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Tracing the main path of interdisciplinary research considering citation preference: A case from blockchain domain

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  • Yu, Dejian
  • Pan, Tianxing

Abstract

Main path analysis has been widely used in various fields to detect their development trajectories. However, the previous methods treat every citation equally. In fact, it leaves a question open to scholars considering that there are different citation preferences in different disciplines and at different publication times. There are different citation preferences in different disciplines and at different periods, which are ignored by scholars. In order to deal with the problem in identifying development paths in interdisciplinary research areas, this paper proposes a new main path analysis method. The improved main path analysis considers two factors involved in citation preference, including discipline bias and time bias. An evidence analysis from blockchain domain is conducted to demonstrate the effectiveness of the proposed method. The research result shows that the proposed main path analysis method in this paper can resolve the problem of discipline bias and time bias in interdisciplinary research. Moreover, the improved method provides a more differentiated ranking for citation linkages in the network. Our research can enhance the objectivity of the resulting main paths and promote broader application of the main path analysis.

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  • Yu, Dejian & Pan, Tianxing, 2021. "Tracing the main path of interdisciplinary research considering citation preference: A case from blockchain domain," Journal of Informetrics, Elsevier, vol. 15(2).
    • Handle: RePEc:eee:infome:v:15:y:2021:i:2:s1751157721000079
    DOI: 10.1016/j.joi.2021.101136
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    5. Huang, Chen-Hao & Liu, John S. & Ho, Mei Hsiu-Ching & Chou, Tzu-Chuan, 2022. "Towards more convergent main paths: A relevance-based approach," Journal of Informetrics, Elsevier, vol. 16(3).

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