Fraunhofer Institute for Systems and Innovation Research ISI
Fraunhofer Institute for Systems and Innovation Research ISI

Innovation Policy and Technology Transfer Design

We understand innovation and knowledge transfer at every level, from individuals and organizations up to entire innovation ecosystems. This puts us in a position to develop effective interventions, formats and strategies for innovation policy and knowledge transfer.

We provide decision-makers, companies and the knowledge and technology transfer community with evidence-based support when designing systems of innovation and transfer. Our applied research fosters resilience and the strategic planning of collaboration, knowledge transfer and support activities.

  • For politics, we design and monitor innovation policy measures, support programs and transformation processes through additional, complementary research and evaluations.
  • We support businesses with studies, networking formats and technology analyses. As an independent public research institution, we provide well-founded information to help you understand your role and development options within innovation ecosystems.
  • We support the knowledge and technology transfer community with designing transfer strategies, developing effective transfer formats and analyzing the potential for commercialization.

Our team works independently and integrates the latest findings from innovation and behavioral economics and knowledge and technology transfer research. What distinguishes us is our ability to combine scientific excellence with practical solutions and support our partners in making innovation and knowledge and technology transfer more effective, increasing their social impact.

Innovation and Knowledge Transfer: Key Research Areas

In the light of profound social and economic transformation, we look at how knowledge, technologies and innovations make their way from research into application. We analyze how people, organizations and innovation ecosystems interact and derive strategies, tools and recommendations for courses of action for policymakers, businesses and academia. We combine knowledge transfer research, innovation ecosystems and additional, complementary research to better understand and actively shape innovation and knowledge transfer processes.

Transfer & Management

Technology transfer and innovation management are essential levers for translating research results into products, innovations and business models:

  • organizational behavior and human resource management approaches: supporting the knowledge and technology transfer community, innovation departments and incubators
  • strategy and evaluation: analyzing the potential for commercialization and the goal-oriented design of technology transfer strategies
  • mechanisms and incentives: designing effective technology transfer structures and behavior-based motivation models for researchers and stakeholders
  • interfaces and culture: professionalizing collaborations and promoting a lively culture of exchange among academia, businesses and start-ups

Innovation (Eco)systems

Businesses, research institutions, start-ups, intermediaries and public stakeholders pool their knowledge and skills in innovation ecosystems. Knowledge and technology transfer enable the development, diffusion and application of innovations. We support stakeholders and networks with the following:

  • ecosystems and stakeholder networks: examining the structures, stakeholder constellations and patterns of interaction in innovation ecosystems using network analyses, case studies and surveys
  • success factors and obstacles: identifying supportive framework conditions, challenges and obstacles to knowledge and technology transfer
  • strategy and recommended courses of action: deriving evidence-based measures to strengthen innovation ecosystems and knowledge and technology transfer processes

Accompanying Research

Accompanying research interconnects the funded technology projects in a strong network to maximize their impact. We look at the bigger picture and help to bridge the gap between research and practical application: 

  • networking and dialogue: identifying stakeholders and sharing knowledge in interactive formats to shape emerging innovation ecosystems
  • analyses and monitoring: systematic trend analysis using discourse, sentiment and ecosystem analyses to derive recommended courses of action
  • empowerment: knowledge transfer training using gamification, practical workshops and train-the-trainer techniques
  • communication: tailoring results to specific target groups in policy papers, social media and exhibits

Methods and tools for evidence-based innovation policy and goal-oriented design of knowledge transfer processes

Successful knowledge transfer always begins with the people involved. This is why our research focuses on discourse-based methods, involving experts and consistently designing user-friendly tools. We integrate these qualitative findings into the bigger picture by combining them with quantitative analyses of complex innovation ecosystems. This results in scientifically validated, intuitive tools for strategic decision-making. 

