Jana Thiel and Bart Clarysse
Starting on the premise that the creation of deep tech ventures is a complex and multi-faceted process, we explore in this review essay important principles to consider when designing support systems to incubate or accelerate deep tech ventures. While many contemporary entrepreneurial support systems cater specifically to the creation of digital start-ups, we know little about the mechanisms that affect important outcomes like investor readiness and attractiveness in the context of deep-tech ventures, i.e. ventures that commercialize early-stage technological innovations and scientific breakthroughs. We summarize the current insight into key learning activities in such ventures and their relation to the lean startup-inspired model of support programs, which currently dominate entrepreneurial ecosystems. Building on recent empirical findings on typical learning activities in deep tech ventures and outcomes patterns, we derive two important design principles that will need to be considered when structuring venture support systems. Contrary to the dominant lean start accelerator model, we suggest that deep tech acceleration and incubation programs will want to consider a loose-coupling in two dimensions: (1) technology from customer problems, in order to build broader application portfolios, and (2) time from outcome milestones, i.e. allow for variation in pace. These insights offer an important starting point for the practice of designing venture support systems, fitted to the specific knowledge context of an entrepreneurial project.
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