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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(More)
W e develop and test an active-machine-learning method to select questions adaptively when consumers use heuristic decision rules. The method tailors priors to each consumer based on a " configurator. " Subsequent questions maximize information about the decision heuristics (minimize expected posterior entropy). To update posteriors after each question, we(More)
Consumers often learn their preferences as they search. For example, after test driving new cars, a consumer might find she undervalued trunk space and overvalued sunroofs. Preference learning makes search complex because, each time a product is searched, updated preferences affect the value of all products and the value of subsequent (optimal) search.(More)
Most models of consumer purchase process that are used in operations focus on purchases from a single channel, typically the offline brick-and-mortar store [3]. However, there has been a proliferation of multiple retail channels, such as online and mobile. This proliferation has created the need for operational decisions to model consumer switching between(More)
With the proliferation of multiple sales channels, a firm's operational decisions must account for the switching of consumers between different channels during their purchase process. This paper considers the assortment problem faced by a firm selling products that vary on multiple features through an online and an offline channel. The firm carries a(More)
This paper presents a novel approach to combining search and recommendations methods into one integrated system to satisfy user information seeking needs. It is shown theoretically and experimentally using simulations that the proposed combined approach outperforms "pure" search and "pure" recommendations in those cases when search is hindered by the user's(More)
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