The research described in this paper is part of SEI’s Behaviour and Choice Initiative. The initiative explores the factors that influence household choice and decision-making, with a specific focus on the uptake of technologies, services or changes of practice that lead to sustainable outcomes. It does so using case studies of drivers of behaviour and a range of analytical approaches. This paper relies on empirical data on the drivers of adoption of improved cookstoves in Kenya.

A woman cooking dinner for her family in Kisumu West, Kenya

A woman cooking dinner for her family in Kisumu West, Kenya. Photo: Peter Kapuscinski, World Bank / Flickr.

The paper outlines an approach for synthesizing empirical data from different analytical levels using the Cross-impact Balance (CIB) method in a way that is epistemologically consistent and documents its application. In doing so, it contributes a systemic view of how behaviour change with regard to the adoption of an improved cooking technology may – or may not – come about.

In order to explore consistent stories of behaviour change, the authors combine CIB with Scenario Diversity Analysis (SDA). Combining CIB with SDA allows a reduction of what might potentially be half a million combinations of scenarios – what is referred to in the paper as scenario kernels – to just four, which represents quite an efficient reduction.

This combined approach can help build a more comprehensive policy perspective than is achieved by leaning solely on analyses from a single analytical level. Systematically combining and exploring insights from different levels also lays the foundations for a deeper understanding of how a policy intervention at one level might influence an outcome at another level.

This knowledge can inform policymakers, development practitioners and other sector actors on how programmes can be designed and implemented to respond to the combination of drivers that operate at different societal levels. Insights can also be used to build a conceptual framework that is applicable to multi-level determinants of household behaviour change across geographical settings, technologies and socio-cultural contexts.