Do Startup Programmes Actually Commercialise Science?

Do Startup Programmes Actually Commercialize Science?

A Preliminary European Analysis of Deep Tech Outcomes, Programme Participation, and Measurement Gaps

Witte, M. K. (2026), DOI 10.5281/zenodo.19663641

Every year, European public and private funders invest heavily in incubators, accelerators, venture builders, and spinout centres — on the assumption that these structures translate scientific research into market outcomes.

That assumption has rarely been tested against a hard definition of commercialisation.

What this paper does

This industry research paper asks a narrower and more consequential question than most programme evaluations ever attempt:

Among European deep tech and science-based companies that have actually reached meaningful commercial outcomes — how visible is startup programme participation in their development path?

It uses a stricter commercialisation threshold than is typical in programme reporting:

• Technology at TRL 8/9 or comparable industrial deployment
• Recurring revenues above a defined threshold
• IPO or M&A

And it combines a review of existing evidence on programme effectiveness with a preliminary coded sample of European deep tech firms that meet at least one of these criteria.

What the preliminary evidence shows

• Many successful firms are more visibly associated with spinout origins, strategic partnerships, licensing models, or revenue-first paths than with classical accelerator participation
• Existing research captures early-stage venture performance more reliably than hard commercialisation outcomes
• Programme participation is not systematically documented — and most studies do not track long-horizon outcomes at all
• Current evaluation systems may be overstating programme contribution to commercialisation — because they rarely measure commercialisation itself

Who this is for

• Programme directors and operators who need to understand what their metrics are actually measuring
• Funders and oversight bodies making decisions based on programme performance data
• Policymakers designing or funding deep tech support infrastructure
• Technology transfer offices and spinout centres tracking contribution claims
• Corporate innovation leaders evaluating programme affiliations

Format: PDF, 17 pages
Includes: Literature review · Pilot sample of 20 European deep tech companies · Methodology · 4 testable hypotheses · Policy implications · Annex with full sample table