IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v91y2012i1d10.1007_s11192-011-0550-3.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-011-0550-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11192-011-0550-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Luís M. A. Bettencourt & David I. Kaiser & Jasleen Kaur & Carlos Castillo-Chávez & David E. Wojick, 2008. "Population modeling of the emergence and development of scientific fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(3), pages 495-518, June.
    2. Peng Hui Lv & Gui-Fang Wang & Yong Wan & Jia Liu & Qing Liu & Fei-cheng Ma, 2011. "Bibliometric trend analysis on global graphene research," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 399-419, August.
    3. Woo Hyoung Lee, 2008. "How to identify emerging research fields using scientometrics: An example in the field of Information Security," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(3), pages 503-525, September.
    4. Shaodong Xie & Jing Zhang & Yuh-Shan Ho, 2008. "Assessment of world aerosol research trends by bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 77(1), pages 113-130, October.
    5. Catherine Lecocq & Bart Looy, 2009. "The impact of collaboration on the technological performance of regions: time invariant or driven by life cycle dynamics?," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(3), pages 845-865, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Shi, Yuwei & Herniman, John, 2023. "The role of expectation in innovation evolution: Exploring hype cycles," Technovation, Elsevier, vol. 119(C).
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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).
    10. 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.
    11. 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.
    12. 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.
    13. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shuo Xu & Liyuan Hao & Xin An & Hongshen Pang & Ting Li, 2020. "Review on emerging research topics with key-route main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 607-624, January.
    2. Krzysztof Klincewicz, 2016. "The emergent dynamics of a technological research topic: the case of graphene," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(1), pages 319-345, January.
    3. Hanning Guo & Scott Weingart & Katy Börner, 2011. "Mixed-indicators model for identifying emerging research areas," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 421-435, October.
    4. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    5. Goio Etxebarria & Mikel Gomez-Uranga & Jon Barrutia, 2012. "Tendencies in scientific output on carbon nanotubes and graphene in global centers of excellence for nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(1), pages 253-268, April.
    6. Bhagaban Behera, 2013. "Drug Trafficking as a Non-Traditional Security Threat to Central Asian States," Jadavpur Journal of International Relations, , vol. 17(2), pages 229-251, December.
    7. Minchul Lee & Min Song, 2020. "Incorporating citation impact into analysis of research trends," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1191-1224, August.
    8. Zifeng Chen & Jiancheng Guan, 2011. "Mapping of biotechnology patents of China from 1995–2008," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 73-89, July.
    9. Rolfe, John & Flint, Nicole, 2018. "Assessing the economic benefits of a tourist access road: A case study in regional coastal Australia," Economic Analysis and Policy, Elsevier, vol. 58(C), pages 167-178.
    10. Chang-Ping Hu & Ji-Ming Hu & Sheng-Li Deng & Yong Liu, 2013. "A co-word analysis of library and information science in China," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(2), pages 369-382, November.
    11. Joaquín M. Azagra-Caro, 2012. "Access to universities’ public knowledge: who’s more nationalist?," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 671-691, June.
    12. Yuh-Shan Ho, 2016. "Rebuttal to: Liu et al. “Progress in global parallel computing research: a bibliometric approach”, vol. 95, pp 967–983," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1693-1694, September.
    13. Xuefeng Wang & Shuo Zhang & Yuqin liu, 2022. "ITGInsight–discovering and visualizing research fronts in the scientific literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6509-6531, November.
    14. John McLevey & Alexander V. Graham & Reid McIlroy-Young & Pierson Browne & Kathryn S. Plaisance, 2018. "Interdisciplinarity and insularity in the diffusion of knowledge: an analysis of disciplinary boundaries between philosophy of science and the sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 331-349, October.
    15. Kim, Hyoungshick & Yoon, Ji Won & Crowcroft, Jon, 2012. "Network analysis of temporal trends in scholarly research productivity," Journal of Informetrics, Elsevier, vol. 6(1), pages 97-110.
    16. Hsia-Ching Chang, 2016. "The Synergy of Scientometric Analysis and Knowledge Mapping with Topic Models: Modelling the Development Trajectories of Information Security and Cyber-Security Research," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 15(04), pages 1-33, December.
    17. Jimi Adams & Ryan Light, 2014. "Mapping Interdisciplinary Fields: Efficiencies, Gaps and Redundancies in HIV/AIDS Research," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-13, December.
    18. Alba Santa Soriano & Carolina Lorenzo Álvarez & Rosa María Torres Valdés, 2018. "Bibliometric analysis to identify an emerging research area: Public Relations Intelligence—a challenge to strengthen technological observatories in the network society," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(3), pages 1591-1614, June.
    19. Shanwu Tian & Xiurui Xu & Ping Li, 2021. "Acknowledgement network and citation count: the moderating role of collaboration network," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7837-7857, September.
    20. Jiang Tan & Hui-Zhen Fu & Yuh-Shan Ho, 2014. "A bibliometric analysis of research on proteomics in Science Citation Index Expanded," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 1473-1490, February.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:91:y:2012:i:1:d:10.1007_s11192-011-0550-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.