Research

In my research, I draw on several different scientific fields: sustainability transitions, innovation studies, agricultural economics and rural sociology, and (evolutionary) economy geography. On this page, I have collected a number of theoretical concepts that I have used in my work and that form the backbone of my consultancy services. As such the information on this page provides some theoretical background and examples of how I applied certain scientific tools and approaches in practice. For the full overview of my papers, see my research gate profile, or my google scholar profile.

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Sustainable development

There are many definition of sustainable development going around and the concepts seems to be bogged down in many controversies. So many in fact that some people prefer not to use the concept at all anymore because they argue that the concept has lost its meaning. I don’t agree with that. I think that sustainable development is still a concept that can engage and enrich policy debates if used within a participatory setting. The three capital approach, where sustainable development is defined by the ecological, economic and social pillars can be useful framework to guide these discussions. When I worked at Telos, the Brabant centre for sustainable development in the Netherlands, we developed a regional sustainability monitor based on this framework, called the Duurzaamheidbalans, or Sustainability Balance Sheet. The method itself is described in this book chapter. After a couple of years, I wrote an evaluation of our experiences in different Dutch provinces that can be found in this paper.

 

In my other work, I have taken often adopted what is called a ‘social constructivist’ view of sustainability’. This means that I view sustainability as something that is shaped by people's ideas, beliefs, and social interactions rather than as a fixed or universally agreed-upon concept. Studying the language, words and phrases people use to describe innovations, through discourse analysis, is a powerful way to investigate their underlying values and worldviews: where they differ the most and where potential opportunities for agreements can be found. I’ve used discourse analysis to study the concept of sustainable development in rural and agricultural regions, and to distinguish different kinds of agricultural innovations.

 

Scaling

A key focus of my research is the concept of scaling in agriculture and the bioeconomy. Scaling refers to the expansion of innovations from a small scale, like an individual farm, to larger scales, such as entire regions or agricultural systems. In my work I view scaling as part of a holistic process where innovations can drive systemic change: When innovations scale successfully, they do more than just get adopted; they can potentially transform entire agricultural systems. Scaling, therefore, not only promotes sustainable practices but also influences the broader agricultural landscape, including supply chains and the entire business ecosystem surrounding farming.

 

However, while scaling can increase the impact of sustainable practices on productivity, environmental conservation, and socio-economic development, it also carries potential risks. Historically, some technologies have caused unforeseen harm when they were scaled. For instance, the pesticide DDT was initially developed to combat hunger but ended up causing significant environmental damage, leading to its ban. Similarly, the large-scale production of biofuels sparked the "food versus fuel" debate, highlighting how scaling can lead to unintended consequences like deforestation and increased greenhouse gas emissions if not managed carefully. For innovations to truly support sustainable development, they must not only scale effectively but also avoid transferring environmental problems from one area to another. This requires understanding how different scales—local, regional, and beyond—interact and influence innovation processes.

 

In my work, I have developed a framework for scaling processes that makes a distinction between upscaling (the political processes necessary to change existing formal and informal rules and regulations), and outscaling (the application of the same innovation in other regions). I have developed and applied this framework in a number of papers, for instance on environmental farmer cooperatives and together with David Ayrapetyan on bioeconomy clusters. This approach to scaling also works well with the innovative ‘scaling readiness approach’ that some of my friends and former colleagues at the Wageningen University have developed. I myself have applied this framework to investigate the scaling readiness of laser-assisted land levellers in Uzbekistan.

 

Networks and network analysis

Network analysis is a valuable tool in innovation and transition studies, offering insights into how ideas, resources, and influence flow within and between organizations. A network refers to a web of relationships among individuals, groups, organizations, or technologies that interact to generate and spread new ideas. These networks include actors like businesses, research institutions, governments, and other stakeholders who collaborate, compete, or influence each other during the innovation process.

