Many theories of consumer behavior involve thresholds and discontinuities. In this paper, we investigate consumers' use of screening rules as part of a discrete-choice model. Alternatives that pass the screen are evaluated in a manner consistent with random utility theory; alternatives that do not pass the screen have a zero probability of being chosen. The proposed model accommodates conjunctive, disjunctive, and compensatory screening rules. We estimate a model that reflects a discontinuous decision process by employing the Bayesian technique of data augmentation and using Markov-chain Monte Carlo methods to integrate over the parameter space. The approach has minimal information requirements and can handle a large number of choice alternatives. The method is illustrated using a conjoint study of cameras. The results indicate that 92% of respondents screen alternatives on one or more attributes.conjoint analysis, noncompensatory decision process, hierarchical Bayes, revealed choice, attribute screening, consideration sets, elimination by aspects

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Marketing managers are interested in knowing how consumers will react to different product configurations. The product manager can change physical attributes through the design of the product and the perception of psychological attributes through promotion strategies. Because consumers are heterogeneous in their tastes and preferences, aggregate level estimates of attribute importance are insufficient to describe the market. New research methods focus on obtaining individual level estimates of attribute importance from a representative sample of consumers. Marketing researchers have procedural and statistical methods of obtaining measures of attribute importance for each respondent on each attribute. In laboratory or experimental choice settings, studies can be designed to help focus respondents' attention and processing of the product attributes. Bayesian methods of modeling heterogeneity shrink poorly measured individual level parameters to the overall or group level mean. However, it is erroneous to assume that consumers use all the product attributes in all brand choice situations. This thesis demonstrates that improved inference and predictive accuracy can be obtained by modeling which attributes are actually being used by consumers in different discrete choice situations. This thesis contributes new models for determining, at the individual level, which product attributes are being used by a consumer in a brand choice decision. The ii heterogeneous variable selection model extends current aggregate level models of Bayesian variable selection. This model assumes a distribution of heterogeneity with mass concentrated at 0 and away from 0 for each parameter. The pooled variable selection model allows the set of attributes used by an individual to vary by choice context. Examples of separate contexts include partial and full profile choice experiments or choice experiments and actual market place transactions. A hybrid model combines the heterogeneous and pooled variable selection models. The threshold variable selection model incorporates insights from an extended model of choice and provides a behavioral explanation of why certain product attributes are used. Tractable algorithms are introduced for estimating the proposed variable selection models. In the two empirical studies presented, a variable selection model fits the data better than baseline models with no variable selection and conventional distributions of heterogeneity. iii Dedicated to my wife, Teresa Minardi Gilbride iv ACKNOWLEDGMENTS

Consumer choice in surveys and in the marketplace reflects a complex process of screening and evaluating choice alternatives. Behavioral and economic models of choice processes are difficult to estimate when using stated and revealed preferences because the underlying process is latent. This paper introduces Bayesian methods for estimating two behavioral models that eliminate alternatives using specific attribute levels. The elimination by aspects theory postulates a sequential elimination of alternatives by attribute levels until a single one, the chosen alternative, remains. In the economic screening rule model, respondents screen out alternatives with certain attribute levels and then choose from the remaining alternatives, using a compensatory function of all the attributes. The economic screening rule model gives an economic justification as to why certain attributes are used to screen alternatives. A commercial conjoint study is used to illustrate the methods and assess their performance. In this data set, the economic screening rule model outperforms the EBA and other standard choice models and provides comparable results to an equivalent conjunctive screening rule model.elimination by aspects, consideration sets, attribute screening, noncompensatory decision processes, conjoint analysis, hierarchical Bayes

The recent surge in the importance of shopper marketing has led to an increased need to understand the drivers of unplanned purchases. The authors address this issue by examining how elements of the current shopping trip (e.g., lagged unplanned purchase, cumulative purchases) and previous shopping trips (e.g., average historical price paid by the shopper) determine unplanned versus planned purchases on the current trip. Using a grocery field study and frequent-shopper-program data, the authors estimate competing models to test behavioral hypotheses using a hierarchical Bayesian probit model with state dependence and serially correlated errors. The results indicate that shoppers with smaller trip budgets tend to exhibit behavior consistent with a self-regulation model (i.e., an unplanned purchase decreases the probability of a subsequent unplanned vs. planned purchase), but this effect reverses later in the trip. In contrast, shoppers with medium-sized trip budgets tend to exhibit behavior consistent with a cuing theory model (i.e., an unplanned purchase increases the probability of a subsequent unplanned vs. planned purchase), and this effect increases as the trip continues. The article concludes with a discussion of implications for research and practice.