دانلود رایگان مقاله انگلیسی کالاهای جستجو شده و آزموده شده درون فروشگاهی یا خرید آنلاین - الزویر 2018

ABSTRACT

This paper aims at explaining the choice between online and in-store shopping for typical search and experience goods (standard electronic appliances and groceries) within an artificial experimental setting assuming no privately owned cars. We present the first alternative-specific integrated choice and latent variable (ICLV) model using stated preference data in the field of shopping behavior research, explicitly asking respondents to trade-off attributes specific to each shopping channel. Respondents with pro-online shopping attitudes have a higher shopping cost sensitivity, which can be explained by the expanded choice set when effectively considering both purchasing channels. They also exhibit a higher choice probability of online shopping for groceries compared to electronic appliances, given the nature of experience goods being preferably purchased in-store, while the pleasure of shopping shows no substantial effect on choice behavior. Results reveal a user profile of pro-online shoppers that is mainly characterized by a technologyoriented generation of younger and well-educated men. Also, given the relatively high value of travel time compared to the value of delivery time, we show that especially for electronic appliances, avoiding a shopping trip produces more benefits than waiting for the delivery of ordered products.

6. Conclusions

This paper presents the first alternative-specific hybrid choice model using stated preference data in the field of shopping behavior research, presenting a sophisticated modeling approach to explore the trade-offs individuals face when choosing between online and instore shopping for two distinctly different types of products: Groceries (G), a typical experience good, and standard electronic appliances (E), a typical search good.

The integrated choice and latent variable (ICLV) approach comes along with an enhanced estimation efficiency and helps to structure respondent heterogeneity via the latent variable efficiently and more intuitively. As we can show for the current application, this leads to a more behaviorally sound representation of individual decision making when comparing to the reduced form Mixed Logit model.

By including two latent variables (LVs) reflecting the attitudes towards online shopping and the pleasure of shopping, the LV structural model reveals information of individual attitudes conditional on observable socio-economic characteristics, which in turn affect the choice of the shopping channel: Given a specific target consumer segment, one can predict alternative-specific market shares and/or heterogeneity in attribute sensitivities such as shopping costs, and based on that, develop an effective retailing strategy.