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Encoding the citation life-cycle: the operationalization of a literature-aging conceptual model

Author

Listed:
  • Zhenyu Gou

    (Wuhan University
    Wuhan University)

  • Fan Meng

    (Peking University)

  • Zaida Chinchilla-Rodríguez

    (Peking University
    Instituto de Políticas y Bienes Públicos (IPP))

  • Yi Bu

    (Peking University)

Abstract

In this study, we introduce a new literature-aging conceptual model to study the citation curve and discuss its implications. First, we improve the conceptual model by adding a period to describe the “death” of citations. Second, we offer a feasible operationalization for this conceptual model and implement a set of cross-discipline publications in the Web of Science to test its performance. Furthermore, we propose two measurements according to the new model—“Sleeping Period” and “Recognition Period”—to capture publications’ citation curve patterns. For instance, we find that half of the papers in Arts & Humanities published in 1985 receive no or extremely few citations in the first 5 years after their publication; after that, on average, those papers in Arts & Humanities have a 5-year-long period when their citations grow rapidly. In addition, we observe a special phenomenon named “literature revival” as some publications may have multiple citation life-cycles, which has received little attention from current research. Finally, we discuss the implications of our study, especially the application of the Sleeping Period and Recognition Period in improving scientific evaluation and collection development in libraries, and the inspiration of the “literature revival”.

Suggested Citation

  • Zhenyu Gou & Fan Meng & Zaida Chinchilla-Rodríguez & Yi Bu, 2022. "Encoding the citation life-cycle: the operationalization of a literature-aging conceptual model," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 5027-5052, August.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:8:d:10.1007_s11192-022-04437-z
    DOI: 10.1007/s11192-022-04437-z
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    Cited by:

    1. Liu, Jialin & Chen, Hongkan & Liu, Zhibo & Bu, Yi & Gu, Weiye, 2022. "Non-linearity between referencing behavior and citation impact: A large-scale, discipline-level analysis," Journal of Informetrics, Elsevier, vol. 16(3).
    2. Chakraborty, Joyita & Pradhan, Dinesh K. & Nandi, Subrata, 2024. "A multiple k-means cluster ensemble framework for clustering citation trajectories," Journal of Informetrics, Elsevier, vol. 18(2).

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