Improving the effectiveness of personalized recommendations through attributional cues

Abstract

Firms often employ personalized recommendations to help customers make purchase decisions. To improve the effectiveness of their personalized recommendations, some firms use cues to offer transparency on how they collect and use data to derive recommendations. We draw on attribution theory to propose an additional mechanism to improve the effectiveness of personalized recommendations with cues. Attributional cues, which refer to the underlying data (i.e., customers' own data vs. similar customers' data) used for personalized recommendations, aim to increase customers' self‐attribution of personalized recommendations. Specifically, in three experimental studies, we show that attributional cues increase customers' self‐attribution of personalized recommendations, leading to higher trust in and lower reactance to personalized recommendations. The accuracy and valence of the personalized recommendations moderate this attributional effect. As a result, employing attributional cues can be an essential and affordable tool for firms to increase the effectiveness of their personalized recommendations.