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Technological Innovation Systems×Innovation System Functions Analysis×
분야Science Technology StudiesScience Technology Studies
계열Process / pipelineProcess / pipeline
기원 연도20082007
창시자Anna Bergek, Staffan Jacobsson, Bo Carlsson and colleaguesMarko Hekkert, Roald Suurs and colleagues
유형Systems-of-innovation framework and scheme of analysisFunctional analysis framework with event-history method
원전Bergek, 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. DOI ↗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. DOI ↗
별칭TIS analysis, Technological innovation system approach, Functional dynamics schemeFunctions of innovation systems, Seven functions approach, Event-history innovation analysis
관련44
요약Technological 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.
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