Research

Supported by Will Mitchell Dissertation Research Award, Strategy Research Foundation, Strategic Management Society

Entrepreneurs often engage with mentors to seek guidance, gain industry insights, and create connections with potential customers and stakeholders. While mentors play a significant role in the success of early-stage startups, some founders, particularly women, may often face challenges in connecting with desired mentors. I propose that, due to the preference for women mentors and the lack of supply to meet the preferences, women founders have limited access to mentors who can provide more relevant industry expertise.  My analysis of state-wide venture competition supports this argument, and I find that women entrepreneurs often prefer women mentors with less industry expertise as much as they prefer men mentors with greater industry expertise. This result contrasts with men founders, who primarily prioritize mentors’ relevant industry expertise over gender. As a result of this preference from women founders, the general demand for expert mentors by all founders, and the limited supply of women expert mentors, women entrepreneurs are not frequently matched with preferred mentors. This result is concerning since it implies that the advantages women founders receive from having same-gender mentors often come at the cost of having a preferred mentor with industry expertise. Further analysis suggests that matching with a preferred expert mentor is associated with an increased likelihood of startup survival. For women-led startups specifically, securing preferred women mentors is beneficial in continuing the business, implying that the challenges women face in obtaining these mentors put them at a disadvantage. Additionally, supply-side mechanism testing shows that a greater shortage of women mentors reduces the likelihood of women-led startups being matched with their preferred expert mentors, implying that increasing the supply of women mentors could play a significant role in remedying the current gender disparity in founders' receiving preferred and effective mentorship. Moreover, findings on demand-side mechanism testing indicate that founders consider a mentor's gender as an indicator of the expected quality of the mentor, and inexperienced women founders are more likely to prefer same-gender mentors compared to experienced founders, suggesting that interpersonal support under uncertainty may be the driver of founders’ preference for same-gender mentors. This research provides key insights for developing more effective support initiatives for women founders. 

In early-stage ventures, founders’ knowledge and expertise are critical to survival, yet acquiring new capabilities is often difficult under conditions of uncertainty. Accelerators help fill these gaps, not only through mentorship and resources but also by fostering peer-to-peer learning within cohorts. While prior research highlights the overall benefits of accelerators, less is known about whether cohort knowledge complements or substitutes for the founding team’s expertise. We examine this question using data from 64 cohorts across 13 accelerators between 2005 and 2013. Our findings reveal that cohort entrepreneurial knowledge consistently improves startups’ fundraising outcomes, regardless of the focal team’s prior expertise. By contrast, managerial knowledge in the cohort enhances performance only when it complements, rather than duplicates, the founding team’s knowledge. These results highlight the dual role of accelerators as structured programs and peer-learning environments, and offer insights into how accelerators can optimize learning environments to support entrepreneurial success. 

• “Board of Advisory Wanted: Mentor Team Composition and Startup Performance” (Data analysis)

• “Value in Trade-off in Founders’ Mentor Search :  Mechanism testing” (Experiment ideation stage)

• “Accelerators and Women Founders” with Sandy Yu (Data analysis)

• “Social Inequality, Resource Accessibility, and R&D Startups” with Haiyue Jiang (Data analysis) 

• “Predicting risky investments and collaboration networks” (Pilot experiment data analysis)