Design and quality criteria for archetype analysis
Klaus Eisenack, Resource Economics Group, Humboldt-Universität zu Berlin, Berlin, Germany
Sergio Villamayor-Tomas, Institute of Environmental Science and Technology (ICTA), Autonomous University of Barcelona, Barcelona, Spain
Graham Epstein, Environmental Change and Governance Group, School of Environment, Resources and Sustainability, University of Waterloo, Canada
Christian Kimmich, Department of Environmental Studies, Masaryk University, Brno, Czechia
Nicholas Magliocca, University of Alabama, Department of Geography, Tuscaloosa, Alabama, USA
David Manuel-Navarrete, School of Sustainability, Arizona State University, Tempe, Arizona, USA
Christoph Oberlack, Institute of Geography, University of Bern, Bern, Switzerland; Centre for Development and Environment (CDE), University of Bern, Bern, Switzerland
Matteo Roggero, Resource Economics Group, Humboldt-Universität zu Berlin, Berlin, Germany
Diana Sietz, Potsdam Institute for Climate Impact Research, Earth System Analysis Research Domain, Potsdam, Germany;
Wageningen University, Soil Physics and Land Management Group, Wageningen, The Netherlands
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A key challenge in addressing the global degradation of natural resources and the environment is to effectively transfer successful strategies across heterogeneous contexts. Archetype analysis is a particularly salient approach in this regard that helps researchers to understand and compare patterns of (un)sustainability in heterogeneous cases. Archetype analysis avoids traps of overgeneralization and ideography by identifying reappearing but nonuniversal patterns that hold for well-defined subsets of cases. It can be applied by researchers working in inter- or transdisciplinary settings to study sustainability issues from a broad range of theoretical and methodological standpoints. However, there is still an urgent need for quality standards to guide the design of theoretically rigorous and practically useful archetype analyses. To this end, we propose four quality criteria and corresponding research strategies to address them: (1) specify the domain of validity for each archetype, (2) ensure that archetypes can be combined to characterize single cases, (3) explicitly navigate levels of abstraction, and (4) obtain a fit between attribute configurations, theories, and empirical domains of validity. These criteria are based on a stocktaking of current methodological challenges in archetypes research, including: to demonstrate the validity of the analysis, delineate boundaries of archetypes, and select appropriate attributes to define them. We thus contribute to a better common understanding of the approach and to the improvement of the research design of future archetype analyses.
abstraction; archetype analysis; generalization; ideographic trap; interdisciplinary collaboration; panacea; pattern; research design; social-ecological systems; qualitative; quantitative; validity
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