Abstract
Marketing practitioners and academics have shown a keen interest in the processes that drive consumers’ choices since the early work of Guadagni and Little (1982). Over the past decade or so, a number of alternative models have been proposed, implemented and analyzed. The common behavioral assumption that underlines these models of discrete choice is random utility maximization (RUM). The RUM assumption, in its simplest form, posits that a consumer with a finite set of brands to choose from chooses the brand that gives her the maximum amount of utility. An alternative approach would be to assume that consumers choose the alternative that offers them the least disutility. Our paper proposes and tests a broad class of generalized extreme value models based on this hypothesis. We model the decision process of the consumer the assumption random disutility minimization (RDM) and derive a new class of discrete choice models based on this assumption. Our findings reveal that there are significant theoretical and econometric differences between the discrete choice models derived from a RUM framework and the RDM framework proposed in this paper. On the theoretical front we find that the class of discrete choice models based on the assumption of disutility minimization is structurally different from the models in the literature. Further, the models in this class are available in closed form and exhibit the same flexibility as the GEV models proposed by McFadden (1978). In fact, the number of parameters are identical to and have the same interpretation as those obtained via RUM based GEV models. In addition to the theoretical differences we also uncover significant empirical insights. With the computing effort and time for both models being roughly the same this new set of models offers marketing academics and researchers a viable new tool with which to investigate discrete choice behavior.
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References
Anderson, S., A. De Palma, and J. Thisse. (1992). Discrete Choice Theory of Product Differentiation, MIT Press.
Anderson, S. and A. de Palma. (1999). “Reverse Discrete Choice Models.” Regional Science and Urban Economics 29(6), 745–764.
Bell D. and J. Lattin. (1998). “Shopping Behavior and Consumer Preference for Store Price Format: Why ‘Large Basket’ Shoppers Prefer EDLP.” Marketing Science 17(1), 66, 23.
Bell D.R, T.-H. Ho, and C.S. Tang. (1998). “Determining Where to Shop: Fixed and Variable Costs of Shipping.” Journal of Marketing Research 35 (August), 352–369.
Ben-Akiva M.E. and S.R. Lerman. (1985). Discrete Choice Analysis: Theory and Application to Travel Demand. Cambridge, MA: MIT Press.
Ben-Akiva, M. and B. Francois. (1983) μ-Homogeneous Generalized Extreme Value Model, Working Paper, Dept. of Civil Engineering, Cambridge, MA, MIT.
Berry, S., J, Levinsohn, and A. Pakes. (1995). “Automobile Prices in Market Equilibrium.” Econometrica 63 (July): 841–990.
Bhat, C.R. (2003). “Simulation Estimation of Mixed Discrete Choice Models Using Randomized and Scrambled Halton Sequences.” Transportation Research (forthcoming).
Bhat, C.R. (2001). “Quasi-Random Maximum Simulated Likelihood Estimation of the Mixed Multinomial Logit Model.” Transportation Research, 35B, 677–693.
Chintagunta, P.K. (1994). “Heterogeneous Logit Model Implications for Brand Positioning” Journal Of Marketing Research, Chicago; 31(2) 304–308.
Chintagunta, P.K. (1992). “Estimating a Multinomial Probit Model of Brand Choice Using the Method of Simulated Moments.” 11(4).
Dubin, J.A. (1986). “A Nested Logit Model of Space and Water Heat System Choice” Marketing Science, 5(2), 112–124.
DeGroot, M. (1989). “Probability and Statistics.” Reading MA: Addison Wesley Publishing.
Elrod, T. (1987). “Choice Map: Inferring a Product-Market Map from Panel Data.” Marketing Science, Linthicum; Winter 1988; 7(1) 21.
Erdem, T. and M. Keane. (1996). “Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets.” Marketing Science 15(1).
Fang, K.T., S. Kotz, and K.W. Ng. (1990). Symmetric Multivariate and Related Distributions. Chapman and Hall, London.
Fotheringham, A.S. (1988). “Consumer Store Choice and Choice Set Definition.” Marketing Science 7(3), 299–310.
Gonul, F. and K. Srinivasan. (1996). “Estimating the Impact of Consumer Expectations of Coupons on Purchase Behavior: A Dynamic Structural Model.” Marketing Science 15(3), 262–279.
Guadagni, P.M. and J.D.C. Little. (1983). “A Logit Model of Brand Choice Calibrated on Scanner Data.” Marketing Science 2(3), 203–238.
Higgins, E.T., R.S. Friedman, R.E. Harlow, L.C. Idson, O.N. Ayduk, and A. Taylor (2000). “Achievement Orientations from Subjective Histories of Success: Promotion Pride Versus Prevention Pride.” European Journal of Social Psychology 30, 1–23.
Higgins, E.T. (1998). “Promotion and Prevention: Regulatory Focus as a Motivational Principle.” Advances in Experimental Social Psychology 30, 1–46.
Higgins, E.T. (1989). “Continuities and Discontinuities in Self-Regulatory and Self-Evaluative Processes: A Developmental Theory Relating Self and Affect.” Journal of Personality 57 (2), 407–444.
