From Concept to Commercialization: Understanding Deep-Tech Startups

• Applicant: Professor Dr. David Bendig
• Subject Area: Operations Management and Computer Science for Business Administration ; Economic Policy, Applied Economics
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 531021081

Deep-tech startups are gaining major public interest. However, despite the surge in interest and importance of deep-tech startups, as well as the significant private and public funding devoted to them, they have not garnered much attention in management research. We argue that this is a shortcoming because deep-tech startups exhibit distinctive characteristics that differentiate them from more mundane types of technology startups typically studied. These characteristics give rise to specific norms and dynamics, particularly concerning the marketability of their solutions, which are currently poorly understood and warrant specific attention. To address this gap, we have devised a thorough research plan consisting of three work packages (WPs). WP1 will conceptualize the epistemic object "deep-tech startup" as a distinct form of a technology startup and clarify ist boundaries. The central research question (RQ) of this study is: How can the deep-tech startup be conceptually demarcated? To answer this RQ, we will use a mixed-methods approach, starting with a quantitative approach employing text mining for topic modeling to inductively identify and map the defining attributes of deep-tech startups from different sources. Next, we will leverage deep-tech experts to validate and expand upon the findings to create a well-grounded conceptualization of deep-tech startups. WP2 will aim to establish a proficient approach to product-market fit (PMF) catering to the unique requirements of deep-tech startups. The RQ for this study is: How can PMF for deep-tech startups be effectively identified? We will use a mixed-methods approach incorporating qualitative interviews with industry experts and deep-tech startups as well as a large-scale survey to validate findings and gather data on factors related to PMF. In doing so, we seek to provide a validated model for identifying PMF in deep-tech startups that, unlike existing paradigms, is optimized to address their distinctive dynamics. WP3 will focus on the role of team composition and organizational design professionalization in achieving PMF in deep-tech startups. The central RQ of this WP is: How can the composition of venture teams be effectively designed to infuse business acumen into technology-focused deep-tech startup teams across a venture lifecycle? To answer this RQ, we propose two studies: a qualitative study exploring the emergence of deep-tech venture teams and potential biases when building teams and a quantitative study applying econometric methods to analyze deep-tech venture team composition in an extensive linked employer-employee database. The results will provide insights into the impact of venture team structure on market success and inform founders and investors on how to effectively balance business and technical backgrounds in deep-tech teams. Overall, we expect the results of this study to carry important implications and lay the groundwork for future research on deep-tech startups.

• DFG Programme: Research Grants
• International Connection: Switzerland
• Cooperation Partners: Professor Dr. Martin Murmann; Professor Dr. Stefan Raff