방법 비교

	Strategic Niche Management	Innovation System Functions Analysis
분야	Science Technology Studies	Science Technology Studies
계열	Process / pipeline	Process / pipeline
기원 연도	1998	2007
창시자	René Kemp, Johan Schot & Remco Hoogma	Marko Hekkert, Roald Suurs and colleagues
유형	Conceptual framework and analytic method for managing innovation niches	Functional analysis framework with event-history method
원전	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.	Hekkert, M. P., Suurs, R. A. A., Negro, S. O., Kuhlmann, S., & Smits, R. E. H. M. (2007). Functions of innovation systems: a new approach for analysing technological change. Technological Forecasting and Social Change, 74(4), 413-432.
별칭	SNM, Niche management approach, Protective space analysis	Functions of innovation systems, Seven functions approach, Event-history innovation analysis
관련	4	4
요약	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.	Functions of Innovation Systems analysis explains technological change by examining how well an innovation system performs seven key functions—entrepreneurial activities, knowledge development, knowledge diffusion, guidance of the search, market formation, resource mobilisation, and the creation of legitimacy. Associated with Hekkert, Suurs, and colleagues at Utrecht, the approach operationalises these functions through event-history analysis: a chronological dataset of innovation events is coded, functional performance is tracked over time, and the reinforcing feedback loops—the 'motors' of cumulative causation—that drive a system's rise or stagnation are identified.