Full project title: The Science of Startups Initiative (SOSI) - Understanding the determinants of success in entrepreneurship, startups and innovation ecosystems
This project aims to reveal the determinants of success in entrepreneurship, startups and innovation ecosystems using data science and qualitative research methods.
Startup success is linked to a combination of factors at the market, firm, and founder levels. Here, we aim to disentangle the various determinants of success, with a focus on the founding team, their previous experiences and failures, and their personalities.
In our current research, we focus on uncovering the extent to which the diversity of personalities in a team relates to their performance, how this influences venture capitalists’ investment decisions, and how it drives ultimate startup success.
Currently, the project employs a three-stage approach:
Lying at the nexus between computational social science, data science, psychology and economics, this project aims to disentangle the diverse factors that influence success in entrepreneurship with a focus on personality characteristics. The insights gained by the project inform (1) individual decision-making in engaging in entrepreneurship, (2) market definition of best-fit for success, (3) team composition in innovation ecosystems, and (4) societal perception of entrepreneurship.
Modelling the determinants of success can help entrepreneurs (whether existent or aspiring), founders, investors, and tech policymakers alike understand the quantifiable characteristics and practical actions needed to nurture individual and collective success as well as innovation in tech ecosystems.
We aim to disseminate the insights gained by the research activities of the Science of Startup Initiative widely within and beyond academia. As part of this mission, we present research findings at universities, scientific conferences, industry- and policy events, and we engage with media to inform and educate about the determinants of startup success and the role of personality diversity within it.
Events, presentations and project news will be announced on LinkedIn
The quantification of the success determinants of startup ecosystems relies critically on the availability of large-scale and system-wide data to measure and disentangle the various influential factors in an empirical way.
At the same time, our research benefits from the experience and knowledge shared by experts in the industry. Therefore, we want to thank all individuals and stakeholders from the industry for advice, insights, and support of our Initiative.
In particular, we are grateful to Campus Founders for the ongoing collaboration, to Crunchbase for the provision of data, as well as to BuiltWith, Influx, TeamSlatts, and AirTree Ventures for insights about startups, founders and venture investments.
This research was supported by funding from the Oxford Internet Institute’s Research Programme on AI & Work, funded by the Dieter Schwarz Stiftung gGmbH.
Departmental Research Lecturer
Project role: Principal Investigator
Dr Fabian Braesemann is a Departmental Research Lecturer in AI & Work at the OII.
UNSW Sydney
Project role: Co-Principal Investigator
Professor Paul X. McCarthy is a data science leader and entrepreneur with over 25 years of experience in business, technology, and research. He is the CEO and co-founder of League of Scholars.
MSc Student
Project role: Researcher
Marieth is a student in Social Data Science who completed her BSc in Data Theory at UCLA. Her research interests include understanding what factors affect social support as well as depolarizing opinion networks.
MSc Student
Project role: Researcher
Coral is a strategic marketer in the fintech space pursuing a part-time MSc at the OII. Building on her industry experience, her research applies a social science lens to the study of fintech, eCommerce, and the platform economy.
University of Technology Sydney
Project role: Researcher
Xian Gong is a PhD candidate in Data Analytics at the University of Technology Sydney, researching inferred personality, firm performance and emerging technologies through large language models.
Technical University of Berlin
Project role: Research Student
Sophia is an economics student at the Technical University of Berlin, researching social networks and startup economics. As a PR consultant, she works closely with family businesses and startups.
With Dr Fabian Braesemann
Here, we show that founder personality traits are a significant feature of a firm’s ultimate success. We draw upon detailed data about the success of a large-scale global sample of startups.
22 January 2024
Departmental Research Lecturer Dr Fabian Braesemann visited Germany to present his latest research on the power of personality in shaping the success of startups and beyond.
17 October 2023
New research shows start-up founders have distinct personality traits which are more important to the success of their companies than previously thought.
Forbes, 09 January 2024
A new study used a multifactor approach to analyze the success of over 21,000 startups worldwide and uncover what factors were most important in their success.
Fast Company, 19 October 2023
A new study published in ‘Scientific Reports’ claims the key to success lies in six founder types.
Entrepreneur, 27 November 2023
A study found that founders who possess a mix of grit, curiosity, conscientiousness, and high emotional intelligence were more likely to propel their startups towards success.
>We are committed to maintaining the highest ethical standards in research with human participants throughout the project. Accordingly, the research project is covered by a Research Ethics Form (CUREC 1A, Ref No: SSH_OII_CIA_22_022) approved by the Oxford Internet Institute’s Departmental Research Ethics Committee (DREC) in accordance with Oxford University’s procedures for ethical approval of all research involving human participants.
Ensuring anonymity of personal information is of utmost importance to SOSI. As such, for the data publicly released (see below), we use differential privacy to ensure individual founders cannot be linked to companies. Additionally, we use statistical privacy techniques such as k-anonymity to ensure companies cannot be identified from their characteristics. Depending on whether companies or individuals are repeated in the data, we used a hashing algorithm and random numerical labelling to anonymise individual and company names.
The code and (anonymised) data to replicate the main findings of the study ‘The impact of founder personalities on startup success’ are available on GitHub.
