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Measuring the diffusion of an innovation: A citation analysis

Author

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  • Yujia Zhai
  • Ying Ding
  • Fang Wang

Abstract

Innovations transform our research traditions and become the driving force to advance individual, group, and social creativity. Meanwhile, interdisciplinary research is increasingly being promoted as a route to advance the complex challenges we face as a society. In this paper, we use Latent Dirichlet Allocation (LDA) citation as a proxy context for the diffusion of an innovation. With an analysis of topic evolution, we divide the diffusion process into five stages: testing and evaluation, implementation, improvement, extending, and fading. Through a correlation analysis of topic and subject, we show the application of LDA in different subjects. We also reveal the cross†boundary diffusion between different subjects based on the analysis of the interdisciplinary studies. The results show that as LDA is transferred into different areas, the adoption of each subject is relatively adjacent to those with similar research interests. Our findings further support researchers' understanding of the impact formation of innovation.

Suggested Citation

  • Yujia Zhai & Ying Ding & Fang Wang, 2018. "Measuring the diffusion of an innovation: A citation analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(3), pages 368-379, March.
    • Handle: RePEc:bla:jinfst:v:69:y:2018:i:3:p:368-379
    DOI: 10.1002/asi.23898
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    Citations

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    Cited by:

    1. Tang, Xuli & Li, Xin & Ding, Ying & Song, Min & Bu, Yi, 2020. "The pace of artificial intelligence innovations: Speed, talent, and trial-and-error," Journal of Informetrics, Elsevier, vol. 14(4).
    2. Yujia Zhai & Ying Ding & Hezhao Zhang, 2021. "Innovation adoption: Broadcasting versus virality," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(4), pages 403-416, April.
    3. Dejing Kong & Jianzhong Yang & Lingfeng Li, 2020. "Early identification of technological convergence in numerical control machine tool: a deep learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1983-2009, December.
    4. Guoqiang Liang & Haiyan Hou & Xiaodan Lou & Zhigang Hu, 2019. "Qualifying threshold of “take-off” stage for successfully disseminated creative ideas," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1193-1208, September.
    5. Guoqiang Liang & Haiyan Hou & Qiao Chen & Zhigang Hu, 2020. "Diffusion and adoption: an explanatory model of “question mark” and “rising star” articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 219-232, July.
    6. Zhang, Tongyang & Sun, Ran & Fensel, Julia & Yu, Andrew & Bu, Yi & Xu, Jian, 2023. "Understanding the domain development through a word status observation model," Journal of Informetrics, Elsevier, vol. 17(2).
    7. 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).
    8. Zhongyi Wang & Keying Wang & Jiyue Liu & Jing Huang & Haihua Chen, 2022. "Measuring the innovation of method knowledge elements in scientific literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2803-2827, May.
    9. Mao, Jin & Liang, Zhentao & Cao, Yujie & Li, Gang, 2020. "Quantifying cross-disciplinary knowledge flow from the perspective of content: Introducing an approach based on knowledge memes," Journal of Informetrics, Elsevier, vol. 14(4).
    10. Lyu, Haihua & Bu, Yi & Zhao, Zhenyue & Zhang, Jiarong & Li, Jiang, 2022. "Citation bias in measuring knowledge flow: Evidence from the web of science at the discipline level," Journal of Informetrics, Elsevier, vol. 16(4).

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