IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v116y2018i1d10.1007_s11192-018-2749-z.html
   My bibliography  Save this article

Predicting the degree of interdisciplinarity in academic fields: the case of nanotechnology

Author

Listed:
  • Wooseok Jang

    (Seoul National University)

  • Heeyeul Kwon

    (Seoul National University)

  • Yongtae Park

    (Seoul National University)

  • Hakyeon Lee

    (Seoul National University of Science and Technology)

Abstract

With today’s technological advances and uncertainty, interdisciplinary research (IDR) is recognized as the core element for an advancement not only in academic realms but also in non-academic worlds. Conventionally, the concept of interdisciplinarity has been measured indirectly through using historical data and estimating how frequently the knowledge is exchanged among various disciplinary fields. However, they were incapable of determining whether a certain discipline may turn out to be a successful interdisciplinary research field in the future. This paper, in response, highlights the significance of data points appearing in the future for predicting the degree of interdisciplinarity in academic fields. To this mean, the future data points are predicted through a stochastic basis, and they are further used to predict the future degree of disciplinary convergence. To demonstrate the effectiveness of this approach, we have selected the case of nanotechnology. Nanotechnology is a multidisciplinary academic field where diverse scientific and engineering disciplines merge with one another and contributes to various scientific development and social problem solving. In details, citation data of nanotechnology journals are gathered to estimate the expected citation frequency through a stochastic model, called Glänzel–Schubert–Schoepflin model, and its future potential of IDR is predicted through a diversity index. The result of the analysis predicted that the IDR of nanotechnology will be actively occurred with other disciplines such as chemistry, material science and energy in the future, and it also holds higher probability of being more frequently conducted compared to other field of studies.

