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Multi-Level Perspective on Transitions×Strategic Niche Management×
FagområdeScience Technology StudiesScience Technology Studies
FamilieProcess / pipelineProcess / pipeline
Oprindelsesår20021998
OphavspersonFrank W. Geels (building on Arie Rip and René Kemp)René Kemp, Johan Schot & Remco Hoogma
TypeConceptual framework and analytic method for sociotechnical changeConceptual framework and analytic method for managing innovation niches
Oprindelig kildeGeels, F. W. (2002). Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case-study. Research Policy, 31(8-9), 1257-1274. DOI ↗Kemp, R., Schot, J., & Hoogma, R. (1998). Regime shifts to sustainability through processes of niche formation: the approach of strategic niche management. Technology Analysis & Strategic Management, 10(2), 175-198. DOI ↗
AliasserMLP, Multi-level perspective framework, Sociotechnical transitions analysisSNM, Niche management approach, Protective space analysis
Relaterede44
ResuméThe Multi-Level Perspective (MLP) is a middle-range framework for analysing how large sociotechnical systems—energy, mobility, food, water—shift from one dominant configuration to another. It locates change in the interplay of three analytic levels: protected niches where radical novelties incubate, the incumbent sociotechnical regime that structures ordinary practice, and a slow-moving exogenous landscape. Transitions occur when landscape pressures destabilise the regime and open windows of opportunity for maturing niche innovations to break through.Strategic Niche Management (SNM) is a framework for understanding and supporting the early development of radical, sustainable innovations by nurturing them in protected spaces—niches—shielded from the full selection pressure of the prevailing market and regime. It zooms into the niche level of transition theory and identifies three internal processes that determine whether an innovation gathers momentum: the articulation of expectations and visions, the building of broad social networks, and learning across multiple dimensions through real-world experiments.
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