About
A collaborative research project led by Florence Honoré from the Wisconsin School of Business, Shinjinee Chattopadhyay from Gies College of Business, and Shinjae Won from the School of Labor and Employment Relations, University of Illinois at Urbana-Champaign, explores the unique dynamics of academic entrepreneurs in the AI startup landscape. Their study investigates how the academic background of founders affects the market opportunities pursued by their startups, offering valuable insights for entrepreneurs, investors, and policymakers.
Challenge
The researchers’ approach aimed to understand the distinctive contributions of academic entrepreneurs in shaping their ventures’ growth trajectories and funding opportunities.To analyze the correlation between founders’ prior experiences in academia or industry and the number of market opportunities their startups pursued, the researchers needed to access and collect publicly available data from both Crunchbase and LinkedIn websites. They compiled a list of approximately 2,200 U.S – based startups founded by both teams and solo entrepreneurs. By utilizing the structured and focused datasets, the researchers were able to focus their efforts on analyzing the data instead of collecting and organizing it.
Bright Data provided us a reliable and efficient way to identify startups founders’ work history. We used this data to differentiate between academic and industry experiences. Having quick access to this data saved us numerous hours and allowed us to focus on the analysis and writing instead
Professor Florence Honoré, Wisconsin School of Business
Impact
Artificial intelligence has radical implications for our society and day-to-day life. It affects how we work, communicate, and the way we live our lives.
The implications of findings from this research are numerous. Firstly, it highlights the significant role of academic founders in the AI sector, guiding team composition in new ventures. Secondly, it offers insights into the different growth paths and market strategies AI startups adopt, and how these choices influence their funding opportunities. Additionally, the study has implications for investors, offering a deeper understanding of the strengths and weaknesses associated with the diverse professional backgrounds of startup founders. For policymakers, the research presents considerations regarding the support of academic research and the promotion of mobility between academia and industry. Lastly, it addresses the choices startups make in targeting multiple markets versus focusing on specific scientific challenges, underscoring the trade-offs and opportunities within the AI sector.
The outcome of this research will provide valuable insights for entrepreneurs, managers, and policymakers on fostering innovation in both new and established companies. Studies such as this research are crucial in learning more about the implications of the AI revolution and how we could leverage the power of public web data to help address our society’s everyday challenges and keep pushing technological innovations forward.
