IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v129y2024i12d10.1007_s11192-024-05202-0.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-024-05202-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-024-05202-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pertti Vakkari & Yu‐Wei Chang & Kalervo Järvelin, 2022. "Disciplinary contributions to research topics and methodology in Library and Information Science—Leading to fragmentation?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(12), pages 1706-1722, December.
    2. Bahaa Ibrahim, 2021. "Statistical methods used in Arabic journals of library and information science," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4383-4416, May.
    3. Wei Lu & Yong Huang & Yi Bu & Qikai Cheng, 2018. "Functional structure identification of scientific documents in computer science," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 463-486, April.
    4. Chengzhi Zhang & Liang Tian, 2023. "Non-synchronism in global usage of research methods in library and information science from 1990 to 2019," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(7), pages 3981-4006, July.
    5. Yuzhuo Wang & Chengzhi Zhang & Kai Li, 2022. "A review on method entities in the academic literature: extraction, evaluation, and application," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2479-2520, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dongin Nam & Jiwon Kim & Jeeyoung Yoon & Chaemin Song & Seongdeok Kim & Min Song, 2024. "Examining knowledge entities and its relationships based on citation sentences using a multi-anchor bipartite network," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 7197-7228, November.
    2. Zhichao Ba & Yujie Cao & Jin Mao & Gang Li, 2019. "A hierarchical approach to analyzing knowledge integration between two fields—a case study on medical informatics and computer science," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1455-1486, June.
    3. Yuzhuo Wang & Chengzhi Zhang & Kai Li, 2022. "A review on method entities in the academic literature: extraction, evaluation, and application," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2479-2520, May.
    4. Jianbing Ma & Kexin Yang, 2025. "A three-dimensional framework for quantifying knowledge intersection intensity: from a micro perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(1), pages 367-398, January.
    5. Pertti Vakkari & Kalervo Järvelin & Yu‐Wei Chang, 2023. "The association of disciplinary background with the evolution of topics and methods in Library and Information Science research 1995–2015," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(7), pages 811-827, July.
    6. Bowen Ma & Chengzhi Zhang & Yuzhuo Wang & Sanhong Deng, 2022. "Enhancing identification of structure function of academic articles using contextual information," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(2), pages 885-925, February.
    7. Jinqing Yang & Zhifeng Liu & Xiufeng Cheng & Guanghui Ye, 2024. "Understanding the keyword adoption behavior patterns of researchers from a functional structure perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3359-3384, June.
    8. Yujie Zhang & Rujiang Bai & Ling Kong & Xiaoyue Wang, 2024. "2SCE-4SL: a 2-stage causality extraction framework for scientific literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 7175-7195, November.
    9. Lu, Wei & Liu, Zhifeng & Huang, Yong & Bu, Yi & Li, Xin & Cheng, Qikai, 2020. "How do authors select keywords? A preliminary study of author keyword selection behavior," Journal of Informetrics, Elsevier, vol. 14(4).
    10. Xiaorui Jiang & Jingqiang Chen, 2023. "Contextualised segment-wise citation function classification," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5117-5158, September.
    11. Bhoomika N. Jadhav & E. P. Abdul Azeez & Nishtha Mishra & A. P. Senthil Kumar, 2024. "Research Productivity and Publishing Trends in Publicly Funded Social Work and Psychology Schools in India: A Bibliometric Analysis," SAGE Open, , vol. 14(2), pages 21582440241, April.
    12. Shengzhi Huang & Jiajia Qian & Yong Huang & Wei Lu & Yi Bu & Jinqing Yang & Qikai Cheng, 2022. "Disclosing the relationship between citation structure and future impact of a publication," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(7), pages 1025-1042, July.
    13. Qikai Cheng & Jiamin Wang & Wei Lu & Yong Huang & Yi Bu, 2020. "Keyword-citation-keyword network: a new perspective of discipline knowledge structure analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 1923-1943, September.
    14. Mohammed Azmi Al-Betar & Ammar Kamal Abasi & Ghazi Al-Naymat & Kamran Arshad & Sharif Naser Makhadmeh, 2023. "Optimization of scientific publications clustering with ensemble approach for topic extraction," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2819-2877, May.
    15. Fabian Stöhr, 2024. "Advancing language models through domain knowledge integration: a comprehensive approach to training, evaluation, and optimization of social scientific neural word embeddings," Journal of Computational Social Science, Springer, vol. 7(2), pages 1753-1793, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:129:y:2024:i:12:d:10.1007_s11192-024-05202-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.