Towards a modular and temporal understanding of system diffusion
Towards a modular and temporal understanding of system diffusion: Adoption models and socio-technical theories applied to Austrian biomass district-heating (1979–2013)
A new paper by Professor Frank Geels, Co-Director, The Centre on Innovation Demand (CIED) and Dr Victoria Johnson, Research Associate at the Sustainable Consumption Institute (SCI), University of Manchester looks at applying different theoretical models to understand the diffusion of biomass district heating systems in Austria. The main contribution of the paper is to analyse the diffusion of systems rather than single innovations; demonstrating that this process is best understood by combining insights from different adoption models and socio-technical theories. The paper ‘Towards a modular and temporal understanding of system diffusion: Adoption models and socio-technical theories applied to Austrian biomass district-heating (1979–2013)’ is available to download on the ScienceDirect website.
The diffusion of socio-technical systems is more complex than that of discrete products and cannot be understood solely with adoption models that have come to dominate the diffusion literature. The paper makes two contributions. First, it aims to broaden the conceptual repertoire by distinguishing two analytical families: adoption models and socio-technical theories of diffusion. We distinguish four adoption models (epidemic, rational choice, socio-psychological, increasing-returns-to-adoption) and three socio-technical models (system building, circulation/replication, societal embedding), and discuss their phenomenological characteristics and causal mechanisms. Second, the paper shows that system diffusion is a multi-dimensional process that is best understood with a modular approach that combines insights from different conceptual models. To demonstrate this second contribution and explore the temporal salience of different models, we apply them to the diffusion of Austrian biomass district heating (BMDH) systems (1979–2013). The paper ends with integrative suggestions by temporally positioning different diffusion models in a broader framework.