What qualitative systems mapping is and what it could be: integrating and visualizing diverse knowledge of complex problems

Hanger-Kopp, S., Lemke, L., & Beier, J. (2024). What qualitative systems mapping is and what it could be: integrating and visualizing diverse knowledge of complex problems. Sustainability Science 10.1007/s11625-024-01497-3.

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Abstract

Researchers in sustainability science deal with increasingly complex problems that cross administrative, geographical, disciplinary, and sectoral boundaries, and are characterized by high stakes and deep uncertainties. This in turn creates methodological challenges to frame, structure, and solve complex problems in science and practice. There is a long tradition in visualizing systems as diagrams, and concept and cognitive maps, but there is insufficient differentiation and comparison between these methods and no clear umbrella term has yet been established. Against this background, we systematically review three foundational methods from different academic disciplines—causal diagrams, concept mapping, and cognitive mapping. Comparing and contrasting them, we facilitate a coherent understanding of qualitative systems mapping (QSM) as an umbrella term. We then proceed to explore the evident intersections between these methods to showcase some of the inter- and transdisciplinary opportunities and challenges crystallizing in integrated QSM approaches. Finally, we share case study insights from the food–water–biodiversity nexus in Austria and elaborate on some of the methodological nuances to data integration in QSM. Overall, with this overview paper, we lay the groundwork for a systematic, transparent, and yet flexible development and application of QSM methods to support mixed-methods research design and clear case study documentation, as well as fostering effective inter- and transdisciplinary communication in sustainability science. Further research needs to explore these QSM applications in depth across alternative sustainability science contexts, particularly with respect to efficient and rigorous protocols for knowledge and data integration vis-a-vis complex problems and transdisciplinary research processes.

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