Abstract
Entering new markets is crucial for technology startups to scale, yet these ventures often face high uncertainty about demand in these markets. This study examines how the composition of initial users shapes startups’ new market growth amid such uncertainty. It theorizes that startups face a learning tradeoff when targeting a foreign market: Local initial users, who are more familiar to the startups, provide clearer signals due to shared language and norms; however, more representative foreign users provide more transferable insights about the target market. Using variation in feature timing on a product platform, this study finds that startups with a higher share of local initial users prior to platform launch experience greater growth in foreign users afterward. This effect is stronger when startups are based in more linguistically homogeneous countries and operate in globally standardized digital product categories, settings where the clarity benefits of local signals outweigh their transferability costs. In contrast to prior work suggesting that startups should begin with target users, this study reveals contexts when more familiar initial users can better foster new market growth and prevent premature scaling.