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• Ms. Zijun (June) Shi
  Ph.D. Candidate in Industrial Administration (Marketing)
  Tepper School of Business
  Carnegie Mellon University

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 highend 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.