The authors integrate concepts in conjoint analysis and discrete choice theory in econometrics to develop a new approach to the design and analysis of controlled consumer choice or resource allocation experiments. The article is concerned with estimating the parameters of conjoint-type functions from discrete choice or allocation data. Emphasis is placed on the multinomial logit model and aggregate choice or allocation data to illustrate the concepts in a series of empirical examples ranging from simple to complex. The authors present limited external validity evidence to support the approach and make comparisons with traditional conjoint approaches.
Get full access to this article
View all access options for this article.
References
1.
AjzenI. and FishbeinM. (1977), “Attitude-Behavior Relations: A Theoretical Analysis and Review of Empirical Research,” Psychological Bulletin, 84, 888–918.
2.
AmemiyaT. (1981), “Qualitative Response Models: A Survey,” Journal of Economic Literature, 19, 1483–1536.
3.
AndersonN. H. (1981), Foundations of Information Integration Theory.New York: Academic Press.
4.
AndersonN. H. and ShanteauJ. (1977), “Weak Inference with Linear Models,” Psychological Bulletin, 84, 1155–70.
5.
ArnoldS. J., RothV., and TigertD. G. (1980), “Conditional Logit Versus MDA in the Prediction of Store Choice,” Advances in Consumer Research, 8, 665–70.
6.
BatsellR. R. (1980), “Consumer Resource Allocation Models at the Individual Level,” Journal of Consumer Research, 7, 78–87.
7.
BatsellR. R. and LodishL. M. (1981), “A Model and Measurement Methodology for Predicting Individual Consumer Choice,” Journal of Marketing Research, 18 (February), 1–12.
8.
BhapkarV. P. (1966), “A Note on the Equivalence of Two Test Criteria for Hypotheses in Categorical Data,” Journal of the American Statistical Association, 61, 228–35.
9.
BirnbaumM. H. (1973), “The Devil Rides Again: Correlation as an Index of Fit,” Psychological Bulletin, 79, 239–42.
10.
BradleyR. A. (1953), “Some Statistical Methods in Taste Testing and Evaluation,” Biometrics, 9, 22–38.
11.
BradleyR. A. (1965), “Another Interpretation of a Model for Paired Comparisons,” Psychometrika, 30, 315–18.
12.
CarmoneF. J. and GreenP. E. (1981), “Model Misspecification in Multiattribute Parameter Estimation,” Journal of Marketing Research, 18 (February), 87–93.
13.
CattinP. and WittinkD. R. (1982), “Commercial Use of Conjoint Analysis: A Survey,” Journal of Marketing, 46, 44–53.
14.
CurrimI. S. (1982), “Predictive Testing of Consumer Choice Models Not Subject to Independence of Irrelevant Alternatives,” Journal of Marketing Research, 19 (May), 208–22.
15.
CurryD. J., LouviereJ. J., and AugustineM. J. (1980), “On the Sensitivity of Brand-Choice Simulations to Attribute Importance Weights,” Decision Sciences, 12 (July), 502–16.
16.
CurryD. J., LouviereJ. J., and AugustineM. J. (1983), “The Aggregate Effects of Induced Changes in Consumer Decision Structures,” Research in Marketing, 6, Greenwich: JAI Press, Inc.
17.
GenschD. H. and ReckerW. W. (1979), “The Multinomial, Multiattribute Logit Choice Model,” Journal of Marketing Research, 16 (February), 124–32.
18.
GreenP. E. (1974), “On the Design of Choice Experiments Involving Multifactor Alternatives,” Journal of Consumer Research, 1, 61–8.
19.
GreenP. E., CarrollJ. D., and GoldbergS. M. (1981), “A General Approach to Product Design Optimization Via Conjoint Analysis,” Journal of Marketing, 45 (3), 17–27.
20.
GreenP. E., DeSarboW. S., and KediaP. K. (1980), “On the Insensitivity of Brand-Choice Simulations to Attribute Importance Weights,” Decision Sciences, 11, 439–50.
21.
GrizzleJ. E., StarmerC. F., and KochG. G. (1969), “Analysis of Categorical Data by Linear Models,” Biometrics, 25, 489–504.
22.
HahnG. J. and ShapiroS. S. (1966), “A Catalog and Computer Program for the Design and Analysis of Symmetric and Asymmetric Fractional Factorial Experiments,” Technical Report No. 66-C-165, General Electric Research and Development Center, Schenectady, NY.
23.
HensherD. A. and JohnsonL. (1981), Applied Discrete Choice Modelling.London: Croom-Helm.
24.
HorowitzJ. L. (1981a), “Identification and Diagnosis of Specification Errors in the Multinomial Logit Model,” Transportation Research, 15B, 345–60.
25.
HorowitzJ. L. (1981b), “Testing the Multinomial Logit Model Against the Multinomial Probit Model Without Estimating the Probit Parameters,” Transportation Science, 15, 153–63.
26.
HuberJ. (1982), “Accounting for Attraction Bias in Choice: An Examination of Center Seeking and Reweighting Explanations,” paper presented to 13th Annual Conference of Association for Consumer Research, San Francisco (October).
27.
JohnsonR. M. (1974), “Tradeoff Analysis of Consumer Values,” Journal of Marketing Research, 11 (May), 121–7.
28.
