Linganisha mbinu

	Patent–Paper Citation Linkage (NPL)	Technology Life Cycle Bibliometrics
Nyanja	Bibliometriki	Bibliometriki
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1997	1997
Mwanzilishi	Francis Narin, Kimberly S. Hamilton & Dominic Olivastro	Holger Ernst; Robert J. Watts & Alan L. Porter
Aina	Citation-linkage pipeline connecting patents to scientific literature	Forecasting pipeline mapping technologies to S-curve life-cycle stages
Chanzo asilia	Narin, F., Hamilton, K. S., & Olivastro, D. (1997). The increasing linkage between U.S. technology and public science. Research Policy, 26(3), 317-330.	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.
Majina mbadala	Science Linkage Analysis, Non-Patent Literature Analysis, NPL Citation Analysis, Patent-to-Science Citation Linkage	Technology Maturity Analysis, S-Curve Bibliometrics, Innovation Forecasting, Patent-Based Life Cycle Analysis
Zinazohusiana	3	3
Muhtasari	Patent–paper citation linkage measures how strongly technology draws on science by analyzing the non-patent literature, or NPL, references that appear on patents. When a patent cites a scientific journal article rather than another patent, it leaves a traceable thread connecting an invention to the research it built on. Francis Narin, Kimberly Hamilton, and Dominic Olivastro's landmark 1997 study traced these threads at national scale and found that the citation linkage between U.S. patents and scientific papers was growing rapidly, that the cited science was overwhelmingly public, authored in universities and government laboratories, and that this linkage offered a quantitative measure of the contribution of public science to industrial technology. The resulting science-linkage indicator distinguishes science-intensive technologies from incremental ones and underpins studies of how publicly funded research feeds private innovation.	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.