Understanding the Framing of Recommendations

Consumers receive product recommendations on a daily basis. Whereas prior research mostly focused on how to improve the accuracy of recommendations (e.g., Bodapati, 2008; Swaminathan, 2003), research on how recommendations should be communicated to consumers (i.e., how they should be framed) is non-existent. This is particularly surprising, considering the billions of dollars companies like Netflix and Amazon invest per year in recommendation algorithms (Koblin, 2017). Our research is intended to close this gap by exploring the impact of different framings of the same recommendation on consumers’ likelihood to follow it. We distinguish between two types of recommendation framings: User-based framing which emphasizes the similarity between users (e.g., “People who like this also like”) and item-based framing which emphasizes the similarity between items (e.g., “Similar to this item”). In this research, we suggest that consumers are more likely to follow a recommendation when it is presented with the user-based rather than the item-based framing, which we term as the ‘framing effect’. This proposition is rooted in research on conformity documenting that people tend to conform to the preferences of ingroup others (Bearden & Etzel, 1982; Cialdini & Goldstein, 2004). We propose that user-based framing enhances the recognition of social clustering (i.e., categorization of users based on preference overlap) as the recommendation strategy and, thus, prompts people to conform to similar others’ preferences. Here, we present four studies (one field experiment) that tested our theorizing.