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Forecasting new product diffusion using both patent citation and web search traffic

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  • Won Sang Lee
  • Hyo Shin Choi
  • So Young Sohn

Abstract

Accurate demand forecasting for new technology products is a key factor in the success of a business. We propose a way to forecasting a new product’s diffusion through technology diffusion and interest diffusion. Technology diffusion and interest diffusion are measured by the volume of patent citations and web search traffic, respectively. We apply the proposed method to forecast the sales of hybrid cars and industrial robots in the US market. The results show that that technology diffusion, as represented by patent citations, can explain long-term sales for hybrid cars and industrial robots. On the other hand, interest diffusion, as represented by web search traffic, can help to improve the predictability of market sales of hybrid cars in the short-term. However, interest diffusion is difficult to explain the sales of industrial robots due to the different market characteristics. Finding indicates our proposed model can relatively well explain the diffusion of consumer goods.

Suggested Citation

  • Won Sang Lee & Hyo Shin Choi & So Young Sohn, 2018. "Forecasting new product diffusion using both patent citation and web search traffic," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-12, April.
  • Handle: RePEc:plo:pone00:0194723
    DOI: 10.1371/journal.pone.0194723
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    References listed on IDEAS

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    Cited by:

    1. David Lenz & Peter Winker, 2020. "Measuring the diffusion of innovations with paragraph vector topic models," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-18, January.
    2. Jinah Yang & Daiki Min & Jeenyoung Kim, 2020. "The Use of Big Data and Its Effects in a Diffusion Forecasting Model for Korean Reverse Mortgage Subscribers," Sustainability, MDPI, vol. 12(3), pages 1-17, January.
    3. Hyoung Jun Kim & Su Jung Jee & So Young Sohn, 2021. "Cost–benefit model for multi-generational high-technology products to compare sequential innovation strategy with quality strategy," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-17, April.
    4. Chuan Zhang & Yu-Xin Tian & Ling-Wei Fan, 2020. "Improving the Bass model’s predictive power through online reviews, search traffic and macroeconomic data," Annals of Operations Research, Springer, vol. 295(2), pages 881-922, December.

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