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Understanding the evolving academic landscape of library and information science through faculty hiring data

Author

Listed:
  • Yongjun Zhu

    (Drexel University)

  • Erjia Yan

    (Drexel University)

  • Min Song

    (Yonsei University)

Abstract

Using a 40-year (from 1975 to 2015) hiring dataset of 642 library and Information science (LIS) faculty members from 44 US universities, this research reveals the disciplinary characteristics of LIS through several key aspects including gender, rank, country, university, major, and research area. Results show that genders and ranks among LIS faculty members are evenly distributed; geographically, more than 90 % of LIS faculty members received doctoral degrees in the US; meanwhile, 60 % of LIS faculty received Ph.D. in LIS, followed by Computer Science and Education; in regards to research interests, Human–Computer interaction, Digital Librarianship, Knowledge Organization and Management, and Information Behavior are the most popular research areas among LIS faculty members. Through a series of dynamic analyses, this study shows that the educational background of LIS faculty members is becoming increasingly diverse; in addition, research areas such as Human–Computer interaction, Social Network Analysis, Services for Children and Youth, Information Literacy, Information Ethics and Policy, and Data and Text Mining, Natural Language Processing, Machine Learning have received an increasing popularity. Predictive analyses are performed to discover trends on majors and research areas. Results show that the growth rate of LIS faculty members is linearly distributed. In addition, among faculty member’s Ph.D. majors, the share of LIS is decreasing while that the share of Computer Science is growing; among faculty members’ research areas, the share of Human–Computer interaction is on the rise.

Suggested Citation

  • Yongjun Zhu & Erjia Yan & Min Song, 2016. "Understanding the evolving academic landscape of library and information science through faculty hiring data," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1461-1478, September.
    • Handle: RePEc:spr:scient:v:108:y:2016:i:3:d:10.1007_s11192-016-2033-z
    DOI: 10.1007/s11192-016-2033-z
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    References listed on IDEAS

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    Cited by:

    1. Zuo, Zhiya & Zhao, Kang, 2018. "The more multidisciplinary the better? – The prevalence and interdisciplinarity of research collaborations in multidisciplinary institutions," Journal of Informetrics, Elsevier, vol. 12(3), pages 736-756.
    2. Zhu, Yongjun & Yan, Erjia, 2017. "Examining academic ranking and inequality in library and information science through faculty hiring networks," Journal of Informetrics, Elsevier, vol. 11(2), pages 641-654.
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    4. Manika Lamba & Margam Madhusudhan, 2019. "Mapping of topics in DESIDOC Journal of Library and Information Technology, India: a study," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 477-505, August.
    5. Xiaoguang Wang & Hongyu Wang & Han Huang, 2021. "Evolutionary exploration and comparative analysis of the research topic networks in information disciplines," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 4991-5017, June.
    6. He, Zekai & Zhen, Ni & Wu, Chaojiang, 2019. "Measuring and exploring the geographic mobility of American professors from graduating institutions: Differences across disciplines, academic ranks, and genders," Journal of Informetrics, Elsevier, vol. 13(3), pages 771-784.
    7. Zuo, Zhiya & Zhao, Kang & Ni, Chaoqun, 2019. "Standing on the shoulders of giants?—Faculty hiring in information schools," Journal of Informetrics, Elsevier, vol. 13(1), pages 341-353.

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