Na Liu
Supervised by Prof. Chris Forman (Cornell University)
Under Review, Information Systems Research
This study examines how commission fee pricing reshapes multi-sided user-generated content (UGC) platforms by analyzing a natural experiment where a major platform eliminated fees for goods-selling ad links in sponsored contents. Using various market-level difference-in-differences models, we reveal three key findings: (1) Advertisers reallocated 13% of placements toward fee-exempt links, demonstrating notable price sensitivity; (2) Platform revenue declined as substitutions outweighed demand expansion; and (3) The fee exemption triggered a redistribution on the creators' side, with contents for treated advertisers gaining 3 additional interactions per 100 views, occurring primarily from top creators. Our work advances information systems research by providing the first causal evidence on how commission fee changes reconfigure multi-sided markets—spanning advertisers, creators, and platform revenues. The findings highlight critical trade-offs: while fee structures steer advertiser behavior, they may inadvertently cannibalize revenue and amplify redistribution among participants in this multi-sided ecosystem. These insights inform platform governance by demonstrating the need to balance participation with profitability and model cross-sided effects before implementing pricing changes.
This study examines how commission fee pricing reshapes multi-sided user-generated content (UGC) platforms by analyzing a natural experiment where a major platform eliminated fees for goods-selling ad links in sponsored contents. Using various market-level difference-in-differences models, we reveal three key findings: (1) Advertisers reallocated 13% of placements toward fee-exempt links, demonstrating notable price sensitivity; (2) Platform revenue declined as substitutions outweighed demand expansion; and (3) The fee exemption triggered a redistribution on the creators' side, with contents for treated advertisers gaining 3 additional interactions per 100 views, occurring primarily from top creators. Our work advances information systems research by providing the first causal evidence on how commission fee changes reconfigure multi-sided markets—spanning advertisers, creators, and platform revenues. The findings highlight critical trade-offs: while fee structures steer advertiser behavior, they may inadvertently cannibalize revenue and amplify redistribution among participants in this multi-sided ecosystem. These insights inform platform governance by demonstrating the need to balance participation with profitability and model cross-sided effects before implementing pricing changes.
Intermediaries in the UGC Digital Economy: MCN Roles and Revenue Sharing
Supervised by Prof. Chris Forman (Cornell) and Prof. Michael Zhang (CUHK)
The rapid expansion of the digital economy has diversified content creation and promotion, positioning digital intermediaries as key actors within the content industry. This study examines the role of Multi-Channel Networks (MCNs) in user-generated content (UGC) platforms through a 60-day platform experiment featuring tiered traffic and cash incentives. Using multiple two-way fixed effects difference-in-differences designs combined with propensity score matching, we quantify how MCN intermediaries affect advertising efficiency and creator compensation. We find that creators working with MCNs experience 56% higher monthly revenue and 18.1% more total views. However, when platforms facilitate MCN-creator partnerships through traffic bonuses, MCNs signed 2.7% more new creators, yet the observed view growth was attributable to platform incentives rather than improvements in content quality—larger MCNs even diverted bonus traffic to incumbent creators. By analyzing entry and exit patterns in MCN-creator collaborations, shifts in creator revenue, and content performance, this research addresses critical research and practice gaps on inferring the plausible range of MCN take rates by isolating the intermediary's "value added" relative to its retained revenue share. It sheds light on how intermediaries balance operational support with profit extraction, offering implications for the regulation of multi-sided digital platforms.
Consumers’ Preferences and Aversion in AIGC
with Prof. Catherine Tucker (MIT)
AI-Generated Content (AIGC) is rapidly transforming digital economies, yet its impact on consumer behavior and trust remains poorly understood. This study investigates how transparency around AI use influences user engagement and economic decisions, with a focus on distinguishing intrinsic aversion (a “taste” for human-made content) from instrumental aversion (fear of economic loss). We design a large-scale field experiment implemented via a retail platform and ad campaigns, testing three disclosure conditions using click-through and conversion rates as behavioral metrics, we quantify the two forms of AI aversion and examine their underlying mechanisms. We supplement experimental results with heterogeneity analysis across demographic and industrial segments, mechanism surveys identifying drivers of trust erosion, and a computer vision model trained to detect AIGC content. Furthermore, we explore the use of GPT-4 as simulated respondents to assess its viability for experimental information system (IS) research. Our findings provide nuanced insights into how AI shapes consumer preferences and platform outcomes, offering strategic guidance for firms and policymakers aiming to implement ethical and effective AIGC deployment
Firm Heterogeneity and AI Industry Development
with Dr. Ting Ma (The Institute of Scientific and Technical Information of China)
Preparing Submission to Research Policy
Preparing Submission to Research Policy
Market Structure and Location-based Food Deserts in Smart Cities
with Prof. Nathan Yang (Cornell)