Final Report Summary - KNOW2ADAPT (Knowledge Transfer for Climate Change Adaptation)
In many regions around the world, climate change is projected to further increase the frequency and intensity of water-related disasters, such as, floods and droughts. International cooperation and knowledge transfer processes may help countries and regions to adapt more effectively and efficiently to climate change. In the project Know2Adapt (Knowledge Transfer for Climate Change Adaptation) we looked into learning in and from international cooperation. Given that the European Commission has financially supported hundreds of climate adaptation-oriented projects in the past decades, we decided to focus on European cooperation projects (INTERREG IV and FP7). The central question that was addressed reads: To what extent and under which conditions participants, organizations and wider societal networks learn from these European cooperation projects?
Conceptualization of learning conditions and outcomes
Studies on European cooperation projects show that learning occurs in projects by participants as well as from projects by partner organizations and other organizations and networks that are not directly involved in a project. In the literature, we identified three learning concepts that could help to understand these learning processes and outcomes. Group learning refers to the relational and substantive learning by persons who are directly and intensely involved in the project. Organizational learning refers to the uptake and use of lessons learned by represented organizations. Network and societal learning refers to the uptake and use of lessons learned by organizations that were not involved in the project and by wider networks and communities. This so-called multi-level conceptualization of learning is visualized in the attached Figure. As the Figure shows, we assert that projects involve three distinct but interrelated learning processes. These processes have different outcomes and can be explained by different combinations of conditions. In the scientific literature we identified for each learning outcome a set of potentially relevant conditions. This includes partner-specific conditions (e.g. motivation, ability or opportunity), project-specific conditions (e.g. interaction process or communication strategy), organization-specific conditions (e.g. prior related knowledge) and context-specific conditions (e.g. policy agenda).
Qualitative Comparative Analysis (QCA)
As we wanted to do justice to within-case complexity and at the same time develop knowledge that is generalizable across more projects, we decided to use QCA as a research approach and method. The method is based on set-theoretic methods and is able to identify complex causal relations. The method is based on the notion that the absence or presence of an outcome may be produced by diverse conditions and combinations thereof. Systematic comparison of an intermediate number of cases helps to identify which conditions are necessary or sufficient for an outcome to occur. We used the method to systemically compare sets of conditions and related learning outcomes for an intermediate number of cases. A case refers here to a participant of a European cooperation project who is representing a partner organization (e.g. a municipality, a national agency, regional authority, an NGO or a knowledge institute). We selected seven projects from the following programmes: FP7 Environment, INTERREG IVC and INTERREG IVB for the North Sea, Northwest Europe and Southeast Europe. All projects focused on climate change adaptation in the water sector and were implemented in the period between 2006 and 2014. They differed in terms of project duration, diversity and number of organizations involved, budget and learning orientation. For these seven projects, we collected data for the overall project and for 2 to 6 project partners (30 partners in total) by means of document analysis and semi-structured interviews. All qualitative data were transformed into fuzzy values between 0 and 1 to allow for systemic comparison of cases using software for QCA.
Results and conclusions
For each type of learning we compared conditions and outcomes. Group learning and in particular substantive learning was rather high for nearly all partners. There were no major differences across projects in terms of how the interaction process and the consortium were functioning. Ability, motivation and opportunity of project participants were hardly ever unsupportive of group learning. We recently started a follow-up study into the co-production of knowledge to better understand the group learning process.
For organizational learning, we found that knowledge institutes and lead partners follow other different pathways towards organizational learning than other partners. For all cases, we found that a combination of group learning and cognitive embedding is necessary for high levels of organizational learning to occur. Prior related knowledge is mentioned as a key factor in the literature on organizational learning. Our analysis shows that when combined with opportunity and necessary conditions (group learning and cognitive embedding), limited prior related knowledge produces high levels of organizational learning. Thus, a lack of prior related knowledge may actually be supportive of organizational learning. When combined with limited opportunity the presence of prior related knowledge may also be supportive of learning. For lead partners this is not the case. All studied lead partners have a high score on prior related knowledge. While this is supportive of learning in other cases, this does not apply to lead partners. For the knowledge institutes, we found that most participants simply did not see the point or added value of transferring knowledge to their organization.
For network and societal learning our preliminary findings are that motivation is necessary for high levels of learning to occur. For most cases external actor involvement is also necessary. However, this does not apply to lead partners. Also, adequate project communication and knowledge and the project theme being on the policy agenda are generally needed for network and societal learning.
Implications and wider impacts
Our findings show that different pathways may produce learning in European cooperation projects. Participants learn but these lessons learned are not necessarily transferred to their home organizations or to wider policy networks. Individual motivations play a role as do other conditions, such as, motivation and organization or context-specific conditions. Some projects are clearly more supportive of network and societal learning than others whereas others are much better at co-producing knowledge. Our findings are useful for policymakers who would like to stimulate international knowledge transfer, those involved in knowledge capitalisation and management of European programmes as well as to those engaged in European projects as policymaker or practitioner. We are still in the process of translating our findings into scientific publications and a policy brief.
