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An empirical study of users’ hype cycle based on search traffic: the case study on hybrid cars

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  • Seung-Pyo Jun

    (Korea Institute of Science and Technology Information)

Abstract

Many forms of technology cycle models have been developed and utilized to identify new/convergent technologies and forecast social changes, and among these, the technology hype cycle introduced by Gartner has become established as an effective method that is widely utilized in the field. Despite the popularity of this commonly deployed model, however, the currently existing research literature fails to provide sufficient consideration of its theoretical frame or its empirical verification. This paper presents a new method for the empirical measurement of this hype cycle model. In particular, it presents a method for measuring the hype of the users rather than the hype cycle generated by research activities or by the media by means of analyzing the hype cycle using search traffic analysis. The analytical results derived from the case study of hybrid automobiles empirically demonstrated that following the introductory stage and the early growth stage of the life cycle, the positive hype curve and the negative hype curve, the representative figures of the hype cycle, were present in the bell curve for the users’ search behavior. Based on this finding, this paper proposes a new method for measuring the users’ expectation and suggests a new direction for future research that enables the forecasting of promising technologies and technological opportunities in linkage with the conventional technology life cycle model. In particular, by interpreting the empirical results using the consumer behavior model and the adoption model, this study empirically demonstrates that the characteristics of each user category can be identified through differences in the hype cycle in the process of the diffusion of new technological products discussed in the past.

Suggested Citation

  • Seung-Pyo Jun, 2012. "An empirical study of users’ hype cycle based on search traffic: the case study on hybrid cars," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(1), pages 81-99, April.
  • Handle: RePEc:spr:scient:v:91:y:2012:i:1:d:10.1007_s11192-011-0550-3
    DOI: 10.1007/s11192-011-0550-3
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    References listed on IDEAS

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    Cited by:

    1. Woondong Yeo & Seonho Kim & Byoung-Youl Coh & Jaewoo Kang, 2013. "A quantitative approach to recommend promising technologies for SME innovation: a case study on knowledge arbitrage from LCD to solar cell," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(2), pages 589-604, August.
    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. 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.
    4. Marco Campani & Ruggero Vaglio, 2015. "A simple interpretation of the growth of scientific/technological research impact leading to hype-type evolution curves," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(1), pages 75-83, April.
    5. 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.
    6. 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.
    7. White, Gareth R.T. & Samuel, Anthony, 2019. "Programmatic Advertising: Forewarning and avoiding hype-cycle failure," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 157-168.
    8. Shi, Yuwei & Herniman, John, 2023. "The role of expectation in innovation evolution: Exploring hype cycles," Technovation, Elsevier, vol. 119(C).
    9. Yuan Zhou & Fang Dong & Yufei Liu & Zhaofu Li & JunFei Du & Li Zhang, 2020. "Forecasting emerging technologies using data augmentation and deep learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 1-29, April.
    10. Lyons, Glenn & Davidson, Cody, 2016. "Guidance for transport planning and policymaking in the face of an uncertain future," Transportation Research Part A: Policy and Practice, Elsevier, vol. 88(C), pages 104-116.
    11. 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.
    12. 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.
    13. 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.

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    More about this item

    Keywords

    Hype cycle model; Search traffic; Hybrid car; Users’ hype cycle; Google trends;
    All these keywords.

    JEL classification:

    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making

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