Do accelerators promote the growth of startups? Analysing the effectiveness of startup accelerators through the lens of big data

ABSTRACT

Startup accelerators have emerged as a prominent mechanism for supporting early-stage ventures, yet questions persist regarding their effectiveness. This study addresses the gap in understanding by investigating whether accelerators promote the growth of all startups or only those already successful. Leveraging web-search traffic data as a proxy for startup growth, trend analysis is used to assess the impact of acceleration. Using a sample of 103 startups from Y Combinator from 2017 and 2018, the analysis reveals that accelerators aid startups regardless of their pre-acceleration growth status (i.e. growing, stagnating or declining). Furthermore, non-growing startups selected for acceleration exhibit better chances of achieving growth post-acceleration compared to already growing counterparts. These findings add important insights into the understanding of accelerators’ effectiveness, demonstrating that accelerators work for all selected startups, not only for those already successful. Our findings offer two important practical implications: policymakers and stakeholders should continue supporting accelerators due to their positive impact on startup growth and reducing uncertainty; startups should focus on their future potential and vision when applying for an acceleration program. Using the introduced approach, future research could compare the effectiveness of accelerators offering offline and online programs, targeting specific industry sectors, and across different periods.

KEYWORDS:

• Accelerator
• effectiveness
• startup
• growth

Acknowledgments

The authors are thankful to Dzhamala Askerova for assistance in data analysis.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

Data will be made available on reasonable request

Additional information

Funding

This research was partially conducted under Research Project No. 2022.011P (OIDIGITAL), funded by the Graduate School of Business at HSE University as part of their 2022–2024 research program. The recipient of this support is Zeljko Tekic; Graduate School of Business, HSE University, Moscow, Russia.

Notes on contributors

Zeljko Tekic

Zeljko Tekic, Ph.D. is a Professor of Innovation and Entrepreneurship at the Graduate School of Business, HSE University, Moscow. His research interest evolves around topics of startups, open innovation and AI, and development of tools and methodologies for understanding them. Zeljko earned his Ph.D. in Engineering Management at the University of Novi Sad (Serbia). He was a postdoctoral scholar at the Fraunhofer Institute for Industrial Engineering in Stuttgart and at Free University Berlin, and a visiting professor at MIT.

Alena Hrynkevich

Alena (Lena) Hrynkevich is a master’s student at HEC Paris (International Business). She holds a bachelor’s degree in Business Analytics & Data Science from the Graduate School of Business, HSE University, Moscow. She was an exchange student at the University of St. Galen (Switzerland). Alena’s research interests include big data analytics and innovation management.

Maksim Malyy

Maksim Malyy, Ph.D. is a Researcher in the iDEAlab of the Creative Educational Center at Novi Sad, Serbia. He received his Ph.D. degree in the Engineering Systems from the Skolkovo Institute of Science and Technology (Skoltech), Moscow, Russia. His field of research includes the evolution of new ventures, its analytical description, and the direct use in managerial applications for decision-making.