Comparar métodos

	Technological Innovation Systems	Innovation System Functions Analysis
Área	Science Technology Studies	Science Technology Studies
Família	Process / pipeline	Process / pipeline
Ano de origem	2008	2007
Autor original	Anna Bergek, Staffan Jacobsson, Bo Carlsson and colleagues	Marko Hekkert, Roald Suurs and colleagues
Tipo	Systems-of-innovation framework and scheme of analysis	Functional analysis framework with event-history method
Fonte seminal	Bergek, A., Jacobsson, S., Carlsson, B., Lindmark, S., & Rickne, A. (2008). Analyzing the functional dynamics of technological innovation systems: a scheme of analysis. Research Policy, 37(3), 407-429.	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.
Outros nomes	TIS analysis, Technological innovation system approach, Functional dynamics scheme	Functions of innovation systems, Seven functions approach, Event-history innovation analysis
Relacionados	4	4
Resumo	Technological Innovation Systems (TIS) analysis studies the emergence, growth, and performance of a specific technology by treating it as a system of actors, networks, and institutions that interact to generate, diffuse, and use that technology. Building on the systems-of-innovation tradition, the influential scheme of Bergek and colleagues combines a structural account of the system's components with a functional analysis of the key processes—or functions—that an innovation system must perform, then compares achieved with desired functionality to diagnose inducement and blocking mechanisms and derive policy.	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.