EXPRESS: Consumer Social Connectedness and Persuasiveness of Collaborative-Filtering Recommender Systems: Evidence from an Online-to-Offline Recommendation App

Abstract

Consumers often rely on their social connections or social technologies, such as (automated) system-generated recommender systems, to navigate the proliferation of diverse products and services offered in online and offline markets and cope with the corresponding choice overload. In this study, we investigate the relationship between the consumers’ social connectedness and the economic impact of recommender systems. Specifically, we examine whether the social connectedness levels of consumers moderate the effectiveness of online recommendations toward increasing product demand levels. We study this novel research question using a combination of datasets and a demand-estimation model. Interestingly, the empirical results show a positive moderating effect of social connectedness on the demand effect of online-to-offline recommendations. Further delving into the findings, we also provide empirical evidence that social identification might explain why denser social connectedness with local users accentuates the effects of collaborative filtering online-to-offline recommendations. Our study enhances the understanding of community factors affecting the efficacy of social technologies in multi-channel operations while also extending the social identity theory in operations in the digital realm. The results also have intriguing operational implications for operations managers and practitioners, while suggesting several interesting avenues for future research on social technologies and operations management.