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  • © Weiße, M.; Dornbusch, F.; Posselt, T. (2026): A Role-Based View on Technological Convergence in Innovation Ecosystems: Evidence from Quantum Computing.
    Roles within an innovation ecosystem along the path of technological convergence

    Innovation ecosystem analysis explores how stakeholders, technologies and institutions interact to transform knowledge into innovations and value creation. The focus is on technology- and topic-based ecosystems in which businesses, research institutions, intermediaries, funding organizations and other stakeholders are interconnected through collaborative or competitive relationships. The analysis reveals how innovations emerge, diffuse and are applied due to these stakeholders’ interactions.

    In contrast to stakeholder-centered approaches, innovation ecosystem analysis looks at the mutual dependencies, value contributions and interactions within an ecosystem. There is a special focus on the functions required for the development, diffusion and use of innovations. It is possible to identify existing and missing functions and structural gaps using a role-based approach. This can detect obstacles to knowledge and technology transfer and opportunities to strengthen innovation processes.

    The method combines bibliometric analyses of publications, patents and project data with network analyses, interviews and participatory formats. It enables a deeper understanding of innovation dynamics and provides an evidence-based foundation for designing innovation policy, knowledge transfer strategies and support structures.

  • Discourse influences which issues make it onto the political agenda, how innovation challenges are framed and which solutions are considered legitimate. Discourse analysis studies how language, narrative and interpretative patterns in debates and documents help to shape social reality and structure political action. As a qualitative method, it is particularly suitable for analyzing the dynamics of transformation. It reveals which coalitions of stakeholders have the power to influence how certain problems are understood and how dominant frames shift over time.

    In knowledge and technology transfer research, discourse analyses help us to understand how society’s expectations of science are changing, which models of knowledge and technology transfer are taken for granted in policy and practice, and how commercialization is institutionally embedded. In innovation policy research, discourse analysis is increasingly combined with policy analysis and quantitative approaches, for example, to question the implicit assumptions underlying support programs.

  • Integrating experts is a key element of our research. The targeted involvement of experts from politics, academia, industry and practice means we can not only develop our methodological approaches theoretically but also continuously test and validate them against real-world demands.

    Consulting experts takes place using various formats: structured interviews, moderated workshops and focus groups with carefully selected stakeholder groups. Iterative feedback loops in ongoing projects ensure that real-world knowledge is directly incorporated into developing the methods and continuously strengthen the application relevance of the results.

    Case studies form the empirical basis for testing the methods and tools we develop under real-world conditions. Experts can actively support these case studies by providing information, making critical assessments and validating approaches.

    Typical case study applications:

    • Reviewing and testing incentives for knowledge and technology transfer
    • Evaluating locations and opportunities for new technologies
    • Analyzing innovation policies and their regional impact
    • Examining sector-specific issues

    Interlinking expert knowledge and case-based analysis ensures the quality and transferability of our research findings. Findings from case studies are incorporated into further developing assessment structures and decision support systems. This creates a continuous learning process that advances both methodology and practice.

  • Our group develops research and transfer tools following the principle of human-centered design (HCD). The focus here is not solely on technical functionality, but rather on the question of how tools should be designed so that those applying them can use them intuitively, efficiently and reliably. Good tools lower the barriers to using complex methods and data and enable well-founded decisions to be made even outside the narrower research environment.

    HCD is an iterative problem-solving process. It identifies user demands systematically, develops early prototypes and tests and refines these using feedback from the target groups. Feedback from the field is used directly as input for the next stage of development. This approach minimizes mismatches, reduces training effort and greatly increases the acceptance of the developed tools.

    What does HCD mean for tools in the fields of supporting innovation, designing knowledge and technology transfer or evaluating economic policies? It offers clear visualizations of complex correlations, interaction logic adapted to different use contexts, and the presentation of results in a comprehensible and practical manner.

    The objective is to design scientifically sound tools that political decision-makers, businesses and the knowledge and technology transfer community can use autonomously and with confidence. This is how HCD contributes directly to the effectiveness and social impact of research.