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The classification of citing motivations: a meta-synthesis

Author

Listed:
  • Dongqing Lyu

    (Nanjing University)

  • Xuanmin Ruan

    (Nanjing University)

  • Juan Xie

    (Nanjing University)

  • Ying Cheng

    (Nanjing University)

Abstract

Citation analysis has been a prevalent method in the field of information science, especially research on bibliometrics and evaluation, but its validity relies heavily on how the citations are treated. It is essential to study authors’ citing motivations to identify citations with different values and significance. This study applied a meta-synthesis approach to establish a new holistic classification of citation motivations based on previous studies. First, we used a four-step search strategy to identify related articles on authors’ citing motivations. Thirty-eight primary studies were included after the inclusion and exclusion criteria were applied and appraised using the Evidence-based Librarianship checklist. Next, we decoded and recoded the citing motivations found in the included studies, following the standard procedures of meta-synthesis. Thirty-five descriptive concepts of citation motivations emerged, which were then synthesized into 13 analytic themes. As a result, we proposed a comprehensive classification, including two main categories of citing reasons, i.e., “scientific motivations” and “tactical motivations.” Generally, the citations driven by scientific motivations serve as a rhetorical function, while tactical motivations are social or benefit-oriented and not easily captured through text-parsing. Our synthesis contributes to bibliometric and scientific evaluation theory. The synthesized classification also provides a comprehensive and unified annotation schema for citation classification and helps identify the useful mentions of a reference in a citing paper to optimize citation- based measurements.

Suggested Citation

  • Dongqing Lyu & Xuanmin Ruan & Juan Xie & Ying Cheng, 2021. "The classification of citing motivations: a meta-synthesis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3243-3264, April.
  • Handle: RePEc:spr:scient:v:126:y:2021:i:4:d:10.1007_s11192-021-03908-z
    DOI: 10.1007/s11192-021-03908-z
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