Prick “filter bubbles” by enhancing consumers' novelty‐seeking: The role of personalized recommendations of unmentionable products

Personalized recommendation algorithms inadvertently foster “filter bubbles,” wherein consumers are predominantly exposed to information that aligns with their existing preferences, limiting their exposure to novel items. This phenomenon raises ethical concerns regarding consumer well‐being, as it potentially compromises the quality of consumption decisions by reinforcing a homogeneity of information. Introducing novelty into recommendation systems is a viable strategy to counteract this issue, as the predominance of homogeneous information plays a crucial role in the formation of filter bubbles. However, there is a notable gap in the literature regarding self‐directed strategies for consumers to break through these filter bubbles. Grounded in social identification theory and utilizing a series of experimental studies, our research employs a range of analytical techniques, including ANOVA, mediation, and moderated‐mediation analysis. Our findings suggest that personalized recommendations of unmentionable products, defined as products eliciting disgust, offense, or anger due to delicacy, ethics, or fear, (vs. ordinary products) can increase consumers' novelty‐seeking by enhancing their motivation to change their implicit social labels given by intelligent recommendation systems. Nonetheless, we observe that this drive for novelty‐seeking diminishes during social‐focused recommendations because this recommendation is based on the behaviors of others in consumers' social networks rather than their actions.