Compara mètodes

	Triple Helix Indicators (Mutual Information)	Technology Life Cycle Bibliometrics
Camp	Bibliometria	Bibliometria
Família	Process / pipeline	Process / pipeline
Any d'origen	2003	1997
Autor original	Loet Leydesdorff	Holger Ernst; Robert J. Watts & Alan L. Porter
Tipus	Information-theoretic pipeline for university-industry-government dynamics	Forecasting pipeline mapping technologies to S-curve life-cycle stages
Font seminal	Leydesdorff, L. (2003). The mutual information of university-industry-government relations: An indicator of the Triple Helix dynamics. Scientometrics, 58(2), 445-467.	Ernst, H. (1997). The use of patent data for technological forecasting: the diffusion of CNC-technology in the machine tool industry. Small Business Economics, 9(4), 361-381.
Àlies	Triple Helix Mutual Information, University-Industry-Government Synergy Indicator, T(uig) Indicator, Triple Helix Synergy Analysis	Technology Maturity Analysis, S-Curve Bibliometrics, Innovation Forecasting, Patent-Based Life Cycle Analysis
Relacionats	3	3
Resum	Triple Helix indicators measure the interaction among universities, industry, and government in a knowledge-based innovation system using information theory. Building on the Triple Helix model of Henry Etzkowitz and Loet Leydesdorff, Leydesdorff proposed in 2003 that the three-way mutual information across these institutional dimensions provides a quantitative indicator of how much the three spheres jointly organize an innovation system. When this three-way mutual information is negative, it signals synergy and self-organization: knowing the values on any two dimensions tells you more about the third than their pairwise relations alone would suggest, a hallmark of an integrated, co-evolving system. Computed over publications, patents, or firm data tagged by geography, sector, and technology, the indicator lets analysts compare regions and nations on the strength of their university-industry-government coupling.	Technology life cycle bibliometrics uses time series of patent and publication counts to locate where a technology sits in its developmental life cycle and to forecast where it is headed. The core premise, developed by Holger Ernst for patent data and by Robert Watts and Alan Porter in their innovation-forecasting framework, is that technologies grow along an S-shaped curve: a slow emerging phase, a rapid growth phase, and a saturating maturity phase. By counting patenting or publishing activity over time and fitting a logistic curve, analysts can read off whether a technology is nascent, accelerating, or plateauing, and project its future trajectory. Watts and Porter combined such life-cycle indicators with contextual and value-chain measures into an enriched approach they called innovation forecasting, giving technology managers and policymakers an evidence-based way to time investment and anticipate competitive shifts.