Algorithms versus Friends in Content Recommendations: a Large-scale Randomized Field Experiment
Algorithms and friends are the two dominant mechanisms in online content recommendations and have significantly shaped the information that people are exposed to and consume daily. Theoretically, the content suggested by friends can be different from that recommended by the algorithms in both content quality and the presence of social influence, and thus perform differently in users’ engagement and retention. However, limited empirical evidence has causally identified such differences. We, therefore, conduct a massive-scale field experiment on WeChat involving more than 2.8 million users. We randomly assign the users into three groups: algorithmic recommendations, friends’ recommendations without social cues, and friends' recommendations with social cues. Our results show that content engagement (i.e., click rate and stay time) is significantly larger under algorithmic recommendations, while social engagement (i.e., like rate) and retention are significantly higher under friends’ recommendations. We also observe that the content recommended by friends is more socially contagious. Social influence, the effects of social cues, significantly lift content engagement, social engagement, and retention. Furthermore, we delved into the mechanisms and find that data richness and the degree of homophily positively affect the content engagement in algorithmic and friends’ recommendations respectively. The effects of displaying social cues are affected by the degree of social engagement in WeChat Moment. Our findings have rich implications for the mechanisms and management of content recommendations by algorithms (AI) and friends (humans).
Social Advertising Effectiveness Using a Large-Scale Randomized Field Experiment
Study 1: Social Advertising Effectiveness Across Products
Study 2: Public versus Private Responses to Social Advertising
Most of the empirical evidence on social advertising effectiveness focuses on a single product at a time. As a result, little is known about how the effectiveness of social advertising varies across product categories or product characteristics. We, therefore, collaborated with a large online social network to conduct a randomized ﬁeld experiment measuring social ads effectiveness across 71 products in 25 categories among more than 37 million users. We found some product categories, like clothing, cars and food exhibited signiﬁcantly stronger social advertising effectiveness than other categories like ﬁnancial services, electrical appliances, and mobile games. More generally, we found that status goods, which rely on status-driven consumption, displayed strong social advertising effectiveness. Meanwhile, social ads for experience goods, which rely on informational social inﬂuence, did not perform any better or worse than their theoretical counterpart search goods. Social advertising effectiveness also signiﬁcantly varied across the relative characteristics of ad viewers and their friends shown in ads. Understanding the heterogeneous effects of social advertising across products can help marketers differentiate their social advertising strategies and lead researchers to more nuanced theories of social inﬂuence in product evaluation.
One unique feature of social advertising is the coexistence of public and private consumer responses. Social media allows users’ certain responses (e.g., likes) to be publicly revealed to one's social network. More importantly, public responses can also influence private responses (e.g., clicks). We, therefore, use data from a large-scale field experiment with a major social media platform (WeChat Moments) to investigate how the display of social cues (friends' likes) affects users' public (likes) and private responses (clicks) to social ads. We find that, on average, displaying the first social cue significantly enhances the liking rate and the clickthrough rate. Nevertheless, although showing additional social cues can further increase users' tendency to like an ad, it does not further increase the clickthrough rate. This empirical pattern is consistent with the interplay between informational and normative social influence in social advertising. Overall, we find that the coexistence of the two forces can enhance the conformity effect on the public liking response. However, when normative social influence dominates, a crowding-out effect on the private clicking response may occur. Our results have rich implications for advertisers and social media platforms in regard to the design of social advertising policies and social networks.