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Mapping the diffusion of scholarly knowledge among major U.S. research institutions

Author

Listed:
  • Katy Börner

    (School of Library and Information Science, Indiana University)

  • Shashikant Penumarthy

    (School of Library and Information Science, Indiana University)

  • Mark Meiss

    (School of Informatics, Indiana University)

  • Weimao Ke

    (School of Library and Information Science, Indiana University)

Abstract

Summary This paper reports the results of a large scale data analysis that aims to identify the production, diffusion, and consumption of scholarly knowledge among top research institutions in the United States. A 20-year publication data set was analyzed to identify the 500 most cited research institutions and spatio-temporal changes in their inter-citation patterns. A novel approach to analyzing the dual role of institutions as producers and consumers of scholarly knowledge and to study the diffusion of knowledge among them is introduced. A geographic visualization metaphor is used to visually depict the production and consumption of knowledge. The highest producers and their consumers as well as the highest consumers and their producers are identified and mapped. Surprisingly, the introduction of the Internet does not seem to affect the distance over which scholarly knowledge diffuses as manifested by citation links. The citation linkages between institutions fall off with the distance between them, and there is a strong linear relationship between the log of the citation counts and the log of the distance. The paper concludes with a discussion of these results and future work.

Suggested Citation

  • Katy Börner & Shashikant Penumarthy & Mark Meiss & Weimao Ke, 2006. "Mapping the diffusion of scholarly knowledge among major U.S. research institutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 68(3), pages 415-426, September.
    • Handle: RePEc:spr:scient:v:68:y:2006:i:3:d:10.1007_s11192-006-0120-2
    DOI: 10.1007/s11192-006-0120-2
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    Cited by:

    1. Pu Han & Jin Shi & Xiaoyan Li & Dongbo Wang & Si Shen & Xinning Su, 2014. "International collaboration in LIS: global trends and networks at the country and institution level," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(1), pages 53-72, January.
    2. Liying Yang & Steven A. Morris & Elizabeth M. Barden, 2009. "Mapping institutions and their weak ties in a specialty: A case study of cystic fibrosis body composition research," Scientometrics, Springer;Akadémiai Kiadó, vol. 79(2), pages 421-434, May.
    3. Wang, Jue & Zhang, Liwei, 2018. "Proximal advantage in knowledge diffusion: The time dimension," Journal of Informetrics, Elsevier, vol. 12(3), pages 858-867.
    4. Giovanni Abramo & Ciriaco Andrea D’Angelo, 2021. "A bibliometric methodology to unveil territorial inequities in the scientific wealth to combat COVID-19," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6601-6624, August.
    5. Jiang Wu, 2013. "Geographical knowledge diffusion and spatial diversity citation rank," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 181-201, January.
    6. Meen Chul Kim & Yoo Kyung Jeong & Min Song, 2014. "Investigating the integrated landscape of the intellectual topology of bioinformatics," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 309-335, October.
    7. Peter Wittek & Sándor Darányi & Gustaf Nelhans, 2017. "Ruling out static latent homophily in citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 765-777, February.
    8. Abramo, Giovanni & D’Angelo, Ciriaco Andrea & Di Costa, Flavia, 2020. "Knowledge spillovers: Does the geographic proximity effect decay over time? A discipline-level analysis, accounting for cognitive proximity, with and without self-citations," Journal of Informetrics, Elsevier, vol. 14(4).
    9. Wagner, Caroline S. & Whetsell, Travis A. & Mukherjee, Satyam, 2019. "International research collaboration: Novelty, conventionality, and atypicality in knowledge recombination," Research Policy, Elsevier, vol. 48(5), pages 1260-1270.
    10. Siqin Wang & Mengxi Zhang & Tao Hu & Xiaokang Fu & Zhe Gao & Briana Halloran & Yan Liu, 2021. "A Bibliometric Analysis and Network Visualisation of Human Mobility Studies from 1990 to 2020: Emerging Trends and Future Research Directions," Sustainability, MDPI, vol. 13(10), pages 1-22, May.
    11. Guang Yu & Ming-Yang Wang & Da-Ren Yu, 2010. "Characterizing knowledge diffusion of Nanoscience & Nanotechnology by citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(1), pages 81-97, July.
    12. Xuan Liu & Shan Jiang & Hsinchun Chen & Catherine A. Larson & Mihail C. Roco, 2015. "Modeling knowledge diffusion in scientific innovation networks: an institutional comparison between China and US with illustration for nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1953-1984, December.
    13. Carlos Olmeda-Gómez & Carlos Romá-Mateo & Maria-Antonia Ovalle-Perandones, 2019. "Overview of trends in global epigenetic research (2009–2017)," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1545-1574, June.
    14. Duede, Eamon & Teplitskiy, Misha & Lakhani, Karim & Evans, James, 2024. "Being together in place as a catalyst for scientific advance," Research Policy, Elsevier, vol. 53(2).
    15. Frenken, Koen & Hardeman, Sjoerd & Hoekman, Jarno, 2009. "Spatial scientometrics: Towards a cumulative research program," Journal of Informetrics, Elsevier, vol. 3(3), pages 222-232.
    16. Morescalchi, Andrea & Pammolli, Fabio & Penner, Orion & Petersen, Alexander M. & Riccaboni, Massimo, 2015. "The evolution of networks of innovators within and across borders: Evidence from patent data," Research Policy, Elsevier, vol. 44(3), pages 651-668.
    17. Pan, Xuelian & Yan, Erjia & Cui, Ming & Hua, Weina, 2018. "Examining the usage, citation, and diffusion patterns of bibliometric mapping software: A comparative study of three tools," Journal of Informetrics, Elsevier, vol. 12(2), pages 481-493.
    18. Muaz Niazi & Amir Hussain, 2011. "Agent-based computing from multi-agent systems to agent-based models: a visual survey," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(2), pages 479-499, November.
    19. Giovanni Abramo & Ciriaco Andrea D’Angelo & Flavia Costa, 2020. "Does the geographic proximity effect on knowledge spillovers vary across research fields?," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 1021-1036, May.
    20. Wencan Tian & Yongzhen Wang & Zhigang Hu & Ruonan Cai & Guangyao Zhang & Xianwen Wang, 2024. "Does Granger causality exist between article usage and publication counts? A topic-level time-series evidence from IEEE Xplore," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3285-3302, June.
    21. Gao, Xue & Rai, Varun, 2023. "Knowledge acquisition and innovation quality: The moderating role of geographical characteristics of technology," Technovation, Elsevier, vol. 125(C).
    22. Saeed-Ul Hassan & Iqra Safder & Anam Akram & Faisal Kamiran, 2018. "A novel machine-learning approach to measuring scientific knowledge flows using citation context analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 973-996, August.
    23. Christin Katharina Kreutz & Premtim Sahitaj & Ralf Schenkel, 2020. "Evaluating semantometrics from computer science publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2915-2954, December.
    24. Lu Liu & Benjamin F. Jones & Brian Uzzi & Dashun Wang, 2023. "Data, measurement and empirical methods in the science of science," Nature Human Behaviour, Nature, vol. 7(7), pages 1046-1058, July.

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