The Accelerator Conundrum: Academic Research

Academic research on startup accelerators has grown significantly since the emergence of the model in the mid-2000s, with scholars seeking to understand their effectiveness, design principles, and impact on entrepreneurial ecosystems. The research largely focuses on understanding what makes accelerators successful, for whom, and under what conditions.

Research, however, suffers from the industry-bias of equating Accelerators with 3-month, Equity-based Accelerators. 

Here’s a summary of key areas of research and prominent researchers:

Key Areas of Academic Research

1. Defining and Differentiating Accelerators:
   • Researchers have worked to establish a clear definition of accelerators, distinguishing them from incubators, angel investors, and other early-stage support mechanisms. Key characteristics often highlighted include: fixed-term, cohort-based, mentorship-driven, culminating in a demo day, and typically involving a small equity investment for seed capital.
   • Focus: Clarifying the unique features and value proposition of accelerators.

2. Impact and Effectiveness of Accelerators:
   • A central question is whether accelerators truly “accelerate” startup success. Studies often compare accelerator graduates to similar companies that did not participate in a program.
   • Findings: Research generally suggests that participation in top-tier accelerators can lead to positive outcomes, such as faster time to raising venture capital, securing more funding, higher survival rates, increased online traffic, and more employee hires. However, these positive effects are not universal across all programs, and the impact can diminish when looking at a broader sample of accelerators. Some studies even suggest ambiguous or negative impacts from lower-performing programs.
   • Metrics: Researchers use various metrics to assess impact, including fundraising amounts, valuation, acquisition rates, survival rates, revenue growth, and employment growth.

3. Accelerator Design and Best Practices:
   • This area explores what specific design elements contribute to an accelerator’s success.
   • Key factors identified:
     • Mentorship: The quality and how mentorship interactions are structured (e.g., concentrated upfront, encouraging frequent meetings with mentors and customers) are crucial.
     • Peer Learning: Accelerators that foster transparency and encourage interaction and mutual support among cohort members tend to perform better.
     • Selection Process: The ability to select promising startups is paramount.
     • Cohort Size: Some research suggests smaller cohorts lead to stronger performance.
     • Sponsorship: Programs sponsored by investors or universities tend to raise more money than those sponsored by governments or corporations (though this can vary by region and specific goals).
     • Founding Team Background: Programs run by former entrepreneurs versus former investors or government employees can correlate with different outcomes (e.g., lower valuations for founder-led programs).
   • Focus: Identifying the causal mechanisms through which accelerators add value.

4. Role in Entrepreneurial Ecosystems:
   • Studies examine how accelerators contribute to the broader entrepreneurial ecosystem of a region.
   • Findings: Accelerators can positively impact regional entrepreneurial finance by attracting capital and increasing the number of investors. This “spillover” effect can benefit not only participating startups but also other companies in the area.
   • Focus: Understanding the macro-level effects of accelerators beyond individual startup success.

5. Types and Evolution of Accelerators:
   • Research also categorizes different types of accelerators (e.g., corporate, government-backed, university-affiliated, private) and analyzes how their models and objectives vary. The evolution of the accelerator model, including the rise of virtual and specialized programs, is also a topic of study.

Prominent Researchers and Institutions

Several academics have made significant contributions to the understanding of startup accelerators:

• Susan Cohen (University of Richmond / University of Georgia): A leading scholar in the field. Her work, often in collaboration with others, has been instrumental in defining accelerators and analyzing their design and impact. She has explored how accelerators differ from incubators and angels, and what specific design elements (like mentorship interaction and transparency) contribute to their effectiveness.
• Yael V. Hochberg (Rice University / MIT): Known for her extensive research on entrepreneurial finance and accelerators. She has co-authored influential papers examining the impact of accelerators on venture performance and regional venture capital supply.
• Benjamin L. Hallen (University of Washington): Collaborated with Cohen and others on early, foundational studies investigating whether accelerators truly “accelerate” startups and what drives their effectiveness.
• Christopher Bingham (University of North Carolina – Kenan-Flagler Business School): Has researched accelerator design, particularly how accelerator structures can mitigate “bounded rationality” in founders.
• Valentina Assenova (Wharton School, University of Pennsylvania): Her recent work (often with Raffi Amit) has brought fresh insights into the performance of accelerated startups, using larger datasets to show that accelerators generally lead to positive outcomes in terms of funding, revenue, and employment, and that these benefits extend beyond high-tech hubs.
• Raffi Amit (Wharton School, University of Pennsylvania): Co-authored recent studies with Assenova on the impact of accelerators across a diverse global sample.
• Juanita Gonzalez-Uribe (London School of Economics) & Michael Leatherbee (Pontificia Universidad Católica de Chile): Their work on “Start-Up Chile” provides important empirical evidence on the effects of government-backed, non-equity accelerators on venture performance.
• Daniel Fehder (University of Southern California): His research with Yael Hochberg has focused on the impact of accelerators on the regional supply of venture capital.

Key Research Outputs / Databases:

• Seed-DB: While not an academic publication itself, it’s a well-known and widely used database that has served as a data source for many academic studies on accelerators. (Note: The reliability and authority of open-source data like Seed-DB are often a point of discussion in academic research).
• Global Accelerator Learning Initiative (GALI): A partnership between the Aspen Network of Development Entrepreneurs (ANDE) and Emory University, GALI aims to collect and analyze data on accelerator programs globally, contributing to a better understanding of what works in startup acceleration, particularly in emerging markets.
• Various academic journals in entrepreneurship, finance, and management (e.g., Research Policy, Administrative Science Quarterly, Organization Science, The Review of Financial Studies) frequently publish research on startup accelerators.

In summary, academic research on startup accelerators has moved from defining the phenomenon to rigorously assessing its impact and identifying the critical design elements that drive success. While positive effects are often found, particularly for top-tier programs, researchers continue to explore the nuances of accelerator effectiveness across different contexts and for various types of ventures.

Future research on accelerator effectiveness needs to take into account models like 1Mby1M that has categorically diverged from the 3-month, equity-based accelerator model and has created significant global impact with an equity-free, virtual model and a heavy emphasis on education and mentoring, deemphasizing fund raising and premature blitzscaling.

Photo Credit: Foundry Co from Pixabay

This segment is a part in the series : The Accelerator Conundrum