In recent decades, some EU Member States, particularly the Nordic countries, have invested in high-quality data infrastructures to capture the outcomes attributable to research and innovation (R&I) grants to businesses. While quantitative methods have become more and more sophisticated, a simultaneous interest in studying more qualitative, behavioural aspects can be observed. This kind of holistic systems analysis was explored and discussed during the first MLE on Ex-Post Evaluation of Business R&I Grant Schemes, which ran throughout 2016. The present MLE follows a challenge-driven approach and will include themes such as the use of Big Data in R&D grant evaluations, as well as methods for capturing behavioural change.
Documents
An overview of the Mutual Learning Excersice on Evaluation of Business R&D Grant Schemes
Grants and loans play a vital role in public innovation policy, prompting businesses to spend more on R&D and helping them overcome barriers to innovation such as risk aversion and market failures. With large sums – and even larger outcomes – at stake, R&D policy-makers need robust and reliable information, which demands a lot from evaluation methodologies.
This report has been prepared for a Mutual Learning Exercise on the Evaluation of Business R&D Grant Schemes.
This thematic report addresses the topic of applying mixed method approaches to the evaluation of public schemes to support R&D and innovation in firms, specifically business R&D grants and associated innovation schemes. It sets out the broad context for the use of mixed-method approaches in the evaluation of innovation support schemes, with a focus on business R&D grants.
This thematic report addresses the topic of understanding and measuring the behavioural change in firms through these schemes and challenges to capture these. It is essential that the community of STI scholars, STI evaluators and STI policy-makers acknowledge more fully the importance of measuring and capturing behavioural change through R&D and innovation schemes.
This report discusses the use of big data to evaluate grant schemes and other types of support for R&D and innovation by businesses. One aspect of big data – data linking – is already being implemented by several public agencies, while others – such as web scraping, text mining and machine learning – are less mature.