| Abstract: |
One advantage of advertising on social media is leveraging users’ expression of “liking” to influence the perceptions and responses of others in their network. We investigate whether displaying more “likes” in an ad can effectively lead to more ad “likes” and clicks, using data from a large-scale field experiment. We find that displaying the first “like” can significantly increase users’ tendency to both like and click on an ad. However, on average, showing additional “likes” does not further increase the clicking propensity, although it always attracts more “likes”. We further find that (1) displaying more “likes” increasingly improves the clickthrough rate for lesser-known brands but not for well-known brands, and (2) displaying more ”likes” has a stronger impact on the “liking” rate for more socially engaged users than for less socially engaged ones. These empirical patterns are consistent with the interplay between informational and normative social influence in social advertising. The coexistence of the two forces can enhance the conformity effect on liking. However, when normative social influence dominates, a crowding-out effect on clicking may occur. Moreover, evidence from lab studies corroborates that informational and normative social influence affect both liking and clicking, while normative social influence exhibits a stronger impact in liking than in clicking, which can be attributable to the visibility of liking. We discuss the implications for advertisers and media platforms regarding the design of social advertising policies and social networks.
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Biography:
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Shan Huang is currently an Assistant Professor at the Faculty of Business and Economics at the University of Hong Kong. Previously, she held the position of Assistant Professor at the Foster School of Business at the University of Washington, Seattle, from 2018 to 2020. Additionally, she serves as a Digital Fellow at the Stanford Digital Economy Lab.
Shan’s research focuses on the digital economy, social networks, and business analytics. Her work explores the effects of social advertising and social referrals on product virality. She also investigates the role of algorithms and social connections in shaping the diffusion of online content. Furthermore, she studies how A/B tests can be utilized to enhance managerial decision-making. Her research findings have been published in prestigious journals and presented at renowned conferences. She has closely engaged with industry partners in understanding and analyzing various aspects of the digital landscape. She obtained her bachelor's degree from Tsinghua University and completed her Ph.D. at the MIT Sloan School of Management.
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