calibrated on Scanner Data,”Marketing Science, 2 (Summer), 203–38.
12.
HorowitzJoel L. (1983), “Statistical Comparisons of Non-Nested Probabilistic Discrete Choice Models,”Transportation Science, 17 (August), 319–50.
13.
JohnsonEric J., and MeyerRobert J. (1984), “Compensatory Choice Models of Noncompensatory Processes: The Effect of Varying Context,”Journal of Consumer Research, 11 (June), 528–41.
14.
JohnsonEric J., MeyerRobert J., and GhoseSanjoy (1989), “When Choice Models Fail: Compensatory Models in Negatively Correlated Environments,”Journal of Marketing Research, 24 (August), 255–70.
15.
KahnBarbara E., MorrisonDonald G., and WrightGordon P. (1986), “Aggregating Individual Purchases to the Household Level,”Marketing Science, 5 (Summer), 260–8.
16.
LuceR. Duncan (1959), Individual Choice Behavior: A Theoretical Analysis.New York: John Wiley & Sons, Inc.
17.
LussierDennis A., and OlshavskyRichard W. (1979), “Task Complexity and Contingent Processing in Brand Choice,”Journal of Consumer Research, 6 (September), 154–65.
18.
ManraiAjay K., and SinhaPrabhakant (1989), “Elimination-by-Cutoffs,”Marketing Science, 8 (Spring), 133–52.
19.
PayneJohn W. (1976), “Task Complexity and Contingent Processing in Decision Making: An Information Search and Protocol Analysis,”Organizational Behavior and Human Performance, 16 (August), 366–87.
20.
PayneJohn W., and Easton RagsdaleE. K. (1978), “Verbal Protocols and Direct Observation of Supermarket Shopping Behavior: Some Findings and a Discussion of Methods,” in Advances in Consumer Research, Vol. 5, Keith HuntH., ed. Chicago: Association for Consumer Research, 571–7.
21.
RussoJay E., and DosherBarbara A. (1983), “Strategies for Multiattribute Binary Choice,”Journal of Experimental Psychology: Learning, Memory and Cognition, 9 (October), 676–96.
22.
TverskyAmos (1972), “Elimination by Aspects: A Theory of Choice,”Psychological Review, 79 (July), 281–99.
