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Process / pipelineScientometrics / technology forecasting

Technology Life Cycle Bibliometrics

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.

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Bronnen

  1. 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. DOI: 10.1023/A:1007921808138
  2. Watts, R. J., & Porter, A. L. (1997). Innovation forecasting. Technological Forecasting and Social Change, 56(1), 25-47. DOI: 10.1016/S0040-1625(97)00050-4

Deze pagina citeren

ScholarGate. (2026, June 23). Technology Life Cycle Bibliometrics: S-Curve Maturity Analysis from Patent and Publication Counts. ScholarGate. https://scholargate.app/nl/bibliometrics/technology-life-cycle-bibliometrics

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Geciteerd door

ScholarGateTechnology Life Cycle Bibliometrics (Technology Life Cycle Bibliometrics: S-Curve Maturity Analysis from Patent and Publication Counts). Geraadpleegd op 2026-06-24 via https://scholargate.app/nl/bibliometrics/technology-life-cycle-bibliometrics · Gegevensset: https://doi.org/10.5281/zenodo.20539026