Title: The Value of Product Visibility in Cultural Markets: Evidence from an Online Music Platform
Speaker:Zijing "Jimmy" Hu (Mays Business School, Texas A&M University)
Time: 14:30(Tuesday),November 3rd, 2026
Venue: Room713, Mingde Business Building (Zhongguancun Campus)
Language: Chinese & English
Abstract：
Cultural marketplaces host large, heterogeneous catalogs in which many products receive little attention despite being readily available and directly evaluable by consumers. We study activity-based visibility, a widely used but underexamined discovery design through which platforms turn consumers’ ongoing actions into visibility for the underlying products. We distinguish two forms of this design. Platform-wide visibility broadcasts recent activity across the marketplace, whereas peer-driven visibility transmits activity through social connections. Using high-frequency data from a direct-to-fan digital music marketplace, we develop and estimate a continuous-time structural model in which consumers first encounter artists and subsequently decide whether to purchase. Counterfactual analyses show that both visibility mechanisms expand demand rather than merely reallocating purchases across artists. Our analysis further shows that platform-wide visibility contributes more to aggregate sales, while peer-driven visibility more strongly disperses purchases across genres and limits market concentration. Both mechanisms improve consumer welfare primarily by directing attention toward more valuable consumer-artist matches rather than by increasing the total number of encounters. Finally, the two mechanisms exhibit limited substitutability, with each generating substantial additional benefits even when the other is present.
Short biography：
Zijing "Jimmy" Hu is a PhD candidate in Quantitative Marketing at Mays Business School, Texas A&M University. His research interests focus on product development, platform strategy, pricing, and market research methodology. He employs interdisciplinary methods in his projects, including deep learning, causal inference, structural modeling, reinforcement learning, and game theory.