I am a fifth-year Ph.D. in Strategy and Business Economics, SC Johnson Graduate School of Management & Economics Department, Cornell University.
My research interests are industrial organization and quantitative marketing. Specifically, I am interested in platform, online retailing, recommendation systems and rating algorithms. I use machine learning, reinforcement learning, casual inference, structural estimation, and game theory model for my research.
Hello! I’m Si ZUO 左思
How to say my name? See Zoo-oh
Research
Working Paper & Work in Progress
Price Signaling and Reputation Building: Evidence from a Service Platform, with Yangguang Huang (HKUST) and Chenyang Li (HKUST Guangzhou) (new version updated!)
Covered by South China Morning Post.
Abstract:
To build a reputation on online platforms, new firms need to accumulate reviews through sales and consider the corresponding pricing strategy. We construct a dynamic model with both price signaling and a review-based reputation system. A high-quality firm can signal its unobserved quality by setting a lower introductory price than that of a low-quality firm because the high-quality firm benefits more from accumulating reviews in early periods. Using data from Zaihang, a service platform, we find empirical evidence that experts with high unobserved ability indeed adopt low introductory prices. We use an expert's performance on another platform as an instrument for the expert's ability to provide evidence for the causal relationship. The price and sales dynamics in the data are also consistent with the model predictions. The platform can accelerate quality revelation by facilitating price signaling. To do so, platforms could make price comparison easier and provide training to new firms about signaling.
Personalized Algorithms and the Virtue of Learning Things the Hard Way, with Omid Rafieian (Cornell)
Abstract:
Recommendation systems are now an integral part of the digital ecosystem. However, the increased dependence of users on recommendation systems has heightened concerns among consumer protection advocates and regulators. Past studies have documented various threats personalization algorithms pose to different aspects of consumer welfare, through violating consumer privacy, unfair allocation of resources, or creating filter bubbles that can lead to increased political polarization. In this work, we bring a consumer learning perspective to this problem and examine whether personalized recommendation systems hinder consumers' ability to learn their own preferences. We develop a utility framework where consumers learn their preference parameters in the presence of a recommendation system. We introduce a notion of regret which is defined as the regret when consumers make decisions on their own. We theoretically show that the presence of the recommendation system acts as a barrier to consumer learning. We then empirically investigate this phenomenon using the MovieLens data. Finally, we discuss a variety of consumer protection policies that help improve consumer learning and document the welfare implications of each.
Stores Going Online: Market Expansion or Cannibalization? , with Yangguang Huang (HKUST) and Chenyang Li (HKUST Guangzhou)
Abstract:
With the continual growth of e-commerce, many brands have opened up online sales channel alongside with their traditional brick-and-mortar (B&M) stores. Consumers usually incur lower shopping costs from purchasing online, so the presence of an online store tend to cannibalize sales of the corresponding B&M store. However, online sales may expand the market for the B&M store by increasing consumer awareness of the brand and transmitting product information. We use a unique dataset of 308 B&M stores matched with their online stores on Taobao to investigate the two counterveiling effects. We utilize rainy days and Covid outbreaks as offline-exclusive demand shocks to identify the (negative) cannibalization effect of online sales on B&M stores. We use Taobao live streaming and Double-11 shopping festival as online-exclusive demand shocks to identify the (positive) informative effect. Our findings reveal that categories of home, clothing, cosmetics, and jewelry suffer the most from the opening of online stores, while amusement and personal care stores are not affected. We also find that local stores experience both large negative and small positive effects. Based on survey data, we find the discounted price difference, online store quality and consumer online shopping habits are the main mechanisms behind these heterogeneous results. Our study unveil the complex relationship between online and offline sales and offer insights into the strategies and operations of store managers and shopping malls in the digital age.
Fair Rating on Online Platforms
Teaching
Instructor
NBA 6955 Industrial Organization, Consulting and Business Strategy, Fall 2022
MBA Elective Course (also open for Graduates), Course Designer, and Lead Instructor. Evaluation 4.4/5.
SC Johnson Graduate School of Management, Cornell University.
Marketing Management, Fall 2023
Undergraduate Businese Minor, Lead Instructor. Evaluation 4.7/5.
SC Johnson Graduate School of Management, Cornell University.
Teaching Assistant
MBA Courses
AI for Marketing Strategy (with Lab Sessions), MBA Elective Course, for Prof. Emaad Manzoor, SC Johnson, Cornell, Spring 2023
Data Analysis and Modeling (with Sessions), MBA Core Course, for Prof. Omid Rafieian, SC Johnson, Cornell, Summer 2022
Microeconomics for Management, MBA Core Course, for Prof. Yi Chen & Prof. Michael Waldman, SC Johnson, Cornell, Summer 2021 & Fall 2020
Strategy, Cornell-Tsinghua Finance MBA Core Course, for Prof. Thomas Jungbauer, SC Johnson, Cornell, Winter 2021 & Spring 2021
Ph.D. Courses
Applied Microeconomics II: Game Theory, Ph.D. Core Course, for Prof. Michael Waldman, Dyson School of Applied Economics and Management, Cornell, Spring 2022
Microeconomics Theory I (with Sessions), Ph.D. Core Course, for Prof. David Easley, Economics Department, Cornell, Fall 2021