Sequential Sampling Models of Choice: Some Recent Advances

Choice models in marketing and economics are generally derived without specifying the underlying cognitive process of decision making. This approach has been successfully used to predict choice behavior. However, it has not much to say about such aspects of decision making as deliberation, attention, conflict, and cognitive limitations and how these influence choices. In contrast, sequential sampling models developed in cognitive psychology explain observed choices based on assumptions about cognitive processes that return the observed choice as the terminal state. We illustrate three advantages of this perspective. First, making explicit assumptions about underlying cognitive processes results in measures of deliberation, attention, conflict, and cognitive limitation. Second, the mathematical representations of underlying cognitive processes imply well documented departures from Luce’s Choice Axiom such as the similarity, compromise, and attraction effects. Third, the process perspective predicts response time and thus allows for inference based on observed choices and response times. Finally, we briefly discuss the relationship between these cognitive models and rules for statistically optimal decisions in sequential designs

Choice Models in Marketing: Economic Assumptions, Challenges and Trends

Direct utility models of consumer choice are reviewed and developed for understanding consumer preferences. We begin with a review of statistical models of choice, posing a series of modeling challenges that are resolved by considering economic foundations based on con-strained utility maximization. Direct utility models differ from other choice models by directly modeling the consumer utility function used to derive the likelihood of the data through Kuhn-Tucker con-ditions. Recent advances in Bayesian estimation make the estimation of these models computationally feasible, offering advantages in model interpretation over models based on indirect utility, and descriptive models that tend to be highly parameterized. Future trends are dis-cussed in terms of the antecedents and enhancements of utility function specification.

Adjusting Choice Models to Better Predict Market Behavior

The emergence of Bayesian methodology has facilitated respondent-level conjoint models, and deriving utilities from choice experiments has become very popular among those modeling product line decisions or new product introductions. This review begins with a paradox of why experimental choices should mirror market behavior despite clear differences in content, structure and motivation. It then addresses ways to design the choice tasks so that they are more likely to reflect market choices. Finally, it examines ways to model the results of the choice experiments to better mirror both underlying decision processes and potential market choices.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47012/1/11002_2005_Article_5885.pd

The Impact of Brand Quality on Shareholder Wealth

This study examines the impact of brand quality on three components of shareholder wealth: stock returns, systematic risk, and idiosyncratic risk. The study finds that brand quality enhances shareholder wealth insofar as unanticipated changes in brand quality are positively associated with stock returns and negatively related to changes in idiosyncratic risk. However, unanticipated changes in brand quality can also erode shareholder wealth because they have a positive association with changes in systematic risk. The study introduces a contingency theory view to the marketing-finance interface by analyzing the moderating role of two factors that are widely followed by investors. The results show an unanticipated increase (decrease) in current-period earnings enhances (depletes) the positive impact of unanticipated changes in brand quality on stock returns and mitigates (enhances) their deleterious effects on changes in systematic risk. Similarly, brand quality is more valuable for firms facing increasing competition (i.e., unanticipated decreases in industry concentration). The results are robust to endogeneity concerns and across alternative models. The authors conclude by discussing the nuanced implications of their findings for shareholder wealth, reporting brand quality to investors, and its use in employee evaluation

A Unified Approach to Identifying, Estimating and Testing Demand Structures with Aggregate Scanner Data

A one-to-one map between a specific demand structure and a restricted cross-elasticity matrix is developed and tested using a random utility model. The resulting demand structure can be used to evaluate a firm's ability to differentiate its products, and can help determine if demand is characterized by factors that are easy to change. Similar brands are assumed to share common attributes that are stochastically evaluated by the consumer, resulting in a random utility model with correlated errors. The map results in more precise cross-elasticity estimates and can be used to both identify and test possible structures.market structure, scanner data, cross elasticities