Webinars
How Social Recommendations Shape Online Contributions
Recommendation algorithms increasingly shape how users discover content online. Many platforms now rely on social filtering, or prioritizing content from people in a user’s network, to drive engagement and satisfaction. But how does this design choice affect the creation of new content?
In this webinar, we examine a large natural experiment on a major Q&A platform that shifted from content-based recommendations to social filtering. The results reveal an unexpected tension: while social filtering appears to increase user agreement and satisfaction with content, it may also change how (and how much) users contribute knowledge.
We discuss what this paradox means for companies that rely on user-generated content, online communities, and recommendation systems, and how algorithm design can influence both engagement and the long-term vitality of digital platforms.
speakers
Jia Liu is currently an Associate Professor of Marketing and an affiliated Associate Professor in the Department of Industrial Engineering and Decision Analytics (IEDA), Hong Kong University of Science and Technology (HKUST). She is a Research Fellow at the Cambridge Centre for Chinese Management, a Lee Heng Fellow, a recipient of the Excellent Young Scientist Fund from the National Natural Science Foundation of China (NSFC), and a Marketing Science Institute (MSI) Young Scholar. Dr. Liu serves on the editorial boards of three leading international journals—Journal of Marketing Research, Marketing Science, and Journal of Consumer Research—and has received multiple service awards. Her research spans consumer behavior modeling, big data analytics, recommendation systems, revenue management, global branding, and the application of generative artificial intelligence (AIGC) in business, with a strong focus on integrating marketing science and AI. Her work has been published in top-tier journals such as Marketing Science, Management Science, Journal of Marketing Research, and Journal of Marketing. She has received prestigious honors including the John Little Award (INFORMS Best Marketing Paper), the Frank M. Bass Outstanding Paper Award nomination, and several best paper awards at international conferences. Dr. Liu has led and participated in research projects funded by NSFC and the Hong Kong Research Grants Council (RGC), securing multi-million HKD grants. Her research not only advances theoretical innovation but also provides empirical insights and practical guidance for strategic decision-making in the era of globalization and digital transformation. In industry practice, Dr. Liu has held research positions at Meta and Microsoft Research and currently serves as a business advisor to the International Digital Economy Academy at Shenzhen, actively promoting the integration of academic research and industry applications. She collaborates with global enterprises to develop AI-driven marketing strategies and data-based decision tools, helping businesses achieve competitive advantages in the digital and intelligent era. Dr. Liu holds a Ph.D. in Marketing from Columbia University, an M.S. in Statistics from Michigan State University, and a B.S. in Mathematics from Tianjin University.
Ziwei Cong is an Assistant Professor of Marketing at McDonough School of Business, Georgetown University. Her research employs econometrics, statistics, and machine learning methods to investigate: (1) the designs of digital platforms; (2) the decision-making processes of content creators and influencers. Her work has been published in leading marketing journals such as the Journal of Marketing Research and Marketing Science. She is honored to have received several awards for her research, including the 2021 Vithala R. and Saroj V. Rao ISMS Doctoral Dissertation Award (winner), the 2020 Shankar-Spiegel Dissertation Proposal Award (runner-up), and the finalist of the 2020 Best Doctoral Dissertation Proposal Competition at the American Statistics Association (Marketing Section). She has also served as an ad hoc reviewer for Marketing Science, Management Science, and the Journal of Marketing Research, etc.