New Hidden Persuaders: An Investigation of Anchoring Effects of Recommender Systems on Consumer Choice (An Abstract)

Today, websites try to suggest us everything, ranging from our next sweatshirts to our next holiday destinations and even our next friends. Based on past behavior or preference statements, recommender systems implemented on marketers’ webpages generate personalized predictions about the products, countries, or people we presumably like and preselect a small subset comprising only potentially relevant choice options from the large number of available alternatives (e.g., Ansari et al. 2000; Bodapati 2008; Resnick and Varian 1997). This preselection is intended to reduce decision efforts and uncertainty (e.g., Ansari et al. 2000; Herlocker et al. 2000; Tam and Ho 2005) and, thereby, to increase sales, customer satisfaction, as well as loyalty (e.g., Fleder and Hosanagar 2009; Jannach and Hegelich 2009; Senecal and Nantel 2004; Pathak et al. 2010; Pu et al. 2011). While extant studies are typically based on the assumption that a recommender system’s effectiveness depends on its ability to identify consumers’ preferences and, therefore, is measurable by the number of individuals who follow the generated suggestions, the present study, in contrast, focuses on the impact of recommendations on the construction of preferences. In line with the large body of research on behavioral decision-making, preference structures are often not well defined or stable (e.g., Bettman et al. 1998; Slovic 1995) such that preferences are frequently formed during decision-making rather than before and, thus, might be sensitive to recommended items.

The results of two experimental studies, conducted in different contexts (i.e., backpacks and hotels), reveal that recommendations provided by recommender systems cause an anchoring effect on consumer choices which is reflected in biased decisions toward (even randomly) generated suggestions. More precisely, even if recommendations do not match preferences, people still seem to adapt their decisions to the characteristics of recommended items. Thus, this research contributes to research on recommender systems in the marketing discipline (e.g., Ansari et al. 2000; Bodapati 2008; Senecal and Nantel 2004; Ying et al. 2006) by uncovering an anchoring effect on decision-making which has been neglected by previous studies. In addition, the findings of our studies have important implications regarding the employment of recommender systems in practice. On the one hand, our results imply that marketers could use such systems not only to help their customers with purchase decisions but also to “nudge” consumer choices in a desired direction. On the other hand, this research raises important questions in terms of customer protection issues in online shopping environments.