The authors test methods, based on cognitively simple decision rules, that predict which products consumers select for their consideration sets. Drawing on qualitative research, the authors propose disjunctions-of-conjunctions (DOC) decision rules that generalize well-studied decision models, such as disjunctive, conjunctive, lexicographic, and subset conjunctive rules. They propose two machine-learning methods to estimate cognitively simple DOC rules. They observe consumers' consideration sets for global positioning systems for both calibration and validation data. They compare the proposed methods with both machine-learning and hierarchical Bayes methods, each based on five extant compensatory and noncompensatory rules. For the validation data, the cognitively simple DOC-based methods predict better than the ten benchmark methods on an information theoretic measure and on hit rates. The results are robust with respect to format by which consideration is measured, sample, and presentation of profiles. The article closes with an illustration of how DOC-based rules can affect managerial decisions.
AllenbyGreg M., AroraNeeraj, and GinterJames L. (1995), “Incorporating Prior Knowledge into the Analysis of Conjoint Studies,”Journal of Marketing Research, 32(May), 152–62.
2.
AndrewsRick L., and SrinivasanT.C. (1995), “Studying Consideration Effects in Empirical Choice Models Using Scanner Panel Data,”Journal of Marketing Research, 32(February), 30–41.
3.
AroraNeeraj, and HuberJoel (2001), “Improving Parameter Estimates and Model Prediction by Aggregate Customization in Choice Experiments,”Journal of Consumer Research, 28(September), 273–83.
4.
BettmanJames R., LuceMary Frances, and PayneJohn W. (1998), “Constructive Consumer Choice Processes,”Journal of Consumer Research, 25(3), 187–217.
5.
BordleyRobert F., and KirkwoodCraig W. (2004), “Multiattribute Preference Analysis with Performance Targets,”Operations Research, 52(November-December), 823–35.
6.
BorosEndre, HammerPeter L., IbarakiToshihide, and KoganAlexander (1997), “Logical Analysis of Numerical Data,”Mathematical Programming, 79(August), 163–90.
7.
BorosEndre, HammerPeter L., IbarakiToshihide, and KoganAlexander, MayorazEddy, and MuchnikIlya (2000), “An Implementation of Logical Analysis of Data,”IEEE Transactions on Knowledge and Data Engineering, 12(2), 292–306.
8.
BreimanLeo, FriedmanJerome H., OlshenRichard A., and StoneCharles J. (1984), Classification and Regression Trees.Belmont, CA: Wadsworth.
9.
BröderArndt (2000), “Assessing the Empirical Validity of the ‘Take the Best’ Heuristic as a Model of Human Probabilistic Inference,”Journal of Experimental Psychology: Learning, Memory, and Cognition, 26(5), 1332–46.
10.
BronnenbergBart J., and VanhonackerWilfried R. (1996), “Limited Choice Sets, Local Price Response, and Implied Measures of Price Competition,”Journal of Marketing Research, 33(May), 163–73.
11.
ChalonerKathryn, and VerdinelliIsabella (1995), “Bayesian Experimental Design: A Review,”Statistical Science, 10(3), 273–304.
12.
ChaseValerie M., HertwigRalph, and GigerenzerGerd (1998), “Visions of Rationality,”Trends in Cognitive Sciences, 2(June), 206–214.
13.
CooilBruce, WinerRussell S., and RadosDavid L. (1987), “Cross-Validation for Prediction,”Journal of Marketing Research, 24(August), 271–79.
14.
CormenThomas H., LeisersonCharles E., RivestRonald L., and SteinClifford (2001), Introduction to Algorithms, 2d ed. Cambridge, MA: MIT Press.
15.
CuckerFelipe, and SmaleSteve (2002), “On the Mathematical Foundations of Learning,”Bulletin of the American Mathematical Society, 39(1), 1–49.
16.
CurrimImran S., MeyerRobert J., and LeNhan T. (1988), “Disaggregate Tree-Structured Modeling of Consumer Choice Data,”Journal of Marketing Research, 25(August), 253–65.
17.
DawesRobyn, M. (1979), “The Robust Beauty of Improper Linear Models in Decision Making,”American Psychologist, 34(7), 571–82.
18.
DawesRobyn M., and CorriganBernard (1974), “Linear Models in Decision Making,”Psychological Bulletin, 81(2), 95–106.
19.
DeSarboWayne S., LehmannDonald R., CarpenterGregory, and SinhaIndrajit (1996), “A Stochastic Multidimensional Unfolding Approach for Representing Phased Decision Outcomes,”Psychometrika, 61(3), 485–508.
20.
DingMin (2007), “An Incentive-Aligned Mechanism for Conjoint Analysis,”Journal of Marketing Research, 44(May), 214–23.
21.
DingMin, GrewalRajdeep, and LiechtyJohn (2005), “Incentive-Aligned Conjoint Analysis,”Journal of Marketing Research, 42(February), 67–82.
