Flying high or crashing down: Pre-entry knowledge, post-entry learning, and the distribution of startup performance

Abstract

Research Summary: We examine variation in high-technology startups' performance based on founders' pre-entry experiences by developing a formal model and using confidential employee-employer linked microdata from the United States to examine the empirical consistency of the model propositions. The model posits that relative to insiders, a lack of industry-specific experience creates greater epistemic uncertainty regarding optimal business models at time of entry for outsiders and thus, higher post-entry adjustment costs associated with necessary pivots. Consequently, outsiders have a higher selection threshold for the value-creation potential of the underlying technical ideas. Together, these mechanisms yield propositions that relative to insiders, outsiders have lower odds of survival on average, but higher growth and probability of being acquired. The empirical results indicate strong and robust support for these propositions. Managerial Summary: Our paper showcases that individuals contemplating entrepreneurial opportunities outside their industry of employment face higher uncertainty in configuring their business model at time of entry relative to those with industry-specific experience. This results in higher adjustment costs for implementing pivots resulting from post-entry learning and a higher likelihood that they will terminate operations. To offset these higher risks, individuals venture outside their industry only if their technical ideas have higher value-creation potential. This implies that outsider startups are more likely to exit (including through acquisitions), but if they survive, they will experience higher growth relative to insider startups. We provide empirical evidence in support of these propositions.