方法对比

	Multi-Level Perspective on Transitions	Strategic Niche Management
领域	Science Technology Studies	Science Technology Studies
方法族	Process / pipeline	Process / pipeline
起源年份	2002	1998
提出者	Frank W. Geels (building on Arie Rip and René Kemp)	René Kemp, Johan Schot & Remco Hoogma
类型	Conceptual framework and analytic method for sociotechnical change	Conceptual framework and analytic method for managing innovation niches
开创性文献	Geels, F. W. (2002). Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case-study. Research Policy, 31(8-9), 1257-1274.	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.
别名	MLP, Multi-level perspective framework, Sociotechnical transitions analysis	SNM, Niche management approach, Protective space analysis
相关	4	4
摘要	The 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.