With cyber shopping, consumers face a massive amount of product information before an educated purchase decision can be made. Identifying relevant products is therefore laborious for consumers, in particular when they look for non-commodity products such as consumer electronics. Product Recommendation Agents (PRAs) help consumers in finding relevant products efficiently. PRAs recommend a set of products either explicitly according to product attributes preferred by the consumer or implicitly based on consumers’ interests and activities. PRAs retrieve hereby product information from various sources such as a retailer’s product database or a third-party’s review database. This entry introduces and discusses PRAs for cyber shopping consumers from five perspectives: (1) Purchase decision-making, (2) natural language interaction, (3) dynamic pricing, (4) product reviews, and finally, (5) product recommendation infrastructures. Future research directions on PRAs for cyber shopping conclude this entry.
TopOverview
Izak Benbasat from the Information Systems discipline (Gregor & Benbasat, 1999; Todd & Benbasat, 1999) and Gerald Häubl and Valerie Trifts (Häubl & Trifts, 2000) from marketing sciences were one of the pioneering scholars that conducted research on PRAs in the late 1990s. Up until now, several studies have investigated the impact of PRAs on consumer behavior in (1) online shopping situations (Gregor & Benbasat, 1999; Häubl & Murray, 2003; Häubl & Trifts, 2000; Kamis, Koufaris, & Stern, 2008; Komiak & Benbasat, 2006; Pereira, 2001; Senecal & Nantel, 2004; Swaminathan, 2003; Todd & Benbasat, 1999; Xiao & Benbasat, 2007) and (2) in-store shopping situations (Kleijnen, de Ruyter, & Wetzels, 2007; Kowatsch & Maass, 2010; Kowatsch & Maass, to appear; Kowatsch, Maass, Filler, & Janzen, 2008; Lee & Benbasat, 2010; Maass & Kowatsch, 2008a; Maass & Kowatsch, 2008b; Maass, Kowatsch, Janzen, & Varshney, 2011; van der Heijden, 2006). The utility of PRAs for online shopping situations has been already shown. They help to reduce search complexity and consumers’ information overload (Häubl & Trifts, 2000; Todd & Benbasat, 1999), improve decision quality (Pereira, 2001; Xiao & Benbasat, 2007), increase trust in decisions (Gregor & Benbasat, 1999; Komiak & Benbasat, 2006), or influence store preferences and purchase intentions (Kamis, Koufaris, & Stern, 2008).