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A modified gamma/Gompertz/NBD model for estimating technology lifetime

Author

Listed:
  • Myoungjae Choi

    (University of Science and Technology)

  • Sun-Hi Yoo

    (Korea Institute of Science and Technology Information)

  • Jongtaik Lee

    (Korea Institute of Science and Technology Information)

  • Jeongsub Choi

    (West Virginia University)

  • Byunghoon Kim

    (Hanyang University)

Abstract

For efficient research and development (R&D) management, estimating the economic value of patents is becoming increasingly necessary. When estimating the economic value of patents, technology lifetime is one of the most important factors to be considered. The Pareto/non-negative binomial distribution (NBD) model is a stochastic model that can estimate the technology lifetime based on patent citation data. However, the Pareto/NBD model has some limitations. First, the model assumes that the technology of a patent is active until it is cited by another patent even though the cited patent is expired. Second, the probability distribution of the technology lifetime for a patent group always has a mode of zero, which implies that patent technologies are immediately replaced by other technologies. To address these issues, we propose a more generalized method that estimates the technology lifetime of a patent based on a modified gamma Gompertz with NBD (G/G/NBD) model. We apply the proposed methodology to estimate the lifetime of US patents in three communication-related technology areas. The case study and sensitivity analysis showed reliable estimates by the proposed methodology, where technology lifetimes were estimated within the patents’ term based on the proposed model while the existing model often resulted in their estimated lifetime being greater than the patent term.

Suggested Citation

  • Myoungjae Choi & Sun-Hi Yoo & Jongtaik Lee & Jeongsub Choi & Byunghoon Kim, 2022. "A modified gamma/Gompertz/NBD model for estimating technology lifetime," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 5731-5751, October.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:10:d:10.1007_s11192-022-04489-1
    DOI: 10.1007/s11192-022-04489-1
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