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Understanding interdisciplinary knowledge integration through citance analysis: A case study on eHealth

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  • Wang, Shiyun
  • Mao, Jin
  • Lu, Kun
  • Cao, Yujie
  • Li, Gang

Abstract

Recent research has shifted to investigating knowledge integration in an interdisciplinary field and measuring the interdisciplinarity. Conventional citation analysis does not consider the context of citations, which limits the understanding of interdisciplinary knowledge integration. This study introduces a novel analytical framework to characterize interdisciplinary knowledge integration by both the content, i.e., integrated knowledge phrases (IKPs), and location of citances (i.e., citing sentences) in addition to citations. Seven knowledge categories are used to classify IKPs, including Research Subject, Theory, Research Methodology, Technology, Human Entity, Data, and Others. The eHealth field is explored as an exemplar interdisciplinary field in the case study. The result reveals that the ranks of source disciplines quantified by the integrated knowledge phrases are different from those by citations, especially in terms of average knowledge integration density. The distributions of the IKPs over the knowledge categories differ among source disciplines, indicating their different contributions to knowledge integration of eHealth field. The knowledge from adjacent disciplines is integrated into the field faster than that from other disciplines. Knowledge distributions over sections of articles are also different among source disciplines, and a correlation between knowledge categories and the sections they were used is observed. The analytical framework offers a way to better understand an interdisciplinary field by disclosing the characteristics of interdisciplinary knowledge integration from the perspective of knowledge content and usage.

Suggested Citation

  • Wang, Shiyun & Mao, Jin & Lu, Kun & Cao, Yujie & Li, Gang, 2021. "Understanding interdisciplinary knowledge integration through citance analysis: A case study on eHealth," Journal of Informetrics, Elsevier, vol. 15(4).
    • Handle: RePEc:eee:infome:v:15:y:2021:i:4:s1751157721000857
    DOI: 10.1016/j.joi.2021.101214
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