Design and quality criteria for archetype analysis

Abstract

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.

Mapping social-ecological systems archetypes

“…Our study also contributes to a growing literature characterizing systems archetypes in SES [14,15,40]. The concept of archetype, first introduced by Forrester [47], refers to canonical structures or causal building blocks to many dynamical systems and their managerial problems.…”

“…Clustering SES Identifying SES archetypes from data is in essence a clustering problem, that is, a classification task of multiple elements by some measure of similarity. Identifying archetypes or systems' typologies is useful because it allows comparison between different cases with similar profiles, they reduce dimensionality, and facilitate extrapolation between cases with similar characteristics [14,15]. Numerous methods exist to perform clustering, but before explaining the details of our choices, first we present a brief overview of what others have done when classifying SES and how our work improves previous efforts.…”

Archetype analysis in sustainability research: meanings, motivations, and evidence-based policy making

“…The appropriate degree of abstraction is usually unknown at the beginning of a study. It depends on the research purpose and the methodology (Eisenack et al 2019). For example, in metaanalyses of case studies, the translation of case study results into the language of the analytical framework during the iterative development of the codebook determines the semantic abstraction of attributes (van Vliet et al 2016, Dell'Angelo et al 2017b.…”

“…We refer to method-specific literature on how to deal with those challenges. For quality criteria that hold across methods, we refer to Eisenack et al (2019).…”

“…Archetypes are analyzed for an increasing range of topics in sustainability research, including land systems, governance and institutional change, social-ecological systems, and global change (Eisenack et al 2019;Sietz, Frey, Roggero, et al unpublished manuscript). For example, "archetypes of vulnerability" depict recurrent configurations of factors that generate vulnerability to socioeconomic and ecological change at global, regional, and local scales (Jäger et al 2007, Vidal Merino et al 2019).…”