Research
Entrepreneurial success depends on reducing uncertainty about the quality of ideas and selecting effective strategies to bring the idea to market. Mentorship plays a critical role in this process. In this paper, I examine how mentorship improves entrepreneurial outcomes within the Creative Destruction Lab (CDL), a global mentorship-driven startup accelerator, through two channels: the direct effect of improving startup quality and the screening effect of identifying high-quality startups. Using mentorship interaction data from CDL, I apply machine learning algorithms to generate quantifiable measures of mentors' advice. I propose and estimate a structural model of mentorship, where the dynamics of quality accumulation are influenced by both the direct effect of mentors' advice and the screening effect from mentors' learning. I find that mentorship generates value through both direct and screening effects, with significant spillovers of quality signals between mentors. This model enables a counterfactual analysis, quantifying the value added by mentors when they actively shape the strategic direction of startups, compared to a more passive role where they support the execution of the entrepreneurs' original plans. The counterfactual analysis shows that entrepreneurs benefit from mentors' strategic guidance, with significant heterogeneity across sectors. In emerging sectors like quantum, mentors' strategic input has minimal impact, especially early on, suggesting that a more passive mentorship approach may be more beneficial. In these sectors, screening gains grow over time as mentors accumulate information and provide guidance that better reflects the true quality of the startups. These results offer important managerial implications for the design of intermediaries, such as accelerators that provide mentorship, suggesting that guidance approaches should be tailored to the specific needs and developmental stages of each sector.
Learning about product demand through Crowdfunding
Do entrepreneurs use crowdfunding to learn about the demand for their entrepreneurial product? Crowdfunding is not only a financial tool for entrepreneurs but also a way to run experiments and gather information about the quality of their idea to get marketing benefits. In this research, a model of Bayesian learning is presented, where entrepreneurs update their beliefs about the demand of their entrepreneurial product based on the signal from the sales of their crowdfunding campaign. This paper focuses on the pricing decision of entrepreneurs in an oligopoly environment, where uncertainty about demand parameters exists. The choice of price can provide varying levels of information about these parameters, with higher prices revealing more information about the slope of the demand curve. Entrepreneurs must weigh the trade-off between current profits and gaining knowledge about the demand for their product, which can lead to increased future profits. Using data from Kickstarter, this research examines the extent to which entrepreneurs use crowdfunding for experimentation. The results show that less experienced entrepreneurs tend to set higher prices, which is consistent with higher learning motives, while more experienced entrepreneurs offer discounts to take advantage of the marketing opportunities provided by crowdfunding platforms. Additionally, entrepreneurs with more innovative and novel products tend to have more concerns for market demand learning relative to marketing benefits.