Perceived effectiveness of recommendation agent routines: search vs. experience goods

The vast amount of information available in online shopping environments has led to the development of shopping agents that seek to assist customers in their purchase decisions. Such recommendation agents use one of two common approaches to build a recommendation: rule-based filtering agents typically ask buyers their product preferences and make a recommendation by comparing these preferences to product features; collaborative filtering agents match users with other buyers who have similar profiles and preferences, and make recommendations based on shared likes and dislikes. We examine how consumers react to these different processes to develop recommendations for both search and experience goods. Our results show that consumers evaluated recommendation agents more favourably for search goods than experience goods. Further, rule-based recommendations were preferred for search goods but not for experience goods. Implications of these results are discussed.