Suggested Citation

  • Wooseok Jang & Heeyeul Kwon & Yongtae Park & Hakyeon Lee, 2018. "Predicting the degree of interdisciplinarity in academic fields: the case of nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 231-254, July.
    • Handle: RePEc:spr:scient:v:116:y:2018:i:1:d:10.1007_s11192-018-2749-z
    DOI: 10.1007/s11192-018-2749-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-018-2749-z
    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/s11192-018-2749-z?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. Juan Alcácer & Michelle Gittelman, 2006. "Patent Citations as a Measure of Knowledge Flows: The Influence of Examiner Citations," The Review of Economics and Statistics, MIT Press, vol. 88(4), pages 774-779, November.
    2. Xuefeng Wang & Zhinan Wang & Ying Huang & Yun Chen & Yi Zhang & Huichao Ren & Rongrong Li & Jinhui Pang, 2017. "Measuring interdisciplinarity of a research system: detecting distinction between publication categories and citation categories," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 2023-2039, June.
    3. Haiyun Xu & Ting Guo & Zenghui Yue & Lijie Ru & Shu Fang, 2016. "Interdisciplinary topics of information science: a study based on the terms interdisciplinarity index series," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(2), pages 583-601, February.
    4. Hakyeon Lee, 2015. "Uncovering the multidisciplinary nature of technology management: journal citation network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 51-75, January.
    5. Vincent Larivière & Yves Gingras, 2010. "On the relationship between interdisciplinarity and scientific impact," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(1), pages 126-131, January.
    6. Ismael Rafols & Martin Meyer, 2010. "Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(2), pages 263-287, February.
    7. Alan L Porter & J David Roessner & Alex S Cohen & Marty Perreault, 2006. "Interdisciplinary research: meaning, metrics and nurture," Research Evaluation, Oxford University Press, vol. 15(3), pages 187-195, December.
    8. Hanning Guo & Scott Weingart & Katy Börner, 2011. "Mixed-indicators model for identifying emerging research areas," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 421-435, October.
    9. Jiming Hu & Yin Zhang, 2017. "Discovering the interdisciplinary nature of Big Data research through social network analysis and visualization," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 91-109, July.
    10. Bammer, Gabriele, 2008. "Enhancing research collaborations: Three key management challenges," Research Policy, Elsevier, vol. 37(5), pages 875-887, June.
    11. Ed J. Rinia & Thed N. van Leeuwen & Eppo E. W. Bruins & Hendrik G. van Vuren & Anthony F. J. van Raan, 2002. "Measuring knowledge transfer between fields of science," Scientometrics, Springer;Akadémiai Kiadó, vol. 54(3), pages 347-362, July.
    12. Alexis-Michel Mugabushaka & Anthi Kyriakou & Theo Papazoglou, 2016. "Bibliometric indicators of interdisciplinarity: the potential of the Leinster–Cobbold diversity indices to study disciplinary diversity," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(2), pages 593-607, May.
    13. Loet Leydesdorff & Caroline S. Wagner & Lutz Bornmann, 2018. "Betweenness and diversity in journal citation networks as measures of interdisciplinarity—A tribute to Eugene Garfield," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 567-592, February.
    14. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    15. Loet Leydesdorff & Lutz Bornmann, 2016. "The operationalization of “fields” as WoS subject categories (WCs) in evaluative bibliometrics: The cases of “library and information science” and “science & technology studies”," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(3), pages 707-714, March.
    16. Karmen Stopar & Damjana Drobne & Klemen Eler & Tomaz Bartol, 2016. "Citation analysis and mapping of nanoscience and nanotechnology: identifying the scope and interdisciplinarity of research," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(2), pages 563-581, February.
    17. König, Bettina & Diehl, Katharina & Tscherning, Karen & Helming, Katharina, 2013. "A framework for structuring interdisciplinary research management," Research Policy, Elsevier, vol. 42(1), pages 261-272.
    18. Joachim Schummer, 2004. "Multidisciplinarity, interdisciplinarity, and patterns of research collaboration in nanoscience and nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 59(3), pages 425-465, March.
    19. Loet Leydesdorff & Ping Zhou, 2007. "Nanotechnology as a field of science: Its delineation in terms of journals and patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 70(3), pages 693-713, March.
    20. Alan L. Porter & Alex S. Cohen & J. David Roessner & Marty Perreault, 2007. "Measuring researcher interdisciplinarity," Scientometrics, Springer;Akadémiai Kiadó, vol. 72(1), pages 117-147, July.
    21. Su, Hsin-Ning & Moaniba, Igam M., 2017. "Investigating the dynamics of interdisciplinary evolution in technology developments," Technological Forecasting and Social Change, Elsevier, vol. 122(C), pages 12-23.
    22. van Rijnsoever, Frank J. & Hessels, Laurens K., 2011. "Factors associated with disciplinary and interdisciplinary research collaboration," Research Policy, Elsevier, vol. 40(3), pages 463-472, April.
    23. Marco Tortoriello & Ray Reagans & Bill McEvily, 2012. "Bridging the Knowledge Gap: The Influence of Strong Ties, Network Cohesion, and Network Range on the Transfer of Knowledge Between Organizational Units," Organization Science, INFORMS, vol. 23(4), pages 1024-1039, August.
    24. Meade, Nigel & Islam, Towhidul, 1995. "Forecasting with growth curves: An empirical comparison," International Journal of Forecasting, Elsevier, vol. 11(2), pages 199-215, June.
    25. Vincent Larivière & Yves Gingras, 2010. "On the relationship between interdisciplinarity and scientific impact," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(1), pages 126-131, January.
    26. Andy Stirling, 2007. "A General Framework for Analysing Diversity in Science, Technology and Society," SPRU Working Paper Series 156, SPRU - Science Policy Research Unit, University of Sussex Business School.
    27. Lee, Changyong & Cho, Yangrae & Seol, Hyeonju & Park, Yongtae, 2012. "A stochastic patent citation analysis approach to assessing future technological impacts," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 16-29.
    28. Chen, Chaomei & Chen, Yue & Horowitz, Mark & Hou, Haiyan & Liu, Zeyuan & Pellegrino, Donald, 2009. "Towards an explanatory and computational theory of scientific discovery," Journal of Informetrics, Elsevier, vol. 3(3), pages 191-209.
    29. Fernanda Morillo & María Bordons & Isabel Gómez, 2001. "An approach to interdisciplinarity through bibliometric indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 51(1), pages 203-222, April.
    30. Alan L. Porter & Ismael Rafols, 2009. "Is science becoming more interdisciplinary? Measuring and mapping six research fields over time," Scientometrics, Springer;Akadémiai Kiadó, vol. 81(3), pages 719-745, December.
    31. Quentin L. Burrell, 2003. "Predicting future citation behavior," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 54(5), pages 372-378, March.
    32. Fildes, Robert, 2006. "The forecasting journals and their contribution to forecasting research: Citation analysis and expert opinion," International Journal of Forecasting, Elsevier, vol. 22(3), pages 415-432.
    33. Burrell, Quentin L., 2007. "Hirsch's h-index: A stochastic model," Journal of Informetrics, Elsevier, vol. 1(1), pages 16-25.
    34. Loet Leydesdorff, 2007. "Betweenness centrality as an indicator of the interdisciplinarity of scientific journals," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(9), pages 1303-1319, July.
    35. Wagner, Caroline S. & Roessner, J. David & Bobb, Kamau & Klein, Julie Thompson & Boyack, Kevin W. & Keyton, Joann & Rafols, Ismael & Börner, Katy, 2011. "Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature," Journal of Informetrics, Elsevier, vol. 5(1), pages 14-26.
    36. Jonathan M. Levitt & Mike Thelwall, 2008. "Is multidisciplinary research more highly cited? A macrolevel study," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(12), pages 1973-1984, October.
    37. Lin Zhang & Ronald Rousseau & Wolfgang Glänzel, 2016. "Diversity of references as an indicator of the interdisciplinarity of journals: Taking similarity between subject fields into account," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(5), pages 1257-1265, May.
    38. Gaston Heimeriks, 2012. "Interdisciplinarity in biotechnology, genomics and nanotechnology," Science and Public Policy, Oxford University Press, vol. 40(1), pages 97-112, September.
    39. Wolfgang Glänzel & Sarah Heeffer & Bart Thijs, 2016. "A triangular model for publication and citation statistics of individual authors," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(2), pages 857-872, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ryo Takahashi & Kenji Kaibe & Kazuyuki Suzuki & Sayaka Takahashi & Kotaro Takeda & Marc Hansen & Michiaki Yumoto, 2023. "New concept of the affinity between research fields using academic journal data in Scopus," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3507-3534, June.

