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Forecasting by analogy using the web search traffic

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  • Jun, Seung-Pyo
  • Sung, Tae-Eung
  • Park, Hyun-Woo

Abstract

Various types of demand forecasting methods have been developed and utilized to predict the adoption of new technologies. Recently, along with the advancement of bibliometrics, there have been particularly active attempts to forecast life cycles using technology documents such as news, paper publications, patents, etc. The present study uses web search traffic to forecast by analogy, which has newly emerged as a method of empirically verifying the life cycle of either a product or a technology. So as to explore the potential of the analogical forecasting method using search traffic, we compare the trends of changes in the life cycle with those of search traffic and compare aspects of the search traffic exhibited by both U.S. and Korean consumers over various products. The study results revealed that search traffic trends tended to precede the adoption of a new product; however it accounted for the trends of adoption over the full life-cycle very accurately. In addition, statistically significant relationships have been observed in the search traffic for the same technology even when the traffic originated from distinct nations, languages and web search engines. From the results therein, we judged that the search traffic-based, analogical forecast method would be effective, and applied it to a case for estimating the Korean Plug-in Hybrid Electric Vehicle (PHEV) market. The most significant contribution of this study is that it presents the potential of utilizing search traffic as a new dimension for forecasting by analogy.

Suggested Citation

  • Jun, Seung-Pyo & Sung, Tae-Eung & Park, Hyun-Woo, 2017. "Forecasting by analogy using the web search traffic," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 37-51.
  • Handle: RePEc:eee:tefoso:v:115:y:2017:i:c:p:37-51
    DOI: 10.1016/j.techfore.2016.09.014
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    3. Jun, Seung-Pyo & Yoo, Hyoung Sun & Lee, Jae-Seong, 2021. "The impact of the pandemic declaration on public awareness and behavior: Focusing on COVID-19 google searches," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    4. Daekook Kang, 2021. "Box-office forecasting in Korea using search trend data: a modified generalized Bass diffusion model," Electronic Commerce Research, Springer, vol. 21(1), pages 41-72, March.
    5. Anca Elena-Bucea & Frederico Cruz-Jesus & Tiago Oliveira & Pedro Simões Coelho, 2021. "Assessing the Role of Age, Education, Gender and Income on the Digital Divide: Evidence for the European Union," Information Systems Frontiers, Springer, vol. 23(4), pages 1007-1021, August.
    6. Christian Ulrich & Benjamin Frieske & Stephan A. Schmid & Horst E. Friedrich, 2022. "Monitoring and Forecasting of Key Functions and Technologies for Automated Driving," Forecasting, MDPI, vol. 4(2), pages 1-24, May.
    7. Bodo Herzog & Lana dos Santos, 2021. "Google Search in Exchange Rate Models: Hype or Hope?," JRFM, MDPI, vol. 14(11), pages 1-40, October.
    8. Chumnumpan, Pattarin & Shi, Xiaohui, 2019. "Understanding new products’ market performance using Google Trends," Australasian marketing journal, Elsevier, vol. 27(2), pages 91-103.
    9. 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.
    10. Anca Elena-Bucea & Frederico Cruz-Jesus & Tiago Oliveira & Pedro Simões Coelho, 0. "Assessing the Role of Age, Education, Gender and Income on the Digital Divide: Evidence for the European Union," Information Systems Frontiers, Springer, vol. 0, pages 1-15.
    11. Jun, Seung-Pyo & Yoo, Hyoung Sun & Choi, San, 2018. "Ten years of research change using Google Trends: From the perspective of big data utilizations and applications," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 69-87.

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