Factors enhancing the level of utilisation of research knowledge on ecosystems

Abstract

The significant resource investment in research on ecosystems for development of the Global South does not necessarily result in high levels of research knowledge utilisation (RKU). Understanding the factors associated with various levels of RKU can inform funding agencies and researchers developing new projects. We applied a combination of a questionnaire survey and follow up interviews with members of research teams of multiple, broadly comparable projects to make an assessment of achieved RKU levels using a combination of quantitative statistical hypothesis testing and narrative description of survey responses. Research knowledge dissemination by members of the project team who work for non-academic institutions or champions, e.g. particularly motivated people that promote and facilitate implementation or adoption of the project results, and via television was associated with higher research knowledge utilization. By contrast, dissemination by members of the project team working for academic institutions and via peer-reviewed journals was associated with lower RKU. The achieved level of RKU was consistently lower than the targeted level of RKU across spatial scales. The discrepancy between the perceived level of RKU and the evidence provided by survey respondents indicates the need for better monitoring the utilisation of research knowledge in development pathways. Our results further suggest that three years project duration is too short to achieve high levels of RKU in socio-ecological systems. We recommend involvement of non-academic members of the project team in project design, leadership and dissemination for increasing RKU.

Global history, the emergence of chaos and inducing sustainability in networks of socio-ecological systems

“…This sensitivity is due to the presence of multiple feedback loops, both positive and negative, which can amplify small perturbations and lead to vastly different outcomes over time. For this reason, it is typically difficult to model trends pertaining to such systems [32], and it takes a long time to assess the effect of actions on these systems [33]. A classic example of this phenomenon is the Lorenz attractor [34], where even small differences in the initial conditions of a simple mathematical model could lead to vastly different long-term behaviors.…”