About Me:

I am an Assistant Professor of Marketing at McDonough School of Business, Georgetown University. My research employs econometrics, statistics, and machine learning methods to investigate: (1) the designs of digital platforms (e.g., recommendation algorithms and business models); (2) the decision-making processes of content creators and influencers.

My work has been published in leading marketing journals, including the Journal of Marketing Research and Marketing Science. I am honored to have received several awards for my research, including the Vithala R. and Saroj V. Rao ISMS Doctoral Dissertation Award (winner, 2022), the Shankar-Spiegel Dissertation Proposal Award (runner-up, 2021), and the finalist of the Best Doctoral Dissertation Proposal Competition at the American Statistical Association, Marketing Section (2021). I have also served as an ad hoc reviewer for Marketing Science, Management Science, and the Journal of Marketing Research, etc.

RA Hiring: I hire part-time research assistants with solid quantitative skills in computer science, econometrics, or data science.

For more about my background, check out my CV.

Publications:

• The Daily Me versus the Daily Others: How Do Recommendation Algorithms Change User Interests? Evidence from a Knowledge-Sharing Platform
  Liu, Jia and Ziwei Cong
  Journal of Marketing Research, 2023, 60(4), 761-791
  
  • Featured in "My Journal Reader" by American Marketing Association
  [ Show Abstract ] [ Paper ]
  

  Recommender systems on online platforms are often accused of polarizing user attention and consumption. The authors examine this phenomenon using a quasi-experiment conducted by Zhihu, the largest online knowledge-sharing platform (or Q&A community) in China. Zhihu originally used a content-based filtering algorithm, which recommends content to users on the basis of the topics to which each user has subscribed. After more than a year, Zhihu moved to a social filtering algorithm, which recommends content with which users' social connections are already engaged. The authors find that this algorithm change increased the creation of social ties by approximately 15% but decreased question subscriptions by 20% and answer contributions by 23%. The authors show that users' increased social interests mainly involved following popular users, leading to a greater concentration of social interests on the platform. However, users' topical interests became less concentrated, as popular topics received significantly fewer subscribers than unpopular topics. The authors explain these findings by exploring the underlying mechanism. They show that compared with content-based filtering algorithms, social filtering algorithms are more likely to expose general users to content consumed by their followees, who are more interested in niche topics than general users are.



• Understanding Users' Content Contribution Behavior When Content Can Be Priced
  Cong, Ziwei, Ying Zhao, and Zilei Zhang
  Marketing Science, 2025, 44(3), 491-732
  
  • Featured in "My Journal Reader" by American Marketing Association
  [ Show Abstract ] [ Paper ]
  

  With the rise of the creator economy, content platforms are exploring ways for creators to monetize their content. An increasingly popular approach is enabling content creators to offer paid content directly to viewers. This paper studies whether giving creators the option of providing paid content influences their incentives to contribute free content. Our setting is an online knowledge-sharing platform, which started out as a Q&A community based on users' voluntary contributions and later introduced a program that allows participants to monetize their expertise by hosting paid live events. We find that participant motivations for contributing answers are rekindled after the program announcement, whereas the contributions of the nonparticipants continue to decrease. Further analysis shows that the motivating effect of program announcement is more pronounced for less established participants or participants facing more intense competition. Moreover, we show that participants adjust their answer contributions according to the time and topics of their paid events. These results suggest that participants strategically leverage free content to boost their reputation and attract "eyeballs" for their paid content. We discuss the implications of our findings for content platforms and content creators.



• Understanding the Evolvement of Multi-User Activities on Content Platforms: A Multi-Dynamic Neural Poisson System
  Yin, Mingzhang, Ziwei Cong and Jia Liu
  Marketing Science, Forthcoming
  [ Show Abstract ] [ Paper ]
  

  Given the increasing importance of user engagement on digital platforms, this paper proposes a Bayesian deep learning model, called the Multi-Dynamic Neural Poisson System (MDNPS), that can capture the multifaceted nature of user sparse but high-dimensional activities on such platforms. MDNPS yields semantically interpretable factors underlying high-dimensional items, quantifies user preferences at different granularity levels, and adeptly captures their temporal and cross-activity dependencies. This model is scalable to large empirical data and can be inferred efficiently with a stochastic variational Bayes algorithm. We apply MDNPS to the largest knowledge-sharing platform in China, focusing on the dynamics in user content consumption and contribution behaviors. We show that MDNPS significantly outperforms benchmark factor models in factor quality and out-of-sample data fitting. Our platform-level and individual-level estimates show rich and interpretable insights about consumer preference dynamics and their relationships with user popularity. These insights offer valuable managerial implications for platforms to understand user segments, personalize content offerings, and enhance user engagement.



• From Live to Recording: Consumer Demand and Response to Price Across the Livestreaming Lifecycle
  Cong, Ziwei, Jia Liu, and Puneet Manchanda
  Marketing Science, Forthcoming
  [ Show Abstract ] [ Paper ]
  

  Livestreaming has evolved into a thriving industry where creators can directly monetize and engage with their audiences and followers. In practice, creators and platforms typically concentrate their marketing efforts on the period leading up to the livestream. However, livestreaming events naturally transition into recorded formats once the event concludes, creating potential "residual" opportunities for monetization. This study systematically examines consumer demand for live events throughout the entire livestream life-cycle, using data from a large livestreaming platform that allows consumers to purchase the recorded version of a paid live event after the livestream ends. We find that the demand is surprisingly more price-sensitive during the pre-livestream period compared to the post-period. This is partly driven by two mechanisms: consumer self-selection (infrequent consumers who may have missed the live events exhibit a higher willingness to pay for recorded versions) and quality uncertainty (consumers face higher uncertainty in event quality during the pre-period than in the post-period). Our findings generate implications for the pricing and targeting strategies in livestreaming markets.

Working Papers:

• When Connections Depress Contributions: The Hidden Cost of Social Filtering
  Cong, Ziwei, Jia Liu, and Shijie Han
  Under Review
  • MSI Working Paper Series, 2025


• Personalizing Language Models for Generative Targeting
  Yin, Mingzhang, Ruijiang Gao, and Ziwei Cong
  Under Review


• Ranking for Neutrality: The Impact of Algorithmic Ranking on Content Production and User Engagement
  Cong, Ziwei and Anita Rao
  Under Review


• Supplier Response to Recommendations: Evidence from a Business-to-Business Electronic Commerce Platform
  Chen, Yi, Ziwei Cong, Yixing Chen, and Liwen Hou
  Under Review