Marketing Seminar（2018-23）

Topic: Does Fast Fashion Increase the Demand for Premium Brands? A Structural Analysis

Speaker: Zijun Shi, Tepper School of Business, Carnegie Mellon University

Time: Friday, 23 November, 14:00-15:30

Location: Room 217, Guanghua Building 2

Abstract:

This Erosion of high-end fashion brands by fast fashion copycats (e.g., Zara, Forever 21) has raised attention from industry insiders and policymakers, demonstrated by unceasing legal attempts to try to get fashion designs covered by copyright. This paper develops a dynamic structural model of fashion choices of brands and styles to investigate the implication of prohibiting fashion copycats, with the help of user-generated data from fashion-specific social media and deep learning methods on image analytics.

We find that copycats can enhance high-end brands demand, contrary to conventional wisdom, due to several novel mechanisms: first, the affordability of mixing low-end copycats with high-end brands boosts demand for high-end brands from financially constrained consumers; second, good styles from low-end brands can help a consumer to build up his popularity/likeability, which increases his value for high-end brands and reduces the cost.

Substantively, our results shed light on copyright enforcement and have implications on how fashion brands should react to copycats. Methodically, we developed a framework to analyze consumer choices where visual features are important product attributes and peer feedback hugely affects the decision-making process.

Introduction:

Zijun (June) Shi is a Ph.D. candidate in Marketing at Tepper School of Business, Carnegie Mellon University. She is a quantitative marketing researcher and conducts both empirical and theoretical studies on digital and technology-driven marketing. Her recent works examine substantively important problems in the fashion market, healthcare, social media, and E-commerce. She uses interdisciplinary methodologies according to the nature of the problem, including structural modeling, machine learning, game theory, econometrics, computer vision, and natural language processing.

Your participation is warmly welcomed!