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Data-driven evolution of library and information science research methods (1990–2022): a perspective based on fine-grained method entities

Author

Listed:
  • Chengzhi Zhang

    (Nanjing University of Science and Technology)

  • Yi Mao

    (Nanjing University of Science and Technology)

  • Shuyu Peng

    (Nanjing University of Science and Technology)

Abstract

Since the 1990s, advancements in big data and information technology have increasingly driven data-centric research in the field of Library and Information Science (LIS). To assess the influence of this data-driven research paradigm on the LIS discipline, this study conducts a fine-grained analysis to uncover the evolutionary trends of research methods within the domain. Using academic papers from LIS published between 1990 and 2022, four key categories of data-driven method entities are automatically extracted: algorithms and models, data resources, software and tools, and metrics. Based on these entities, the study examines the evolution of LIS research methods from three dimensions: the characteristics of research method entities over time, their evolution within different research topics, and the evolutionary features of research method entities across various research methods. The findings highlight data resources as a pivotal driver of methodological evolution in LIS, revealing a cyclical pattern of "emergence-stability/practical application" in the development of research methods within the field.

Suggested Citation

  • Chengzhi Zhang & Yi Mao & Shuyu Peng, 2024. "Data-driven evolution of library and information science research methods (1990–2022): a perspective based on fine-grained method entities," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(12), pages 7889-7912, December.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:12:d:10.1007_s11192-024-05202-0
    DOI: 10.1007/s11192-024-05202-0
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