Frameworks for multi-system innovation analysis from a sociotechnical perspective

A systematic literature review

Journal Article (2024)
Author(s)

Jerico Bakhuis (TU Delft - Energy and Industry)

L.M. Kamp (TU Delft - Energy and Industry)

Natalia Barbour (University of Central Florida)

Emile J.L. Chappin (TU Delft - Energy and Industry)

Research Group
Energy and Industry
Copyright
© 2024 J.J. Bakhuis, L.M. Kamp, Natalia Barbour, E.J.L. Chappin
DOI related publication
https://doi.org/10.1016/j.techfore.2024.123266
More Info
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Publication Year
2024
Language
English
Copyright
© 2024 J.J. Bakhuis, L.M. Kamp, Natalia Barbour, E.J.L. Chappin
Research Group
Energy and Industry
Volume number
201
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Abstract

This paper systematically reviews the literature on sociotechnical multi-system innovation frameworks that broaden the usual focus on one sociotechnical system to encompass influences from multiple systems. The review includes 75 peer-reviewed papers that span a broad range of energy-demanding systems and mainly build upon the core frameworks of the Multi-level Perspective (MLP) and Technological Innovation Systems (TIS). The analysis identifies three key aspects to consider in multi-system frameworks. The first aspect is the importance of considering the overarching directionality of multiple sociotechnical systems and how they influence each other. The second is to explicitly analyse the phase of each transitioning system. The third aspect is a need for explicit system configuration analysis. This includes analysing the value chain and the number and types of sectors linked to it, typifying the distinct characteristics of sectors internally and how they interact, and analysing complementary or competitive technologies. The paper concludes by providing recommendations for future research, with a particular focus on the further development of new multi-system frameworks that include one or more of the prior-mentioned three key takeaways. Firstly, focusing on dynamics within multi-system niches. Secondly, performing actor-level analysis, including demand-side analysis. Finally, applying quantitative methods, such as computer simulation modelling.