How Do Fast Fashion Copycats Affect the Popularity of Premium Brands? Evidence from Social Media

Abstract

The erosion of high-end fashion brands by fast fashion copycats (e.g., Zara, H&M) has provoked ongoing controversies and unceasing legal attempts to copyright fashion designs. Despite this tension around the enforcement of copyrights and the purported negative impact of copycats, the effect of fashion copycats on high-end brands remains empirically unclear. Research on this topic has been impeded by the absence of a modeling framework to quantify fashion and by the lack of consumer-level data on fashion choices. We collect data on the posting behaviors of consumers on a fashion-specific social media platform and develop a dynamic structural model with deep learning image analytics to characterize consumers’ choices of brands and styles. Results suggest that fast fashion copycats can both harm high-end brands (a cannibalization effect) and help high-end brands (a market expansion effect). We also identify both static and dynamic mechanisms that contribute to the market expansion effect: The affordability of mixing copycats with high-end brands boosts the number of high-end items featured in posts by financially-constrained consumers (a static mechanism). Also, good styles from copycats enable users to build their popularity on social media over time, which may increase the user’s valuation of high-end brands and reduce the user’s future costs via sponsorship opportunities (dynamic mechanisms). Our results could inform policymakers about the potential consequences of prohibiting fashion copycats.

Keywords: Copyright Policy, Fashion Analytics, Social Media, Computer Vision, Deep Learning