英语翻译We build on previous work in consumer shopping trip mode

英语翻译
We build on previous work in consumer shopping trip modeling and conjoint choice design to develop a new approach to investigating the tendency of consumers to combine multiple shopping purposes and destinations in their shopping trips.1 Our proposed approach is based on a combination of a multi-layered nested logit structure which captures the different purposes and stops in consumer shopping trips,and a purposely designed consumer choice experiment which allows one to measure aspects of consumer shopping trip decision making that are difficult to deduce from real world shopping data.In modeling terms,we build mainly on two previous models in the consumer shopping behavior literature.The first,introduced by Kitamura (1984),used the concept of prospective utility to model consumers’ choices of shopping trip chains.Prospective utility is defined recursively and the utility of each shopping destination is expressed in terms of its own utility plus the utility of possible other destinations in the trip chain,which itself again includes the utility of other destinations,etc.The model offers a framework to model trip chains or multi-stop trips that consumers make,but unfortunately falls short in that it does not allow for multi-purpose trips (i.e.trips where consumers buy several different types of products).
The second model we build on was introduced by Arentze,Borgers and Timmermans (1993) and largely deals with this latter element.Arentze,Borgers and Timmermans introduced a model that recursively describes consumer store choices for different categories of products.Products are ordered hierarchically on the basis of purchase frequency,and the utility of a shopping center is described as a function of two types of variables:the distance between the consumer’s home and the shopping center,and the shopping center’s range of product categories.However,their model does not incorporate multi-stop trips and it is not developed in the utility maximization framework of random utility theory.This makes it difficult to interpret the parameters of the model,to compare the model to empirical data that is stochastic in nature,or to compare the model to more conventional random utility approaches.
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