    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. Alfonso Ávila-Robinson & Cristian Mejia & Shintaro Sengoku, 2021. "Are bibliometric measures consistent with scientists’ perceptions? The case of interdisciplinarity in research," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7477-7502, September.
    2. Giulio Giacomo Cantone, 2024. "How to measure interdisciplinary research? A systemic design for the model of measurement," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(8), pages 4937-4982, August.
    3. Lin Zhang & Beibei Sun & Zaida Chinchilla-Rodríguez & Lixin Chen & Ying Huang, 2018. "Interdisciplinarity and collaboration: on the relationship between disciplinary diversity in departmental affiliations and reference lists," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 271-291, October.
    4. Shunshun Shi & Wenyu Zhang & Shuai Zhang & Jie Chen, 2018. "Does prestige dimension influence the interdisciplinary performance of scientific entities in knowledge flow? Evidence from the e-government field," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 1237-1264, November.
    5. Rafols, Ismael & Leydesdorff, Loet & O’Hare, Alice & Nightingale, Paul & Stirling, Andy, 2012. "How journal rankings can suppress interdisciplinary research: A comparison between Innovation Studies and Business & Management," Research Policy, Elsevier, vol. 41(7), pages 1262-1282.
    6. Shiji Chen & Yanhui Song & Fei Shu & Vincent Larivière, 2022. "Interdisciplinarity and impact: the effects of the citation time window," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2621-2642, May.
    7. Chen, Shiji & Qiu, Junping & Arsenault, Clément & Larivière, Vincent, 2021. "Exploring the interdisciplinarity patterns of highly cited papers," Journal of Informetrics, Elsevier, vol. 15(1).
    8. Abramo, Giovanni & D’Angelo, Ciriaco Andrea & Zhang, Lin, 2018. "A comparison of two approaches for measuring interdisciplinary research output: The disciplinary diversity of authors vs the disciplinary diversity of the reference list," Journal of Informetrics, Elsevier, vol. 12(4), pages 1182-1193.
    9. Shengli Deng & Sudi Xia, 2020. "Mapping the interdisciplinarity in information behavior research: a quantitative study using diversity measure and co-occurrence analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 489-513, July.
    10. Wolfgang Glänzel & Koenraad Debackere, 2022. "Various aspects of interdisciplinarity in research and how to quantify and measure those," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5551-5569, September.
    11. Wagner, Caroline S. & Roessner, J. David & Bobb, Kamau & Klein, Julie Thompson & Boyack, Kevin W. & Keyton, Joann & Rafols, Ismael & Börner, Katy, 2011. "Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature," Journal of Informetrics, Elsevier, vol. 5(1), pages 14-26.
    12. Ricardo Arencibia-Jorge & Rosa Lidia Vega-Almeida & José Luis Jiménez-Andrade & Humberto Carrillo-Calvet, 2022. "Evolutionary stages and multidisciplinary nature of artificial intelligence research," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5139-5158, September.
    13. Tanmoy Chakraborty, 2018. "Role of interdisciplinarity in computer sciences: quantification, impact and life trajectory," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1011-1029, March.
    14. Hongyu Zhou & Raf Guns & Tim C. E. Engels, 2022. "Are social sciences becoming more interdisciplinary? Evidence from publications 1960–2014," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(9), pages 1201-1221, September.
    15. Xuefeng Wang & Zhinan Wang & Ying Huang & Yun Chen & Yi Zhang & Huichao Ren & Rongrong Li & Jinhui Pang, 2017. "Measuring interdisciplinarity of a research system: detecting distinction between publication categories and citation categories," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 2023-2039, June.
    16. Sander Zwanenburg & Maryam Nakhoda & Peter Whigham, 2022. "Toward greater consistency and validity in measuring interdisciplinarity: a systematic and conceptual evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7769-7788, December.
    17. Leydesdorff, Loet & Wagner, Caroline S. & Bornmann, Lutz, 2019. "Interdisciplinarity as diversity in citation patterns among journals: Rao-Stirling diversity, relative variety, and the Gini coefficient," Journal of Informetrics, Elsevier, vol. 13(1), pages 255-269.
    18. Juste Raimbault, 2019. "Exploration of an interdisciplinary scientific landscape," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 617-641, May.
    19. Fei Shu & Jesse David Dinneen & Shiji Chen, 2022. "Measuring the disparity among scientific disciplines using Library of Congress Subject Headings," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3613-3628, June.
    20. Jingjing Ren & Fang Wang & Minglu Li, 2023. "Dynamics and characteristics of interdisciplinary research in scientific breakthroughs: case studies of Nobel-winning research in the past 120 years," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4383-4419, August.

    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:116:y:2018:i:1:d:10.1007_s11192-018-2749-z. 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.