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Technology Forecasting Using a Diffusion Model Incorporating Replacement Purchases

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  • Chul-Yong Lee

    (Korea Energy Economics Institute (KEEI), 405-11 Jongga-ro, Jung-gu, Ulsan 44543, Korea)

  • Sung-Yoon Huh

    (Haas School of Business, University of California Berkeley, 2220 Piedmont Avenue, Berkeley, CA 94720, USA)

Abstract

Understanding the nature of the diffusion process is crucial for sustainable development of a new technology and product. This study introduces a replacement diffusion model that leads to a better understanding of the growth dynamics of a technology. The model operates in an environment with multiple competitors and overcomes the limitations of existing models. The model (1) consists of a diffusion model and an additional time series model; (2) separately identifies the diffusion of first-time purchases and that of replacement purchases; (3) incorporates players’ marketing-mix variables, affecting a new technology diffusion; and (4) characterizes consumers’ different replacement cycles. The proposed model is applied to South Korea’s mobile handset market. The model performs well in terms of its fit and forecasting capability when compared with other diffusion models incorporating replacement and repeat purchases. The usefulness of the model stems from its ability to describe complicated environments and its flexibility in including multiple factors that drives diffusion in the regression analysis.

Suggested Citation

  • Chul-Yong Lee & Sung-Yoon Huh, 2017. "Technology Forecasting Using a Diffusion Model Incorporating Replacement Purchases," Sustainability, MDPI, vol. 9(6), pages 1-14, June.
  • Handle: RePEc:gam:jsusta:v:9:y:2017:i:6:p:1038-:d:101654
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    References listed on IDEAS

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    Cited by:

    1. Saurabh Panwar & P. K. Kapur & Ompal Singh, 2021. "Technology diffusion model with change in adoption rate and repeat purchases: a case of consumer balking," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(1), pages 29-36, February.
    2. Aslan Lotfi & Zhengrui Jiang & Ali Lotfi & Dipak C. Jain, 2023. "Estimating Life Cycle Sales of Technology Products with Frequent Repeat Purchases: A Fractional Calculus-Based Approach," Information Systems Research, INFORMS, vol. 34(2), pages 409-422, June.
    3. Li, Xishu & Yin, Ying & Manrique, David Vergara & Bäck, Thomas, 2021. "Lifecycle forecast for consumer technology products with limited sales data," International Journal of Production Economics, Elsevier, vol. 239(C).
    4. Yoo Hwan Lee & Young Wook Seo, 2018. "Strategies for Sustainable Business Development: Utilizing Consulting and Innovation Activities," Sustainability, MDPI, vol. 10(11), pages 1-19, November.

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