More information?
- Contact persons: Joanne Vinke-de Kruijf (University of Twente) and Claudia Pahl-Wostl (University of Osnabrück)
- https://www.researchgate.net/project/Know2Adapt-Knowledge-transfer-for-climate-change-adaptation
Conceptualization of learning conditions and outcomes
Studies on European cooperation projects show that learning occurs in projects by participants as well as from projects by partner organizations and other organizations and networks that are not directly involved in a project. In the literature, we identified three learning concepts that could help to understand these learning processes and outcomes. Group learning refers to the relational and substantive learning by persons who are directly and intensely involved in the project. Organizational learning refers to the uptake and use of lessons learned by represented organizations. Network and societal learning refers to the uptake and use of lessons learned by organizations that were not involved in the project and by wider networks and communities. This so-called multi-level conceptualization of learning is visualized in the attached Figure. As the Figure shows, we assert that projects involve three distinct but interrelated learning processes. These processes have different outcomes and can be explained by different combinations of conditions. In the scientific literature we identified for each learning outcome a set of potentially relevant conditions. This includes partner-specific conditions (e.g. motivation, ability or opportunity), project-specific conditions (e.g. interaction process or communication strategy), organization-specific conditions (e.g. prior related knowledge) and context-specific conditions (e.g. policy agenda).
Qualitative Comparative Analysis (QCA)
As we wanted to do justice to within-case complexity and at the same time develop knowledge that is generalizable across more projects, we decided to use QCA as a research approach and method. The method is based on set-theoretic methods and is able to identify complex causal relations. The method is based on the notion that the absence or presence of an outcome may be produced by diverse conditions and combinations thereof. Systematic comparison of an intermediate number of cases helps to identify which conditions are necessary or sufficient for an outcome to occur. We used the method to systemically compare sets of conditions and related learning outcomes for an intermediate number of cases. A case refers here to a participant of a European cooperation project who is representing a partner organization (e.g. a municipality, a national agency, regional authority, an NGO or a knowledge institute). We selected seven projects from the following programmes: FP7 Environment, INTERREG IVC and INTERREG IVB for the North Sea, Northwest Europe and Southeast Europe. All projects focused on climate change adaptation in the water sector and were implemented in the period between 2006 and 2014. They differed in terms of project duration, diversity and number of organizations involved, budget and learning orientation. For these seven projects, we collected data for the overall project and for 2 to 6 project partners (30 partners in total) by means of document analysis and semi-structured interviews. All qualitative data were transformed into fuzzy values between 0 and 1 to allow for systemic comparison of cases using software for QCA.
Results and conclusions
For each type of learning we compared conditions and outcomes. Group learning and in particular substantive learning was rather high for nearly all partners. There were no major differences across projects in terms of how the interaction process and the consortium were functioning. Ability, motivation and opportunity of project participants were hardly ever unsupportive of group learning. We recently started a follow-up study into the co-production of knowledge to better understand the group learning process.
For organizational learning, we found that knowledge institutes and lead partners follow other different pathways towards organizational learning than other partners. For all cases, we found that a combination of group learning and cognitive embedding is necessary for high levels of organizational learning to occur. Prior related knowledge is mentioned as a key factor in the literature on organizational learning. Our analysis shows that when combined with opportunity and necessary conditions (group learning and cognitive embedding), limited prior related knowledge produces high levels of organizational learning. Thus, a lack of prior related knowledge may actually be supportive of organizational learning. When combined with limited opportunity the presence of prior related knowledge may also be supportive of learning. For lead partners this is not the case. All studied lead partners have a high score on prior related knowledge. While this is supportive of learning in other cases, this does not apply to lead partners. For the knowledge institutes, we found that most participants simply did not see the point or added value of transferring knowledge to their organization.
For network and societal learning our preliminary findings are that motivation is necessary for high levels of learning to occur. For most cases external actor involvement is also necessary. However, this does not apply to lead partners. Also, adequate project communication and knowledge and the project theme being on the policy agenda are generally needed for network and societal learning.
Implications and wider impacts
Our findings show that different pathways may produce learning in European cooperation projects. Participants learn but these lessons learned are not necessarily transferred to their home organizations or to wider policy networks. Individual motivations play a role as do other conditions, such as, motivation and organization or context-specific conditions. Some projects are clearly more supportive of network and societal learning than others whereas others are much better at co-producing knowledge. Our findings are useful for policymakers who would like to stimulate international knowledge transfer, those involved in knowledge capitalisation and management of European programmes as well as to those engaged in European projects as policymaker or practitioner. We are still in the process of translating our findings into scientific publications and a policy brief.
More information?
- Contact persons: Joanne Vinke-de Kruijf (University of Twente) and Claudia Pahl-Wostl (University of Osnabrück)
- https://www.researchgate.net/project/Know2Adapt-Knowledge-transfer-for-climate-change-adaptation
