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Technology Life Cycle Bibliometrics/证据
方法证据记录

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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源记录

引文逐字复制自方法源记录。这些引文不代表任何层级的验证。

Technology Life Cycle Bibliometrics: S-Curve Maturity Analysis from Patent and Publication Counts
分类方法记录 · process-pipeline / bibliometrics
  • 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
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Same method familyDisruption Index (CD-Index)machine-suggested · Relational suggestion, not evidence.Same method familyPatent–Paper Citation Linkage (NPL)machine-suggested · Relational suggestion, not evidence.Same method familyTriple Helix Indicators (Mutual Information)machine-suggested · Relational suggestion, not evidence.

证据状态

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

来源

从方法源记录复制的 2 条记录的引文。

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