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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출처
- 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 ↗
- 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 ↗
이 페이지 인용 방법
ScholarGate. (2026, June 23). Technology Life Cycle Bibliometrics: S-Curve Maturity Analysis from Patent and Publication Counts. ScholarGate. https://scholargate.app/ko/bibliometrics/technology-life-cycle-bibliometrics
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이 방법을 가장 가까운 동류의 방법들과 나란히 놓고 비교해 보세요 — 라이브러리는 책을 펼쳐 놓을 뿐, 선택은 여러분의 몫입니다.
- Disruption Index (CD-Index)계량서지학↔ 비교
- Patent–Paper Citation Linkage (NPL)계량서지학↔ 비교
- Triple Helix Indicators (Mutual Information)계량서지학↔ 비교