Guangzhi Chen
Guangzhi Chen
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 study strategic interactions between firms and consumers in digital markets, in which I seek to identify crucial mechanisms that meaningfully shape market decisions and outcomes, advancing our understanding of important marketing problems. My current work focuses on e-commerce marketplace design, technology adoption (e.g., personalized pricing, artificial intelligence), and digital product monetization. In addition, I am interested in empirical modeling and data-driven approaches.
Please feel free to reach out at guangzhi.chen@ufl.edu.
Research Interests
Online platforms 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
Under review 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
Under review at Marketing Science
Abstract
In single-player, multi-level video games, sustaining player progression is central to retention and long-run monetization. To facilitate progression, developers often provide players with boosters, consumable tools that can reduce their effort to clear a level. In this paper, we study a developer’s optimal booster design, accounting for its impact on a player’s gameplay across levels. In a model with two game levels where a booster is offered to help the player clear the first level and move forward to the second level, we show that increased booster power affects the player’s total gameplay time non-monotonically. In particular, as progression aid strengthens, the player’s playtime initially rises and then falls, indicating that a developer monetizing through playtime should offer a moderately powerful booster. However, when the developer has a strong incentive to profit from booster sales, a sales–time tension emerges: the developer optimally increases the booster power and price to extract player surplus, although doing so can compress gameplay. When this incentive becomes very strong, a powerful booster can even reduce the player’s total playtime relative to when no booster is offered. In addition, we show that the optimal booster power and price vary with players’ perceived value of level progression, suggesting the benefits of booster customization. Interestingly, for a player with higher valuation for progression, the developer may offer a booster with lower power but charge a higher price.
Publications
Personalized Pricing with Consumers’ Quality Uncertainty
Guangzhi Chen and Tianxin Zou
Conditionally accepted at Production and Operations Management
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, and Marketing Strategy.
Instructor
Marketing Management, University of Florida
Undergraduate course, Spring 2024
Evaluation: 4.3/5, above college mean.
Teaching Assistant
Art and Science of Pricing, University of Florida
Master’s course, Fall 2021.