The European Commission has introduced a new framework to measure how research is transformed into tangible economic and societal benefits across the European Research Area (ERA), marking a significant step forward in how innovation performance is assessed across Europe.
Building on the results of an expert study, the framework moves beyond traditional indicators of technology transfer to capture what the Commission calls the “multifaceted value” of knowledge valorisation. With 16 indicators and 41 metrics, it offers Member States a more comprehensive tool to understand how research investments translate into outcomes ranging from commercial innovation to public engagement and policymaking.
But the study underpinning the framework reveals a more profound shift in thinking: knowledge valorisation is no longer seen as a linear process focused mainly on commercialisation, but as a complex, interconnected system spanning multiple actors, stages, and types of value creation.
From fragmented metrics to a systemic approach
One of the central findings of the study is that existing Research and Innovation (R&I) measurement systems fail to fully capture knowledge valorisation. Current frameworks tend to prioritise narrow indicators such as patents or licensing revenues, overlooking other critical pathways like citizen engagement, policy uptake, or open science practices.
Moreover, measurement approaches across EU countries remain fragmented, with no harmonised system at the European level. This has limited the ability to compare performance or design coordinated policies.
The new framework addresses this gap by mapping existing indicators and introducing new ones, combining established data sources with emerging metrics. Importantly, it reflects both economic and societal dimensions of value, signalling a broader understanding of what “impact” means in the context of research.
Seven channels, one integrated system
A key innovation lies in the framework’s structure: it captures knowledge valorisation across seven interconnected channels, including academia-industry collaboration, startup creation, intellectual asset management, citizen engagement, and policy uptake.
Rather than treating these as separate silos, the framework emphasises their interdependence. Activities such as co-patenting, research-driven entrepreneurship, and standardisation are understood to reinforce each other within a broader innovation ecosystem.
This integrated design is reflected in the metrics themselves. The framework includes:
- 22 output metrics, capturing immediate results such as patents, startups, or collaborations
- 18 impact metrics, assessing longer-term effects on the economy and society
- 1 input metric, ensuring early-stage activities are also considered
By balancing these elements, the framework enables both short-term monitoring and long-term evaluation: a notable improvement over previous approaches.
A “layered chain” of value creation
Perhaps the most important conceptual contribution of the study is its redefinition of knowledge valorisation as a layered chain of outputs, outcomes, and impacts.
Early ambitions to focus primarily on impact measurement proved unrealistic, the study notes, as impacts often emerge over long time horizons and are difficult to attribute directly to specific research activities.
Instead, the framework captures:
- Early-stage outputs (e.g. research collaborations, data sharing)
- Intermediate outcomes (e.g. startup formation, policy influence)
- Long-term impacts (e.g. economic growth, societal benefits)
This layered approach provides a more robust and realistic basis for assessing value creation, while helping policymakers understand how different stages of the innovation process are connected.
Practical, Implementable, and Cross-Cutting Innovation Metrics
From an operational standpoint, the framework is designed to be largely implementable using existing data. Of the 41 metrics studied, 18 are already available through established sources as Eurostat, OECD databases, and patent repositories, while the remaining 23 can be developed using emerging data or new methodologies. The reliance on centralised EU-level data sources is expected to reduce administrative burdens for Member States and facilitate cross-country comparisons.
However, the study also highlights several challenges that need to be addressed before full-scale implementation. These include:
- The lack of harmonised definitions for key concepts such as “startup”, “scaleup”, and “spinout”;
- Variations in data quality and maturity across countries
- Methodological limitations linked to sectoral differences or proxy indicators
To mitigate these issues, the Commission recommends pilot testing selected metrics, refining methodologies, and improving data alignment. It also points to the value of qualitative approaches, such as case studies and surveys, to complement quantitative indicators.
Another key insight is the importance of cross-cutting indicators that reflect the interconnected nature of research and innovation ecosystems. Metrics such as co-patenting or IP-based firms often span multiple valorisation channels, making it difficult and potentially misleading to assign them to a single category. By embracing a cross-cutting approach, the framework more accurately mirrors the reality of modern innovation systems, where collaboration, entrepreneurship, and policy development are deeply intertwined.
Supporting Europe’s innovation ambitions
The new measurement framework is expected to feed into the ERA monitoring mechanism and the upcoming Science, Research and Innovation Performance (SRIP) report, providing policymakers with a more nuanced tool to track progress and design evidence-based policies.
Ultimately, the initiative reflects a broader ambition: to ensure that Europe’s research excellence translates more effectively into societal and economic value.
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