To address this key question, our research focusses on understanding, facilitating, sustaining and upscaling these process, through reflexive monitoring and evaluation.
Through reflection and learning we also combine the analysis of challenges and innovations with methodologies for knowledge integration and transformation. This brings us to our additional research focus: Training and empowering stakeholders, professionals and students to facilitate and participate in inclusive multi-stakeholder innovation processes.
Levels of analyses
Realising effective innovations through inclusive multi-stakeholder innovation processes typically entails interventions at multiple levels, as an activity at one level strongly influences activities at other levels as well. Therefore, our research foci are studied at all levels and in interaction:
- Micro: actors and projects
- Meso: organisations and networks
- Macro: socio-technical systems
Our analyses start from different, but converging starting points. We may start from new developments in science and technology, such as those in biotechnology, neuroscience, nanotechnology and information and communication technology. To adequately anticipate potential societal impacts and address societal needs and concerns, innovation processes need to include a wide variety of factors and actors. Secondly, our research also often starts from the complex challenges of vulnerable groups in society in specific domains: (global) health and well-being and nature, agriculture and environment. Thirdly, we respond to the increasing need to develop capacities of students and professionals in science-society interactions and transdisciplinary research. Lastly, we take an historical point of view, in order to provide a long-term perspective on contemporary and future processes of knowledge production and innovation. Together, this transpires into our five research domains.
Conducting our research in different domains provides complementarity and synergy, leading to more robust knowledge. In other words, findings in one domain are tested and verified in other domains, thereby contributing to insights that are both contextualised and generalisable.