Ho, T.H., C.S. Tang, and D.R. Bell. (1998). “Rational Shopping Behavior and the Option Value of Variable Pricing.” Management Science 44(12, 2), 145–160
Hobbs, J.E. (1997). “Measuring the Importance of Transaction Costs in Cattle Marketing.” American Journal of Agricultural Economics 79, 1083–1095.
Horsky, D. and P. Nelson. (1992). “New Brand Positioning and Pricing in an Oligopolistic Market.” Marketing Science 11(2), 133–121.
Jain, D., P. Chintagunta, and N. Vilcassim. (1994). “A Random-Coefficients Logit Brand-Choice Model Applied to Panel Data.” Journal Of Business & Economic Statistics 12(3), 317.
Joskow P.L. and F.S. Mishkin. (1977). “Electric Utility Fuel Choice Behavior in the United States” International Economic Review 18(3), 719–736.
Kadiyali, V., P. Chintagunta, and N. Vilcassim. (2000). “Manufacturer-Retailer Channel Interactions and Implications for Channel Power: An Empirical Investigation of Pricing in a Local Market.” Marketing Science 19(2), 127.
Kamakura W. and G. Russell. (1989). “A Probabilistic Choice Model For Market Segmentation and Elasticy Structure” Journal of Marketing Research, Chicago 26(4), 379, 12
Kannan, P.K and G. Wright. (1991). “Modeling and Testing Structured Markets: A Nested Logit Approach.” Marketing Science 10(1).
Koppelman, F.S. and C.-H. Wen. (2000). “The Paired Combinatorial Logit Model: Properties, Estimation and Application.” Transportation Research Part B: Methodological 34(2), 75–89.
Koppelman, F.S. and V. Sethi. (2000). “Closed Form Logit Models.” In D.A. Hensher and K.J. Button (eds.), Handbook of Transport Modeling, Oxford: Pergamon Press.
Mas-Colell, A., M.D. Whinston, and J.R. Green. (1995). Microeconomic Theory, Oxford: Oxford University Press.
McFadden, D. (1978). “Modelling the Choice of Residential Location.” In A. Karlqvist, L. Lundqvist, F. Snickars, and J. Weibull (eds.), Spatial Interaction Theory and Planning Models, 75–96, Amsterdam: North Holland.
McFadden, D. (1981). “Econometric Models of Probabilistic Choice.” in C.F. Manski and D. McFadden (eds.), Structural Analysis of Discrete Data with Econometric Applications, MIT Press: Cambridge, MA, 198–272.
McFadden, D. and K. Train. (1999). “Mixed MNL Models for Discrete Response.” with K. Train, Journal of Applied Econometrics 15(5), 447–470, John Wiley & Sons Ltd.: New York, December.
McFadden, D. (2001). “Disaggregate Behavioral Travel Demand’s RUM Side: A 30-Year Retrospective.” The Leading Edge of Travel Behavior Research, David Heshner (ed.), Oxford: Pergamon Press.
McCulloch, R. and P.E. Rossi. (1994) “An Exact Likelihood Analysis of the Multinomial Probit Model.” Journal of Econometrics 64, 207–240.
Mazumdar T. and P. Pappatla. (2000). “An Investigation of Reference Price Segments.” Journal of Marketing Research Chicago 37(2) 246, 13.
Roy R., P. Chintagunta, and S. Haldar. (1996). “A Framework for Investigating Habits, “The Hand of the Past.” and Heterogeneity in Dynamic Brand Choice.” Marketing Science 15(3) 280–299.
Rossi, P.E., R.E. McCulloch, and G.M. Allenby. (1996), “The Value of Purchase History Data in Target Marketing.” Marketing Science 15, 321–340.
Seetharaman, P.B. (2003), “Modeling Multiple Sources of State Dependence in Random Utility Models of Brand Choice: A Distributed Lag Approach.” Marketing Science (forthcoming).
Shugan, S. (1980). “The Cost of Thinking.” Journal of Consumer Research 7(2), 99–111.
Small, K. (1994). “Approximate Generalized Extreme Value Models of Discrete Choice.” Journal of Econometrics, Amsterdam 62(2), 51, 32.
Sudhir, K. (2001). “Competitive Pricing Behavior in the Auto Market: A Structural Analysis.” Marketing Science 20(1), 42.
Swait, J. (2003). “Flexible Covariance Structures for Categorical Dependent Variables Through Finite Mixtures of GEV Models.” Journal of Business and Economic Statistics 21(1), 80–87.
Vovsha, P. (1998). “Application of Cross-Nested Logit Model to Mode Choice in Tel Aviv, Israel, Metropolitan Area, Transportation Research Record 1607, 6–15.
Vuong, Q.H. (1989). “Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses.” Econometrica 57, 307–333.
Winer, R. (1986). “A Reference Price Model of Brand Choice for Frequently Purchased Products.” Journal of Consumer Research, Gainesville; 13(2) 250–257.
Wen, C. and F. Koppelman. (2001). “The Generalized Nested Logit Model.” Transportation Research Part B, 35(7), 627–641.
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JEL Classification: C25, C35, M37, D12
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Misra, S. Generalized Reverse Discrete Choice Models. Quant Market Econ 3, 175–200 (2005). https://doi.org/10.1007/s11129-005-0260-3
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DOI: https://doi.org/10.1007/s11129-005-0260-3