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Disciplinary knowledge diffusion in business research

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  • Wu, Chaojiang
  • Yan, Erjia
  • Hill, Chelsey

Abstract

Business research has established itself in largely six disciplines: Accounting, Marketing, Organizational Behavior and Management, Finance, Management Science and Operations Research, and Management Information Systems. The knowledge flows among these six disciplines and the factors that drive knowledge diffusion are important considerations. The quantitative analyses on a large dataset containing over 400,000 journal-to-journal citations for business journals published between 1997 and 2009 reveal important patterns of knowledge diffusion in business research. The cross-disciplinary knowledge diffusion is discipline-dependent and converging to a similar level in terms of the diversity. Aside from other factors such as articles published in the journal and the number of classifications, we find that journal quality, as measured by inclusion in the UT Dallas top journal list, has a significant effect on cross-disciplinary knowledge flows. We also offer some potential explanations for the effect of this formalized measure of quality.

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  • Wu, Chaojiang & Yan, Erjia & Hill, Chelsey, 2017. "Disciplinary knowledge diffusion in business research," Journal of Informetrics, Elsevier, vol. 11(2), pages 655-668.
    • Handle: RePEc:eee:infome:v:11:y:2017:i:2:p:655-668
    DOI: 10.1016/j.joi.2017.04.005
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    2. Chaojiang Wu & Erjia Yan & Yongjun Zhu & Kai Li, 2021. "Gender imbalance in the productivity of funded projects: A study of the outputs of National Institutes of Health R01 grants," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(11), pages 1386-1399, November.
    3. Cheng Peng & Zhepeng (Lionel) Li & Chaojiang Wu, 2023. "Researcher geographic mobility and publication productivity: an investigation into individual and institutional characteristics and the roles of academicians," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 379-406, January.
    4. Chao Yang & Cui Huang, 2024. "Target-oriented policy diffusion analysis: a case study of China’s information technology policy," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(3), pages 1347-1376, March.
    5. 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.
    6. Ashik, Farhan & Marc Lim, Weng & Vassallo, Jarrod P. & Voola, Ranjit, 2025. "Can marketing reduce inequality? Evidence from marketing science," Journal of Business Research, Elsevier, vol. 188(C).
    7. Giziński, Stanisław & Kaczyńska, Paulina & Ruczyński, Hubert & Wiśnios, Emilia & Pieliński, Bartosz & Biecek, Przemysław & Sienkiewicz, Julian, 2024. "Big Tech influence over AI research revisited: Memetic analysis of attribution of ideas to affiliation," Journal of Informetrics, Elsevier, vol. 18(4).
    8. Xinyuan Zhang & Qing Xie & Chaemin Song & Min Song, 2022. "Mining the evolutionary process of knowledge through multiple relationships between keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2023-2053, April.
    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).

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