KeeneyR. L. and RaiffaH. (1976), Decisions with Multiple Objectives: Preferences and Value Tradeoffs.New York: John Wiley & Sons, Inc.
29.
KrantzD. H. and TverskyA. (1971), “Conjoint-Measurement Analysis of Composition Rules in Psychology,” Psychological Review, 78, 151–69.
30.
LevinI. P., LouviereJ. J., NormanK. L., and SchepanskiA. (1983), “External Validity Tests of Laboratory Studies of Information Integration,” Organizational Behavior and Human Performance, 31, 173–93.
31.
LouviereJ. J. (1981), “On the Identification of the Function Form of the Utility Expression and Its Relationship to Discrete Choice,” Appendix B in Applied Discrete Choice Modelling, HensherD. A. and JohnsonL.London: Croom-Helm, 385–416.
32.
LouviereJ. J. and HensherD. A. (1982), “On the Design and Analysis of Simulated Choice or Allocation Experiments in Travel Choice Modelling,” Transportation Research Record No. 890, 11–17.
33.
LouviereJ. J. and HensherD. A. (1983), “Forecasting Consumer Demand for a Unique Cultural Event: An Approach Based on an Integration of Probabilistic Discrete Choice Models and Experimental Design Data,” Journal of Consumer Research, December, in press.
34.
LouviereJ. J., WoodwormG. G., and AndersonD. A. (1981), “Predicting Consumer Choice of Destination and Ticket for International Travel from Australia,” paper presented to South Pacific Meetings of Regional Science Association, Surfers’ Paradise, Queensland, Australia (August).
35.
LuceR. D. (1959), Individual Choice Behavior: A Theoretical Analysis.New York: John Wiley & Sons, Inc.
36.
LuceR. D. (1977), “The Choice Axiom After Twenty Years,” Journal of Mathematical Psychology, 3, 215–33.
37.
MahajanV., GreenP. E., and GoldbergS. M. (1982), “A Conjoint Model for Measuring Self- and Cross-Price/Demand Relationships,” Journal of Marketing Research, 19 (August), 334–42.
38.
ManskiC. F. and McFaddenD., eds. (1981), Structural Analysis of Discrete Data.Cambridge, MA: MIT Press.
39.
McFaddenD. (1974a), “Conditional Logit Analysis of Qualitative Choice Behavior,” in Frontiers in Econometrics, ZarembkaP., ed. New York: Academic Press, 105–42.
40.
McFaddenD. (1974b), “The Measurement of Urban Travel Demand,” Journal of Public Economics, 303–28.
41.
McFaddenD. (1979), “Quantitative Methods for Analyzing Travel Behavior of Individuals: Some Recent Developments,” in Behavioural Travel Modelling, HensherD. A. and StopherP. R., eds. London: Croom-Helm, 279–318.
42.
McFaddenD., TyeW., and TrainK. (1976), “Diagnostic Tests for the Independence from Irrelevant Alternatives Property of the Multinomial Logit Model,” Working Paper No. 7616, Institute of Transportation Studies, University of California, Berkeley.
43.
NakanishiM. and CooperL. G. (1974), “Parameter Estimation for a Multiplicative Competitive Interaction Model—Least Squares Approach,” Journal of Marketing Research, 11 (August), 303–11.
44.
NakanishiM. and CooperL. G. (1982), “Simplified Estimation Procedures for MCI Models,” Marketing Science, 1 (3), 314–22.
45.
National Bureau of Standards (1957), Fractional Factorial Experimental Designs for Factors at Two Levels, NBS Applied Mathematics Series, No. 48.
46.
PunjG. N. and StaelinR. (1978), “The Choice Process for Graduate Business Schools,” Journal of Marketing Research, 15 (November), 588–98.
47.
ReibsteinD. J. (1978) “The Prediction of Individual Probabilities of Brand Choice,” Journal of Consumer Research, 5, 163–8.
48.
SAS Institute (1979), SAS User's Guide. Cary, NC.
49.
SkvoretzJ., WindellP., and FararoT. J. (1974), “Luce's Axiom and Occupational Prestige: Test of a Measurement Model,” Journal of Mathematical Sociology, 3, 147–62.
50.
SlovicP., FischhoffB., and LichtensteinS. (1977), “Behavioral Decision Theory,” Annual Review of Psychology, 28, 1–39.
51.
TheilH. (1971), Principles of Econometrics.New York: John Wiley & Sons, Inc.
52.
TimmermansH. T. and VeldhuisenK. J. (1981), “Behavioural Models and Spatial Planning: Some Methodological Considerations and Empirical Tests,” Environment and Planning A, 13, 1485–98.
53.
TverskyA. (1972), “Elimination by Aspects: A Theory of Choice,” Psychological Review, 79, 281–99.
54.
WestinR. B. and WatsonP. L. (1975), “Reported and Revealed Preferences as Determinants of Mode Choice Behavior,” Journal of Marketing Research, 12 (August), 282–9.
55.
WilksS. S. (1962), Mathematical Statistics.New York: John Wiley & Sons, Inc.
56.
YellotJ. E. (1977), “The Relationship Between Luce's Choice Axiom, Thurstone's Theory of Comparative Judgment, and the Double Exponential Distribution,” Journal of Mathematical Psychology, 15, 109–44.
