Rethinking the forecasting of innovation diffusion

A combined actor- and system approach

Dados Bibliográficos

ID21402593
AutoresHanna Cardol (Chalmers University of Technology, autor correspondente), Ingrid Mignon (0000-0002-3134-4442, Chalmers University of Technology), Björn Lantz (0000-0001-6816-582X, Chalmers University of Technology)
Ano2025
Volume214
Páginas124058
Data de publicação2025-05-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoTechnological Forecasting and Social Change (JOURNAL)
Identificadores do periódicoISSN: 0040-1625 • E-ISSN: 1873-5509
EditoraElsevier BV (PUBLISHER)
DOI10.1016/j.techfore.2025.124058
OpenAlexW4407841817
IdiomaEN
Citações recebidas3
Referências citadas114

Technological forecasting has significantly expanded over the last decades, leading to widespread use of forecasting models for explaining technology adoption and diffusion of innovation. While these models are broadly used, they have faced criticism for narrowing the explanatory components of adoption, focusing on adopters, innovation characteristics, or environmental factors, but seldom combine these to address complex problems holistically. This paper aims to combine actor- and system perspectives on innovation diffusion with the intention to broaden the explanatory power of traditional forecasting models. The study focuses on the case of solar photovoltaic (PV) diffusion in Sweden, surveying 46,507 residential PV adopters that applied for the capital subsidy program between 2009 and 2021 about their adoption satisfaction. Findings suggest that traditional models primarily account for direct effects on adoption satisfaction, whereas incorporating system-level factors captures indirect effects, providing a more comprehensive understanding of technology adoption. This highlights the interplay between actor- and system-level factors and acknowledging the holistic nature of innovation diffusion, which can inform future forecasting practices. • Traditional forecasting models are lacking a system-perspective • We broaden traditional forecasting models combining actor- and system-perspectives • Traditional models primarily account for direct effects on technology adoption • Incorporating system-level factors captures indirect effects on technology adoption

Diffusion · Innovation diffusion · Knowledge management · Physics · Technology forecasting · Thermodynamics · Artificial Intelligence · Computer Science · Economic Growth and Productivity · Energy, Environment, Economic Growth · Innovation Diffusion and Forecasting

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