22.
EfronBradley, and TibshiraniRobert (1997), “Improvements on Cross-Validation: The .632+ Bootstrap Method,”Journal of the American Statistical Association, 92(June), 548–60.
GigerenzerGerd, and GoldsteinDaniel G. (1996), “Reasoning the Fast and Frugal Way: Models of Bounded Rationality,”Psychological Review, 103(4), 650–69.
29.
Gigerenzer, Gerd, Peter M. Todd, and the ABC Research Group (1999), Simple Heuristics That Make Us Smart.Oxford: Oxford University Press.
30.
GilbrideTimothy J., and AllenbyGreg M. (2004), “A Choice Model with Conjunctive, Disjunctive, and Compensatory Screening Rules,”Marketing Science, 23(3), 391–406.
31.
GilbrideTimothy J., and AllenbyGreg M. (2006), “Estimating Heterogeneous EBA and Economic Screening Rule Choice Models,”Marketing Science, 25(September-October), 494–509.
32.
HastieTrevor, TibshiraniRobert, and FriedmanJerome H. (2003), The Elements of Statistical Learning.New York: Springer.
33.
HauserJohn, R. (1978), “Testing the Accuracy, Usefulness and Significance of Probabilistic Models: An Information Theoretic Approach,”Operations Research, 26(May-June), 406–421.
34.
HauserJohn R., and WernerfeltBirger (1990), “An Evaluation Cost Model of Consideration Sets,”Journal of Consumer Research, 16(March), 393–408.
35.
HauserJohn R., and WisniewskiKenneth J. (1982), “Dynamic Analysis of Consumer Response to Marketing Strategies,”Management Science, 28(May), 455–86.
36.
HogarthRobin M., and KarelaiaNatalia (2005), “Simple Models for Multiattribute Choice with Many Alternatives: When It Does and Does Not Pay to Face Trade-Offs with Binary Attributes,”Management Science, 51(December), 1860–72.
37.
HuberJoel, and ZwerinaKlaus (1996), “The Importance of Utility Balance in Efficient Choice Designs,”Journal of Marketing Research, 33(August), 307–317.
38.
HughesMarie Adele, and GarrettDennis E. (1990), “Intercoder Reliability Estimation Approaches in Marketing: A Generaliz-ability Theory Framework for Quantitative Data,”Journal of Marketing Research, 27(May), 185–95.
39.
JedidiKamel, and KohliRajeev (2005), “Probabilistic Subset-Conjunctive Models for Heterogeneous Consumers,”Journal of Marketing Research, 42(November), 483–94.
40.
JedidiKamel, and KohliRajeev, and DeSarboWayne S. (1996), “Consideration Sets in Conjoint Analysis,”Journal of Marketing Research, 33(August), 364–72.
41.
JohnsonEric J., MeyerRobert J., and GhoseSanjoy (1989), “When Choice Models Fail: Compensatory Models in Negatively Correlated Environments,”Journal of Marketing Research, 26(August), 255–90.
42.
KanninenBarbara, J. (2002), “Optimal Design for Multinomial Choice Experiments,”Journal of Marketing Research, 39(May), 214–27.
43.
KearnsMichael, and RonDana (1999), “Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation,”Neural Computation, 11(6), 1427–53.
44.
KohaviRon (1995), “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection,” in Proceedings of the 14th International Joint Conference on Artificial Intelligence, Vol. 2. San Francisco: Morgan Kaufmann, 1137–43.
45.
KohliRajeev, and JedidiKamel (2007), “Representation and Inference of Lexicographic Preference Models and Their Variants,”Marketing Science, 26(3), 380–99.
46.
KullbackSolomon, and LeiblerRichard A. (1951), “On Information and Sufficiency,”Annals of Mathematical Statistics, 22(1), 79–86.
47.
LangleyPat (1996), Elements of Machine Learning.San Francisco: Morgan Kaufmann.
48.
LindleyDennis, V. (1956), “On a Measure of the Information Provided by an Experiment,”Annals of Mathematical Statistics, 27(December), 986–1005.
49.
LundCarsten, and YannakakisMihalis (1994), “On the Hardness of Approximating Minimization Problems,”Journal of the Association for Computing Machinery, 41(5), 960–81.
50.
MartignonLaura, and HoffrageUlrich (2002), “Fast, Frugal, and Fit: Simple Heuristics for Paired Comparisons,”Theory and Decision, 52(1), 29–71.
51.
MehtaNitin, RajivSurendra, and SrinivasanKannan (2003), “Price Uncertainty and Consumer Search: A Structural Model of Consideration Set Formation,”Marketing Science, 22(1), 58–84.
52.
MelaCarl F., and LehmannDonald R. (1995), “Using Fuzzy Set Theoretic Techniques to Identify Preference Rules From Interactions in the Linear Model: An Empirical Study,”Fuzzy Sets and Systems, 71(2), 165–81.
