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

Zhiyu Zeng (), Hengchen Dai (), Dennis J. Zhang (), Heng Zhang (), Renyu Zhang (), Zhiwei Xu () and Zuo-Jun Max Shen ()
Additional contact information
Zhiyu Zeng: Department of Industrial Engineering, Tsinghua University, Beijing 100000, China
Hengchen Dai: Anderson School of Management, University of California, Los Angeles, California 90095
Dennis J. Zhang: Olin Business School, Washington University in St. Louis, St. Louis, Missouri 63130
Heng Zhang: W. P. Carey School of Business, Arizona State University, Tempe, Arizona 85287
Renyu Zhang: Department of Decision Sciences and Managerial Economics, The Chinese University of Hong Kong, Hong Kong, China
Zhiwei Xu: Independent Contributor, Beijing 100000, China
Zuo-Jun Max Shen: Department of Industrial Engineering and Operations Research, University of California, Berkeley, Berkeley, California 94720; Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, California 94720

Management Science, 2023, vol. 69, issue 9, 5189-5208

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 = 993 , 676 ) 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.

Keywords: content production; platform operations; social network; field experiment; information-based intervention (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.2022.4622 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:69:y:2023:i:9:p:5189-5208

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().