Consumer decision support systems: Internet versus in-store application

In-store consumer behavior: How mobile recommendation agents influence usage intentions, product purchases, and store preferences

“…Recently, several studies evaluated the impact of digital replications with different sensual experiences by virtual reality simulations, which facilitate further transfer of physical shopping experiences into digital shopping environments (Lee & Chung, 2008;Zhenhui & Benbasat, 2004). By contrast and consistent with our approach, the opposite direction investigates how E-Commerce services can be embedded into physical shopping environments by desktop-based (Westerman et al, 2007) or mobile and ubiquitous computing technologies (Junglas & Watson, 2006;Kleijnen, de Ruyter, & Wetzels, 2007;van der Heijden, 2006;Watson, Pitt, Berthon, & Zinkhan, 2002). In these scenarios, rich product information becomes available at the point of sale and can be dynamically adapted to consumers' needs by using a product interface that embeds value added services.…”

“…Up until now, little research has been conducted on the utility of RAs for in-store purchase decision-making. For example, Westerman, Tuck, Booth, and Khakzar (2007) found that a desktop-based RA improves the quality of purchase decisions by providing recommendations based on a weighted adding model. In another lab experiment, product information provided by a MRA was perceived as being better than static product information (e.g., information printed on product labels) particularly for product bundle purchases in in-store situations (Kowatsch, Maass, Filler, & Janzen, 2008;Maass & Kowatsch, 2008a).…”

MobileCDP: A mobile framework for the consumer decision process

“…When more alternatives are taken into account, a better decision is possible [130]. Westerman, et al [114] show that the quality of decisions is better when participants have access to a decision support system. A recent study shows that simple decision aids provided by many online retailers, such as the ability to sort on a particular attribute or eliminate undesired alternatives, decreases decision quality, especially when choice conflict is high [115].…”

A Mashup Application to Support Complex Decision Making for Retail Consumers

“…The prototype mashup used in this experiment incorporated only rudimentary decision tree logic, with only several criteria preferences included (some being pre-specified) and as preferences were entered or changed, decision options and/or constraints were updated. Existing research has demonstrated the utility of using more sophisticated techniques for eliciting preferences (Gao et al, 2007;van der Heijden, 2006;Westerman, Tuck, Booth, & Khakzar, 2007) and/or utilizing more complex multi-criteria decision techniques such as the Analytical Hierarchy Process (Chen, 2003;Mustajoki & Hamalainen, 2007;Williams et al, 2007). Further research should explore the utility of incorporating mashup and wikibased knowledge repositories with additional decision-making techniques.…”