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Scientific influence is not always visible: The phenomenon of under-cited influential publications

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  • Hu, Xiaojun
  • Rousseau, Ronald

Abstract

In this submission we introduce the notion of under-cited influential publications and show that these publications are like “wake-up switches” for significant follow-up research. To be considered an under-cited influential article we require an article to meet three requirements. One is on the level of received number of citations (first generation citations), while the two other ones take subsequent citation generations into account. In general terms these three conditions are:

Suggested Citation

  • Hu, Xiaojun & Rousseau, Ronald, 2016. "Scientific influence is not always visible: The phenomenon of under-cited influential publications," Journal of Informetrics, Elsevier, vol. 10(4), pages 1079-1091.
    • Handle: RePEc:eee:infome:v:10:y:2016:i:4:p:1079-1091
    DOI: 10.1016/j.joi.2016.10.002
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    Cited by:

    1. Chao Min & Qingyu Chen & Erjia Yan & Yi Bu & Jianjun Sun, 2021. "Citation cascade and the evolution of topic relevance," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(1), pages 110-127, January.
    2. Liang, Guoqiang & Hou, Haiyan & Ding, Ying & Hu, Zhigang, 2020. "Knowledge recency to the birth of Nobel Prize-winning articles: Gender, career stage, and country," Journal of Informetrics, Elsevier, vol. 14(3).
    3. Xiaojun Hu & Ronald Rousseau, 2017. "Nobel Prize winners 2016: Igniting or sparking foundational publications?," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 1053-1063, February.
    4. Lyu, Haihua & Bu, Yi & Zhao, Zhenyue & Zhang, Jiarong & Li, Jiang, 2022. "Citation bias in measuring knowledge flow: Evidence from the web of science at the discipline level," Journal of Informetrics, Elsevier, vol. 16(4).
    5. Bei Zeng & Haihua Lyu & Zhenyue Zhao & Jiang Li, 2021. "Exploring the direction and diversity of interdisciplinary knowledge diffusion: A case study of professor Zeyuan Liu's scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 6253-6272, July.
    6. Min, Chao & Bu, Yi & Sun, Jianjun, 2021. "Predicting scientific breakthroughs based on knowledge structure variations," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    7. Xian Li & Ronald Rousseau & Liming Liang & Fangjie Xi & Yushuang Lü & Yifan Yuan & Xiaojun Hu, 2022. "Is low interdisciplinarity of references an unexpected characteristic of Nobel Prize winning research?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2105-2122, April.
    8. Ronald Rousseau & Xiaojun Hu, 2018. "Under-cited influential work by Eugene Garfield," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 651-657, February.
    9. Yu Zhang & Min Wang & Morteza Saberi & Elizabeth Chang, 2022. "Analysing academic paper ranking algorithms using test data and benchmarks: an investigation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 4045-4074, July.
    10. Jiang, Xiaorui & Zhuge, Hai, 2019. "Forward search path count as an alternative indirect citation impact indicator," Journal of Informetrics, Elsevier, vol. 13(4).
    11. Hu, Xiaojun & Rousseau, Ronald, 2018. "A new approach to explore the knowledge transition path in the evolution of science & technology: From the biology of restriction enzymes to their application in biotechnology," Journal of Informetrics, Elsevier, vol. 12(3), pages 842-857.
    12. 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.
    13. Silveira Gontijo, Tiago & Motta Alves, Fabiana Alexandra, 2018. "A Bibliometric Study On Industry 4.0," MPRA Paper 115925, University Library of Munich, Germany, revised 23 Dec 2019.
    14. Werner Marx & Robin Haunschild & Bernie French & Lutz Bornmann, 2017. "Slow reception and under-citedness in climate change research: A case study of Charles David Keeling, discoverer of the risk of global warming," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(2), pages 1079-1092, August.

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