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A study of the method using search traffic to analyze new technology adoption

Author

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  • Jun, Seung-Pyo
  • Yeom, Jaeho
  • Son, Jong-Ku

Abstract

Various types of indices have been developed and applied for the purpose of identifying emergent technologies and forecasting their adoption. Recently, researchers have proposed search traffic analysis as a new method for tracking changes among consumers and utilizing this information to conduct further market research. Now with the onset of big data era, various attempts are being made to analyze the immense body of information made available by hidden traces left behind by consumers. In the same vein, our present study seeks to draw attention to the analytical advantages of utilizing search traffic. In this study, we use search traffic to analyze the adoption process of a new technology, in this case the technology of hybrid cars, for the purpose of verifying the potential value of conducting adoption analysis based on search traffic and we also propose a more refined method of analysis. First, we undertook to examine the keyword unit used in the searches, in order to refine our analysis of search traffic and thereby obtain greater practical utility. This was accomplished by comparing technology searches that specified the technology name with searches that specified the brand name. For each respective case, we also performed comparative analyses examining instances in which consumers simultaneously included the representative attributes of a product in their search.

Suggested Citation

  • Jun, Seung-Pyo & Yeom, Jaeho & Son, Jong-Ku, 2014. "A study of the method using search traffic to analyze new technology adoption," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 82-95.
    • Handle: RePEc:eee:tefoso:v:81:y:2014:i:c:p:82-95
    DOI: 10.1016/j.techfore.2013.02.007
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    Citations

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    Cited by:

    1. Malyy, Maksim & Tekic, Zeljko & Podladchikova, Tatiana, 2021. "The value of big data for analyzing growth dynamics of technology-based new ventures," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    2. 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).
    3. Xiaodong Yuan & Weiling Song, 2022. "Evaluating technology innovation capabilities of companies based on entropy- TOPSIS: the case of solar cell companies," Information Technology and Management, Springer, vol. 23(2), pages 65-76, June.
    4. Erdmann, Anett & Ponzoa, José M., 2021. "Digital inbound marketing: Measuring the economic performance of grocery e-commerce in Europe and the USA," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    5. Dhar, Suparna & Tarafdar, Pratik & Bose, Indranil, 2022. "Understanding the evolution of an emerging technological paradigm and its impact: The case of Digital Twin," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    6. Min-Seung Kim & Chan-Ho Lee & Ji-Hye Choi & Yong-Ju Jang & Jeong-Hee Lee & Jaesik Lee & Tae-Eung Sung, 2021. "A Study on Intelligent Technology Valuation System: Introduction of KIBO Patent Appraisal System II," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
    7. Dedehayir, Ozgur & Steinert, Martin, 2016. "The hype cycle model: A review and future directions," Technological Forecasting and Social Change, Elsevier, vol. 108(C), pages 28-41.
    8. Mirzadeh Phirouzabadi, Amir & Blackmore, Karen & Savage, David & Juniper, James, 2022. "Modelling and simulating a multi-modal and multi-dimensional technology interaction framework: The case of vehicle powertrain technologies in the US market," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    9. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    10. 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.
    11. Attié, Elodie & Meyer-Waarden, Lars, 2022. "The acceptance and usage of smart connected objects according to adoption stages: an enhanced technology acceptance model integrating the diffusion of innovation, uses and gratification and privacy ca," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    12. Jun, Seung-Pyo & Park, Do-Hyung, 2016. "Consumer information search behavior and purchasing decisions: Empirical evidence from Korea," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 97-111.
    13. Stieler, Maximilian & Munk, Alexander, 2020. "Wie man Hypes antizipiert und für sich nutzen kann - Die Additive Fertigung bei KSB," Marketing Review St.Gallen, Universität St.Gallen, Institut für Marketing und Customer Insight, vol. 37(6), pages 46-53.
    14. Jun, Seung-Pyo & Yoo, Hyoung Sun & Kim, Ji-Hui, 2016. "A study on the effects of the CAFE standard on consumers," Energy Policy, Elsevier, vol. 91(C), pages 148-160.
    15. Jun, Seung-Pyo & Park, Do-Hyung & Yeom, Jaeho, 2014. "The possibility of using search traffic information to explore consumer product attitudes and forecast consumer preference," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 237-253.
    16. 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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