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  • Steven P. Gaskin, MIN DING, +4 authors STEVEN P. GASKIN
  • 2010
Ujwal and from participants in a seminar given at University of Houston and a presentation given at the 2009 Marketing Science Conference in Ann Arbor, Michigan. Carl Mela served as associate editor for this article. The authors investigate the feasibility of unstructured direct elicitation (UDE) of decision rules consumers use to form consideration sets.(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)
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)
We estimate the effect of consumer search on the price of the purchased product in a physical store environment. We implement the analysis using a unique data set obtained from radio frequency identification tags, which are attached to supermarket shopping carts. This technology allows us to record consumers' purchases as well as the time they spent in(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)
This research focuses on consumers who do not have well-formed preferences. As they search and evaluate potential products, they may become exposed to previously unconsidered attributes, and incorporate them into their decision criteria. We model this phenomenon by allowing the consumer to change the weights she assigns to different attributes during the(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)
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)