The assumption of Athena’s research program is that in order to realize the development of science and technology that contributes to solving complex societal challenges in a sustainable and equitable way, the knowledge and expertise of a diverse group of scientists as well as non-scientific actors need to be integrated in decision making on research and innovation processes – the research becomes transdisciplinary. The rationale for this is that these actors have relevant knowledge and different perspectives about needs and concerns. 

The key question then is: How to organize an inclusive multi-stakeholder innovation process which contributes to solving complex societal challenges? This requires an understanding of the dynamics within and between science and society; methodologies (principles and heuristics) that can guide inclusive multi-stakeholder innovation processes; strategies that can sustain and upscale such processes; methodologies to monitor and evaluate such innovation processes; as well as approaches to train students and professionals in these methodologies.

Because of a lack of suitable approaches to inclusive multi-stakeholder innovation processes, Athena’s research is partly focused on methodology development. For example, to organize a structured inclusive innovation process, Athena has developed the Interactive Learning and Action (ILA) approach. This transdisciplinary research approach integrates available knowledge and perspectives on societal needs and on potential innovations from relevant actors in societal and scientific fields (e.g. scientists, policy makers, civil society organizations, companies, citizens, and consumers). The methodology includes a range of methods and tools, including various types of multi-stakeholder dialogues. To study ongoing (system) innovation processes Athena has (co-)developed the method of Reflexive Monitoring in Action (RMA) – a participatory, learning method to guide multi-level innovation/change processes.

As inclusive multi-stakeholder innovation processes typically entail changes at multiple levels, our analyses likewise cover multiple levels. To enhance analytic capacity various theoretical notions are applied at each level, such as:

  •  micro level: inclusive deliberation and decision-making; anticipation and reflection; framing and sense-making; mutual learning; valorization;
  •  meso level: learning organizations, learning networks and communities of practice;
  •  macro level: system innovation theory, transition management and strategic niche management.

Activities at these levels strongly influence each other. The use of these different levels of analysis emphasizes the need for interventions at different levels in order to realize appropriate innovations. In our research we apply these conceptual frameworks, but at the same time our empirical research leads to new insights into these frameworks, thereby enriching them with new concepts, relationships between concepts and perspectives.

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Our analyses start from three different, but converging starting points. On the one hand we start from the new and exciting developments in science and technology. To adequately anticipate potential societal impacts and address societal needs and concerns, the innovation process needs to include a wide diversity of factors and actors. Secondly, we start from the complex challenges of poor and vulnerable groups in society. Adequately addressing the complex societal challenges requires a transdisciplinary approach. Thirdly, we assess the lessons learnt from training students and professionals in science-society interaction in general and transdisciplinary approaches in particular as well as the effects of this training. These different starting points are reflected in the four domains of Athena’s research program – the first takes emerging technologies as the point of departure, the next two start from the complex societal challenges and the fourth considers the issues of education:

Research in these domains takes place at the different levels (micro, meso, macro) and provides complementarity and synergy, leading to more robust knowledge in relation to the key question. In other words, findings in one domain are tested and verified in other domains, thereby contributing to insights of contextualization and generalizability.