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A bibliometric model for identifying emerging research topics

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  • Qi Wang

Abstract

Detecting emerging research topics is essential, not only for research agencies but also for individual researchers. Previous studies have created various bibliographic indicators for the identification of emerging research topics. However, as indicated by Rotolo et al. (Research Policy 44, 1827–1843, ), the most serious problems are the lack of an acknowledged definition of emergence and incomplete elaboration of the linkages between the definitions that are used and the indicators that are created. With these issues in mind, this study first adjusts the definition of an emerging technology that Rotolo et al. () have proposed to accommodate the analysis. Next, a set of criteria for the identification of emerging topics is proposed according to the adjusted definition and attributes of emergence. Using two sets of parameter values, several emerging research topics are identified. Finally, evaluation tests are conducted by demonstration of the proposed approach and comparison with previous studies. The strength of the present methodology lies in the fact that it is fully transparent, straightforward, and flexible.

Suggested Citation

  • Qi Wang, 2018. "A bibliometric model for identifying emerging research topics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(2), pages 290-304, February.
    • Handle: RePEc:bla:jinfst:v:69:y:2018:i:2:p:290-304
    DOI: 10.1002/asi.23930
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    Cited by:

    1. Xu, Haiyun & Winnink, Jos & Yue, Zenghui & Zhang, Huiling & Pang, Hongshen, 2021. "Multidimensional Scientometric indicators for the detection of emerging research topics," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    2. David A. Pendlebury, 2019. "Charting a path between the simple and the false and the complex and unusable: Review of Henk F. Moed, Applied Evaluative Informetrics [in the series Qualitative and Quantitative Analysis of Scientifi," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 549-560, April.
    3. Lu, Kun & Yang, Guancan & Wang, Xue, 2022. "Topics emerged in the biomedical field and their characteristics," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    4. Xu, Shuo & Hao, Liyuan & An, Xin & Yang, Guancan & Wang, Feifei, 2019. "Emerging research topics detection with multiple machine learning models," Journal of Informetrics, Elsevier, vol. 13(4).
    5. Qi Wang & Tobias Jeppsson, 2022. "Identifying benchmark units for research management and evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7557-7574, December.
    6. Lara-Rodríguez, Juan Sebastián & Rojas-Contreras, Camilo & Duque Oliva, Edison Jair, 2019. "Discovering emerging research topics for brand personality: A bibliometric analysis," Australasian marketing journal, Elsevier, vol. 27(4), pages 261-272.
    7. Lucie Beranová & Marcin P. Joachimiak & Tomáš Kliegr & Gollam Rabby & Vilém Sklenák, 2022. "Why was this cited? Explainable machine learning applied to COVID-19 research literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2313-2349, May.
    8. Jiang, Man & Yang, Siluo & Gao, Qiang, 2024. "Multidimensional indicators to identify emerging technologies: Perspective of technological knowledge flow," Journal of Informetrics, Elsevier, vol. 18(1).
    9. Kiran Kaur & Kwan Hoong Ng & Ray Kemp & Yin Yee Ong & Zaharah Ramly & Ai Peng Koh, 2019. "Knowledge generation in the wake of the Fukushima Daiichi nuclear power plant disaster," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 149-169, April.
    10. Zamani, Mehdi & Yalcin, Haydar & Naeini, Ali Bonyadi & Zeba, Gordana & Daim, Tugrul U, 2022. "Developing metrics for emerging technologies: identification and assessment," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    11. Xu, Shuo & Hao, Liyuan & Yang, Guancan & Lu, Kun & An, Xin, 2021. "A topic models based framework for detecting and forecasting emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    12. Huang, Lu & Chen, Xiang & Ni, Xingxing & Liu, Jiarun & Cao, Xiaoli & Wang, Changtian, 2021. "Tracking the dynamics of co-word networks for emerging topic identification," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    13. Wooseok Jang & Yongtae Park & Hyeonju Seol, 2021. "Identifying emerging technologies using expert opinions on the future: A topic modeling and fuzzy clustering approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6505-6532, August.
    14. Xiaoyu Liu & Alan L. Porter, 2020. "A 3-dimensional analysis for evaluating technology emergence indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 27-55, July.
    15. Gao, Qiang & Liang, Zhentao & Wang, Ping & Hou, Jingrui & Chen, Xiuxiu & Liu, Manman, 2021. "Potential index: Revealing the future impact of research topics based on current knowledge networks," Journal of Informetrics, Elsevier, vol. 15(3).
    16. Ryosuke L. Ohniwa & Kunio Takeyasu & Aiko Hibino, 2022. "Researcher dynamics in the generation of emerging topics in life sciences and medicine," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(2), pages 871-884, February.
    17. Kwon, Seokbeom & Liu, Xiaoyu & Porter, Alan L. & Youtie, Jan, 2019. "Research addressing emerging technological ideas has greater scientific impact," Research Policy, Elsevier, vol. 48(9), pages 1-1.
    18. Sandra Rousseau & Ronald Rousseau, 2021. "Bibliometric Techniques And Their Use In Business And Economics Research," Journal of Economic Surveys, Wiley Blackwell, vol. 35(5), pages 1428-1451, December.
    19. Peter van den Besselaar & Ulf Sandström, 2019. "Measuring researcher independence using bibliometric data: A proposal for a new performance indicator," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-20, March.
    20. Zhentao Liang & Jin Mao & Kun Lu & Gang Li, 2021. "Finding citations for PubMed: a large-scale comparison between five freely available bibliographic data sources," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9519-9542, December.
    21. Peter Sjögårde & Fereshteh Didegah, 2022. "The association between topic growth and citation impact of research publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1903-1921, April.
    22. 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.
    23. Bornmann, Lutz & Haunschild, Robin, 2022. "Empirical analysis of recent temporal dynamics of research fields: Annual publications in chemistry and related areas as an example," Journal of Informetrics, Elsevier, vol. 16(2).

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