Guangzhi Chen
Guangzhi Chen (陈光智)
On the 2026–2027 academic job market
Hi! I am a Ph.D. candidate in Quantitative Marketing at the Warrington College of Business, University of Florida.
My research interests center on building theoretical models to examine strategic interactions between firms and consumers in digital markets, in which I aim to identify important mechanisms that meaningfully shape market decisions and outcomes. My current work studies e-commerce marketplace design, technology adoption (e.g., personalized pricing, artificial intelligence), and digital product monetization. I am also interested in empirical modeling and data-driven approaches.
Please feel free to reach out at guangzhi.chen@ufl.edu.
Research Interests
Online Platform Digital Marketing Economics of AI Applied Game Theory Empirical Modeling
Research
Working Papers
Job Market Paper
Product Discoverability in E-Commerce Marketplaces with Seller External Advertising
Guangzhi Chen, Zheyin (Jane) Gu, and Tianxin Zou
Major revision at Journal of Marketing Research
Abstract
This paper examines how an e-commerce marketplace should design product discovery tools, which help consumers find products on the platform, accounting for sellers potentially advertising on external channels (e.g., search engines and social media) to attract consumers to the marketplace. Our paper’s central finding is that the platform can profit from strategically limiting product discovery because of two distinct mechanisms: (1) a traffic-expansion mechanism, where limiting discovery mitigates advertising leakage and encourages sellers to advertise, expanding total platform traffic; (2) a differentiation mechanism, where limiting discovery induces some but not all sellers to advertise, creating differentiation in consumer awareness among sellers and thus softening price competition. We also show that the platform’s optimal discoverability level changes non-monotonically with different market parameters, such as how substitutable competing products are and how costly or effective external advertising is. The strategic interactions of the platform and sellers further yield counterintuitive welfare consequences: when products are stronger substitutes, despite more intense price competition, sellers’ profit can increase while consumer surplus can decrease; and when sellers’ external advertising becomes less costly or more effective in attracting consumers, both sellers and consumers can be worse off.
Facilitating Progression, Preserving Gameplay: Booster Design in Video Games
Guangzhi Chen and Zheyin (Jane) Gu
In preparation for submission to Journal of Marketing Research
Accepted for presentation at Conference on Information Systems and Technology (CIST) 2026
Abstract
In single-player video games, developers often provide players with boosters, or consumable tools that can reduce their effort to clear a level, to sustain progression. Meanwhile, players’ lower-level play can facilitate their understanding of game design and structure, contributing to improved performance and enjoyment at a more advanced level. We study the developer’s optimal booster design, accounting for its impact on a player’s endogenous gameplay decisions. First, we show that a more powerful booster, when not too powerful, can motivate the player to extend gameplay at both the lower and advanced levels. This motivating impact is stronger under more efficient carryover of gameplay experience across levels. However, if the booster is already powerful, further enhancing its power discourages gameplay at both levels. Second, a developer that maximizes monetization opportunities from players’ gameplay time should offer a booster of moderate power. However, a strong tendency to profit from booster sales induces the developer to increase booster power, causing a reduction in players’ playtime. Third, the optimal booster power and price vary with player characteristics, suggesting the benefits of booster customization. Interestingly, the developer may offer a less powerful booster at a higher price to players perceiving a higher progression reward.
Publications
Personalized Pricing with Consumers’ Quality Uncertainty
Guangzhi Chen and Tianxin Zou (2026)
Production and Operations Management, published online.
Abstract
We examine a firm’s personalized pricing (PP) strategies in markets where consumers are uncertain about product quality. In such markets, prices serve not only as a tool for price discrimination but also as a means of conveying quality information to consumers. We reveal that a firm faces a tradeoff between adopting PP to better price discriminate among consumers and not adopting it to signal its high quality. We find that a high-quality firm should adopt PP only when its product quality is known to either a very small or a very large fraction of consumers, and when its high-quality product, on average, offers either very low or very high additional value to consumers relative to a low-quality product. Moreover, the high-quality firm may charge consumers personalized prices less than their willingness to pay to signal its quality in equilibrium, deviating from first-degree price discrimination when consumers are informed about quality. Counterintuitively, when more consumers know product quality or when the high-quality product provides higher average value to consumers, consumer surplus and social welfare may decrease, but a low-quality firm’s profit may increase. Furthermore, the firm’s profit can be lower when its personalized pricing leverages more information about consumer characteristics. Randomized experiments provide evidence that a personalized price is a weaker signal of objective product quality than a uniform price.
Information Sharing Motivated by Production Cost Reduction in a Supply Chain with Downstream Competition
Erbao Cao and Guangzhi Chen (2021)
Naval Research Logistics, 68(7), 898–907.
Work in Progress
AI-Generated Summary of Consumer Reviews
Guangzhi Chen, Baojun Jiang, and Tianxin Zou
AI Shopping Assistants and Product Recommendations
Guangzhi Chen and Zheyin (Jane) Gu
Teaching
Teaching Interests
Marketing Analytics; AI/ML in Marketing; Digital Marketing; Marketing Strategy
Instructor
Marketing Management, University of Florida
- Undergraduate course, Spring 2024
- Evaluation: 4.26/5, above college mean
Teaching Assistant
Art and Science of Pricing, University of Florida
- Master’s course, Fall 2021