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Quantitative and Causal Effects of Generic vs. Specialised Programmes for Science and Deep Tech Ven
Quantitative and Causal Effects of Generic vs. Specialised Programmes for Science and Deep Tech Ventures
A Structural Analysis of Programme–Venture Fit, Outcome Distributions, and the Cost of Misalignment
Most startup programmes were not designed for science ventures. They were designed for ventures whose primary uncertainty is market-oriented — who the customer is, what they will pay, how to grow. Science and deep tech ventures face a categorically different problem: whether the mechanism works, whether it can be scaled, which regulatory pathway applies, and what can be protected before it is publicly disclosed.
When generic programme logic is applied to technical-uncertainty ventures, it does not simply produce less benefit. It produces active structural damage through four identifiable mechanisms — and current evaluation systems cannot detect it.
This paper names and formalises those mechanisms for the first time.
What this paper covers
- The bottleneck uncertainty concept: why programme–venture fit depends on what is actually blocking the venture, not its industry label
- Four distortion mechanisms: time displacement, amplified pivot costs, IP disclosure risk under European absolute novelty law, and optionality collapse
- A real options formalisation of optionality collapse — the destruction of application portfolio value through forced early narrative commitment
- A structured review of the accelerator effectiveness literature, with explicit confidence levels
- Quantified cost scenarios: what misfit costs per venture, per programme cycle, and at ecosystem scale
- What specialised programme design looks like and where the evidence for it sits
- A four-question fit assessment framework for programme operators
- 15 practical recommendations across programme designers, funders, policymakers, and founders
Who this is for
Programme managers and directors running incubators, accelerators, or deep tech support initiatives. Institutional funders and oversight bodies evaluating programme performance. Policymakers designing innovation support infrastructure. Technology transfer offices and spinout centres. Science venture founders deciding whether a programme is worth their time.
Format and citation
29 pages · 6 annexes · peer-reviewed references · CC BY-NC-ND 4.0
Part of a research series on structural conditions for deep tech commercialisation. Related papers available here.