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Scale-up phase in deeptech start-ups: Replication or massive learnings?

Abstract : Because of the possible response to main, current and global issues, a particular attention is paid to deeptech start-ups and their growth mechanisms. Nevertheless, first observations on technological start-ups point out a limited growth. As deeptech start-ups are developing by nature advanced technologies, they are intended to be deployed on different markets, revealing technological genericity. Scaling these technologies encounters unfortunately some hurdles and seems to be more complex. This article focuses on scale-up for deeptech startups and on means to achieve this development phase. Literature usually considers scale-up as a phase of business model replication, suggesting low learnings. On the contrary, our hypothesis is to regard scale-up as a more complex phase in deeptech start-ups development, through additional means and learnings that have to be determined. This research is based on 8 case studies from different fields: For each start-up, we study what should be learnt and what should be relevant design strategies to ensure scale-up. Main issue in scale-up phase appears to prove that most of activities will not change, that should refer to the concept of creation heritage, taking into account external interactions.
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Contributor : Louise Taupin Connect in order to contact the contributor
Submitted on : Monday, October 18, 2021 - 4:59:47 PM
Last modification on : Wednesday, November 17, 2021 - 3:42:02 PM
Long-term archiving on: : Wednesday, January 19, 2022 - 9:36:17 PM


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  • HAL Id : hal-03383828, version 1


Louise Taupin, Pascal Le Masson, Blanche Segrestin. Scale-up phase in deeptech start-ups: Replication or massive learnings?. R&D Management Conference, Jul 2021, Online, United Kingdom. ⟨hal-03383828⟩



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