 

Network analysis combines qualitative methods to understand connections with quantitative tools to measure relationships' strength and significance. This approach provides a comprehensive view of the interactions driving innovation and transition, whether in small communities or global industries.

 

In my research, I have used network analysis to study different processes. For instance to explore the different roles project partners have in the scaling of an innovation. By mapping out connections and examining patterns of interaction, network analysis helped to identify the key players, and the pathways through which knowledge and influence travelled. In another paper we compared three national level innovation platforms in Africa with each other. This analysis revealed how innovations spread, who influences decision-making, and where collaborations can be most effective. By understanding the intricacies of these knowledge and influence networks, researchers and practitioners can better support innovation ecosystems and targeting measures to make the platforms more effective in their implementation.

 

In the ICT4BXW project, we analyzed Rwandan banana farmers' advice networks, identifying those with large networks to enhance knowledge transmission about disease management. The following website provides a simple example of this model tool.

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Innovation systems

In my research I have used the concept of the innovation system to examine how technological changes happen on a large scale. An innovation system includes all the people and organizations involved in creating, sharing, and using knowledge—such as businesses, governments, NGOs, research institutes, and even banks and consumer groups. It also considers the formal and informal "rules of the game," (the institutions) that shape how innovation happens in different settings.

 

Innovation systems can be analyzed using either a fixed framework or a dynamic functional approach. The fixed analysis offers a snapshot of an innovation system’s current status, assessing whether it has the right mix of components—such as people, networks, institutions, and infrastructure—necessary for successful innovation processes. If any of these elements are lacking or underdeveloped, the entire system may struggle to function effectively. This fixed approach is also useful for comparing different types of innovation systems, as we did in this paper on the organisation of agricultural innovation systems in Europe.

 

The dynamic, or functional approach to innovation systems is particularly valuable for analyzing changes over time. This method is especially useful for assessing the effectiveness of specific policy instruments, such as public-private partnerships in promoting various sustainable innovations, or for understanding how regional, national, and international policies have shaped the development of a biocluster in comparison to the internal actions taken by its members over time.

 

Social learning

To tackle complex sustainability problems, it’s crucial for different people and groups to work together and learn from each other—a process known as social learning. This concept is a key idea in my research. I wrote my dissertation on this topic and it forms the basis for my approach to workshop organisation and facilitation

Social learning involves people coming together to share knowledge, reflect on their experiences, and learn from one another. Unlike traditional learning, which often focuses on individual understanding, social learning is about collective learning. By discussing, debating, and collaborating, participants gain a deeper and more well-rounded understanding of the issues at hand. The "social" aspect refers to the active interaction and communication between participants, whether through face-to-face discussions, online forums, or collaborative projects. Through these interactions, people refine their ideas and integrate others' viewpoints, leading to a richer and more dynamic learning experience. That is not to say that social learning is easy! In this paper we show that when stakeholders learn internally, but fail to convince their social environment, innovations can still struggle to take off.

 

Bioclusters

In the last couple of years I have been working especially on the concept of the bioeconomy and bioclusters. In my TRAFOBIT group we studied ‘The Role and Functions of Bioclusters in the Transition to a bioeconomy). In my group we defined a biocluster as a network of companies and organizations, like universities and trade associations, which are geographically close and collaborate together in the bioeconomy—a field focused on using biological resources sustainably. However, these is not one kind of biocluster. In this book chapter I make a distinction between four types of bioclusters: agricultural agglomerations, biodistricts, green chemistry clusters and life science clusters.

 

The bioeconomy and bioclusters have become very popular instruments in many government policies to move away from fossil fuels and promote innovation at the same time. Traditionally, cluster research was mainly interested in issues of competitiveness and innovation, but nowadays there's a broader focus on how they can contribute to regional development and sustainability as well. Bioclusters are thought to be especially promising for rural areas in this regard. Together with Kerstin Wilde I have investigated the promises around both the bioeconomy and the cluster concept and the different perspectives on different groups of stakeholders.  

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