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Forecasting technology substitution based on hazard function

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  • Jeong, Yujin
  • Park, Inchae
  • Yoon, Byungun

Abstract

The failing to prepare for forecasting along with an excessive focus on incumbent technology may lead to failure in coping with emerging technology. This, in turn, results in entering the market late. Although identifying technology substitution in advance has become increasingly important, there have only been a few attempts to forecast technology substitution. These forecasts have only tried to explain the phenomenon of substitution by focusing on the diffusion of products. Therefore, this paper aims to suggest an approach to forecasting substitution from incumbent technology and emerging technology by applying a hazard rate that originates in equipment placement in reliability engineering. A candidate for emerging technology with a high possibility to substitute existing dominant technology is chosen first. Second, we have developed a model that includes the bathtub-curve to estimate the hazard rate for technologies. This model considers uncertainty, risk, and utility when developing technology. Based on the model, the hazard rate of the dominant technology is estimated by using patent data. After estimating the hazard rate in the form of the bathtub, inflection points and several factors will define an appropriate time point for the substitution. This proposed approach is a novel approach that applies the critical concepts in reliability engineering to technological substitution. Based on a mathematical theory, this method can be used as a decision-making tool for deciding when to develop a technology and apply for patents.

Suggested Citation

  • Jeong, Yujin & Park, Inchae & Yoon, Byungun, 2016. "Forecasting technology substitution based on hazard function," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 259-272.
    • Handle: RePEc:eee:tefoso:v:104:y:2016:i:c:p:259-272
    DOI: 10.1016/j.techfore.2016.01.014
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    References listed on IDEAS

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    Cited by:

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    2. Lee, Changyong & Kwon, Ohjin & Kim, Myeongjung & Kwon, Daeil, 2018. "Early identification of emerging technologies: A machine learning approach using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 291-303.
    3. Noh, Heeyong & Song, Young-Keun & Lee, Sungjoo, 2016. "Identifying emerging core technologies for the future: Case study of patents published by leading telecommunication organizations," Telecommunications Policy, Elsevier, vol. 40(10), pages 956-970.
    4. Yuan Zhou & Fang Dong & Yufei Liu & Liang Ran, 2021. "A deep learning framework to early identify emerging technologies in large-scale outlier patents: an empirical study of CNC machine tool," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 969-994, February.
    5. Cheng, Yu & Huang, Lucheng & Ramlogan, Ronnie & Li, Xin, 2017. "Forecasting of potential impacts of disruptive technology in promising technological areas: Elaborating the SIRS epidemic model in RFID technology," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 170-183.
    6. Zhang, Hao & Daim, Tugrul & Zhang, Yunqiu (Peggy), 2021. "Integrating patent analysis into technology roadmapping: A latent dirichlet allocation based technology assessment and roadmapping in the field of Blockchain," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    7. Pereira, Cristiano Gonçalves & Lavoie, Joao Ricardo & Garces, Edwin & Basso, Fernanda & Dabić, Marina & Porto, Geciane Silveira & Daim, Tugrul, 2019. "Forecasting of emerging therapeutic monoclonal antibodies patents based on a decision model," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 185-199.

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