Compare methods

	Technology Life Cycle Bibliometrics	Disruption Index (CD-Index)
Field	Bibliometrics	Bibliometrics
Family	Process / pipeline	Process / pipeline
Year of origin	1997	2017
Originator	Holger Ernst; Robert J. Watts & Alan L. Porter	Russell J. Funk & Jason Owen-Smith; Lingfei Wu, Dashun Wang & James A. Evans
Type	Forecasting pipeline mapping technologies to S-curve life-cycle stages	Citation-network pipeline for classifying contributions as disruptive or consolidating
Seminal source	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.	Funk, R. J., & Owen-Smith, J. (2017). A Dynamic Network Measure of Technological Change. Management Science, 63(3), 791-817.
Aliases	Technology Maturity Analysis, S-Curve Bibliometrics, Innovation Forecasting, Patent-Based Life Cycle Analysis	CD Index, Consolidation-Disruption Index, CD5 Index, Disruptiveness Measure
Related	3	3
Summary	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.	The disruption index, or CD index, classifies a scientific paper or patent by how the work that cites it treats the work it built on. Introduced by Russell Funk and Jason Owen-Smith in 2017 as a dynamic network measure of technological change, and popularized for science by Lingfei Wu, Dashun Wang, and James Evans in 2019, it asks a simple structural question: when later researchers cite a focal work, do they also keep citing that work's own references, or do they cite the focal work instead of its predecessors? If subsequent work cites the focal item but largely ignores its references, the item has disrupted its field, eclipsing what came before; if subsequent work cites both the item and its references together, the item has consolidated existing knowledge. The index runs from -1 (purely consolidating) to +1 (purely disrupting) and has become a standard tool for measuring whether contributions push science in new directions or deepen established lines.