New methodological inroads to regional path development – Epistemological reflections on the contribution of semantic network analysis

by Bernhard Truffer

Evolutionary thinking has provided very potent explanations for regional industrial path development in the past decade. Recent commentators argued for extending the originally rather narrow focus on preexisting knowledge stocks to include institutional dimensions, system resource build up, and the agentic shaping of industrial pathways. On an epistemological level, such conceptual enlargements require the bridging of quantitative variance explanations and qualitative process explanations, which few scholars have successfully managed to do. In the present paper, I will argue that developments in the rapidly expanding field of semantic network analysis might improve rigor in qualitative process reconstructions and by this be more easily relatable to established quantitative approaches in evolutionary economic geography. Semantic networks enable the systematic reconstruction of higher order analytical constructs based on the analysis of statements and actions of actors as reported in collections of text documents. More specifically, we will introduce the socio-technical configuration analysis (STCA) method, recently developed in the scholarly field of sustainability transition studies, and show how it can inform regional path development research. An illustrative empirical case will analyze the path development dynamics in the German federal state of Baden-Württemberg, a global leader in automobile manufacturing, in the wake of the global challenge of electric cars. I conclude with wider ramifications of semantic network approaches for economic geography research and how it can be leveraged in mixed method designs.

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Cite as: Truffer (2024) New methodological inroads to regional path development – Epistemological reflections on the contribution of semantic network analysis. GEIST – Geography of Innovation and Sustainability Transitions, 2024(05), GEIST Working Paper series.