Forecasting by analogy using the web search traffic

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

Technological Forecasting and Social Change, 2017, vol. 115, issue C, 37-51

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.

Keywords: Analogical forecasting; Web search traffic; Product life cycle; Search cycle; Google trends; Naver trend (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:115:y:2017:i:c:p:37-51

DOI: 10.1016/j.techfore.2016.09.014

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