53.
MontgomeryH., and SvensonO. (1976), “On Decision Rules and Information Processing Strategies for Choices Among Multiattribute Alternatives,”Scandinavian Journal of Psychology, 17(4), 283–91.
54.
OfekElie, and SrinivasanV. (2002), “How Much Does the Market Value an Improvement in a Product Attribute?”Marketing Science, 21(Fall), 398–411.
55.
OlshavskyRichard W., and AcitoFranklin (1980), “An Information Processing Probe into Conjoint Analysis,”Decision Sciences, 11(July), 451–70.
56.
OrdóñezLisa D., BensonLehmannIII, and BeachLee Roy (1999), “Testing the Compatibility Test: How Instructions, Accountability, and Anticipated Regret Affect Prechoice Screening of Options,”Organizational Behavior and Human Decision Processes, 78(April), 63–80.
57.
PayneJohn, W. (1976), “Task Complexity and Contingent Processing in Decision Making: An Information Search,”Organizational Behavior and Human Performance, 16(August), 366–87.
58.
PayneJohn W., BettmanJames R., and JohnsonEric J. (1988), “Adaptive Strategy Selection in Decision Making,”Journal of Experimental Psychology: Learning, Memory, and Cognition, 14(3), 534–52.
59.
PayneJohn W., BettmanJames R., and JohnsonEric J. (1993), The Adaptive Decision Maker.Cambridge, UK: Cambridge University Press.
60.
PerreaultWilliam D.Jr., and LeighLaurence E. (1989), “Reliability of Nominal Data Based on Qualitative Judgments,”Journal of Marketing Research, 26(May), 135–48.
61.
RobertsJohn H., and LattinJames M. (1991), “Development and Testing of a Model of Consideration Set Composition,”Journal of Marketing Research, 28(November), 429–40.
62.
RossiPeter E., and AllenbyGreg M. (2003), “Bayesian Statistics and Marketing,”Marketing Science, 22(3), 304–328.
63.
ShaoJun (1993), “Linear Model Selection by Cross-Validation,”Journal of the American Statistical Association, 88(June), 486–94.
64.
ShockerAllan D., Ben-AkivaMoshe, BoccaraBruno, and NedungadiPrakash (1991), “Consideration Set Influences on Consumer Decision-Making and Choice: Issues, Models, and Suggestions,”Marketing Letters, 2(3), 181–97.
65.
ShuganSteven (1980), “The Cost of Thinking,”Journal of Consumer Research, 27(2), 99–111.
66.
SiddarthS., BucklinRandolph E., and MorrisonDonald G. (1995), “Making the Cut: Modeling and Analyzing Choice Set Restriction in Scanner Panel Data,”Journal of Marketing Research, 32(August), 255–66.
67.
SimonHerbert, A. (1955), “A Behavioral Model of Rational Choice,”Quarterly Journal of Economics, 69(1), 99–118.
68.
SrinivasanV. (1988), “A Conjunctive-Compensatory Approach to the Self-Explication of Multiattributed Preferences,”Decision Sciences, 19(2), 295–305.
69.
SrinivasanV., and ParkChan Su (1997), “Surprising Robustness of the Self-Explicated Approach to Customer Preference Structure Measurement,”Journal of Marketing Research, 34(May), 286–91.
70.
SwaitJoffre, and ErdemTülin (2007), “Brand Effects on Choice and Choice Set Formation Under Uncertainty,”Marketing Science, 26(September-October), 679–97.
71.
ToubiaOlivier, EvgeniouTheodoros, and HauserJohn (2007), “Optimization-Based and Machine-Learning Methods for Conjoint Analysis: Estimation and Question Design,” in Conjoint Measurement: Methods and Applications, 4th ed., GustafssonAnders, HerrmannAndreas, and HuberFrank, eds. New York: Springer, 231–58.
72.
ToubiaOlivier, and HauserJohn R. (2007), “On Managerial Efficient Designs,”Marketing Science, 26(November-December), 851–58.
73.
TverskyAmos (1972), “Elimination by Aspects: A Theory of Choice,”Psychological Review, 79(4), 281–99.
74.
UrbanGlen L., and HauserJohn R. (2004), “‘Listening In’ to Find and Explore New Combinations of Customer Needs,”Journal of Marketing, 68(April), 72–87.
75.
VapnikVladimir (1998), Statistical Learning Theory.New York: John Wiley & Sons.
76.
WuJianan, and RangaswamyArvind (2003), “A Fuzzy Set Model of Search and Consideration with an Application to an Online Market,”Marketing Science, 22(3), 411–34.
77.
YeeMichael, DahanEly, HauserJohn R., and OrlinJames (2007), “Greedoid-Based Noncompensatory Inference,”Marketing Science, 26(July-August), 532–49.
78.
ZhangTong (2003), “Leave One Out Bounds for Kernel Methods,”Neural Computation, 15(6), 1397–1437.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
