The Impact of Social Nudges on User-Generated Content for Social Network Platforms

Author:

Zeng Zhiyu1ORCID,Dai Hengchen2ORCID,Zhang Dennis J.3ORCID,Zhang Heng4ORCID,Zhang Renyu5ORCID,Xu Zhiwei6,Shen Zuo-Jun Max78ORCID

Affiliation:

1. Department of Industrial Engineering, Tsinghua University, Beijing 100000, China;

2. Anderson School of Management, University of California, Los Angeles, California 90095;

3. Olin Business School, Washington University in St. Louis, St. Louis, Missouri 63130;

4. W. P. Carey School of Business, Arizona State University, Tempe, Arizona 85287;

5. Department of Decision Sciences and Managerial Economics, The Chinese University of Hong Kong, Hong Kong, China;

6. Independent Contributor, Beijing 100000, China;

7. Department of Industrial Engineering and Operations Research, University of California, Berkeley, Berkeley, California 94720;

8. Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, California 94720

Abstract

Content-sharing social network platforms rely heavily on user-generated content to attract users and advertisers, but they have limited authority over content provision. We develop an intervention that leverages social interactions between users to stimulate content production. We study social nudges, whereby users connected with a content provider on a platform encourage that provider to supply more content. We conducted a randomized field experiment (N [Formula: see text]) on a video-sharing social network platform where treatment providers could receive messages from other users encouraging them to produce more, but control providers could not. We find that social nudges not only immediately boosted video supply by 13.21% without changing video quality but also, increased the number of nudges providers sent to others by 15.57%. Such production-boosting and diffusion effects, although declining over time, lasted beyond the day of receiving nudges and were amplified when nudge senders and recipients had stronger ties. We replicate these results in a second experiment. To estimate the overall production boost over the entire network and guide platforms to utilize social nudges, we combine the experimental data with a social network model that captures the diffusion and over-time effects of social nudges. We showcase the importance of considering the network effects when estimating the impact of social nudges and optimizing platform operations regarding social nudges. Our research highlights the value of leveraging co-user influence for platforms and provides guidance for future research to incorporate the diffusion of an intervention into the estimation of its impacts within a social network. This paper was accepted by Victor Martínez-de-Albéniz, operations management. Funding: H. Dai thanks the University of California, Los Angeles (UCLA) [Hellman Fellowship and Faculty Development Award] for funding support. R. Zhang is grateful for financial support from the Hong Kong Research Grants Council [Grant 16505418]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4622 .

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Management Science and Operations Research,Strategy and Management

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