This guide is meant to assist researchers seeking to apply Socio-Technical Configuration Analysis (STCA) by providing practical advice on how to get started.

The first chapter elaborates on the theoretical contexts in which socio-technical configurations, their dynamics and geographical variation play a key role and how this epistemological approach relates to well-established conceptual frameworks from innovation and transition studies. In STCA, statements or actions of actors that are reported in document stocks are aggregated into different forms of network or proximity map graphs, which can be interpreted as coherent storylines or strategies reflecting institutionalized socio-technical configurations shared by various actors. Shifts over time of these networks can then be interpreted as depicting transition dynamics, and comparisons across space as local variations of regime or innovation system structures. The introduction to the method provided in Chapter 1 provides a coherent terminology to help researchers navigate through the different steps and software programs. It furthermore elaborates on a typology of research problems that can be analyzed through STCA and an overview on the generic steps that a researcher has to conduct when applying the method.

The following chapters describe the process of identifying text material (Chapter 2), coding the data and exporting it (Chapter 3 and Chapter 4), transforming the data matrix (Chapter 5) and analyzing and visualizing the results (Chapter 6 and Chapter 7).

Under Resources we have collected R-scripts, recorded lectures, and listed publications based on STCA – to support and inspire potential use cases.

We are welcoming contributions – feedback on how to improve or simplify the steps presented in the guide is much appreciated. Get in touch with Johan Miörner.

To cite the guide, please refer to the working paper version of the introduction: 
Miörner J., Truffer T., Binz C., Heiberg J. & Yap X.-S. (2022) Guidebook for applying the Socio-Technical Configuration Analysis method. GEIST – Geography of Innovation and Sustainability Transitions 2022(01), GEIST Working Paper series.