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How are collaboration and productivity correlated at various career stages of scientists?

Author

Listed:
  • Zhigang Hu

    (Dalian University of Technology
    Dalian University of Technology)

  • Chaomei Chen

    (Dalian University of Technology
    Drexel University
    Drexel University)

  • Zeyuan Liu

    (Dalian University of Technology
    Dalian University of Technology)

Abstract

Collaboration is believed to be influential on researchers’ productivity. However, the impact of collaboration relies on factors such as disciplines, collaboration patterns, and collaborators’ characters. In addition, at different career stages, such as the growth or the establishment career stages of scientists, collaboration is different in scale and scope, and its effect on productivity varies. In this paper, we study the relationships between collaboration and productivity in four disciplines, Organic Chemistry, Virology, Mathematics and Computer Science. Our study found that the productivity is correlated with collaboration in general, but the correlation could be positive or negative on the basis of which aspect of collaboration to measure, i.e., the scale or scope of the collaboration. The correlation becomes stronger as individual scientists progress through various stages of their career. Furthermore, experimental disciplines, such as Organic Chemistry and Virology, have shown stronger correlation coefficients than theoretical ones such as Mathematics and Computer Science.

Suggested Citation

  • Zhigang Hu & Chaomei Chen & Zeyuan Liu, 2014. "How are collaboration and productivity correlated at various career stages of scientists?," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1553-1564, November.
    • Handle: RePEc:spr:scient:v:101:y:2014:i:2:d:10.1007_s11192-014-1323-6
    DOI: 10.1007/s11192-014-1323-6
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    Cited by:

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    3. Helena Mihaljević-Brandt & Lucía Santamaría & Marco Tullney, 2016. "The Effect of Gender in the Publication Patterns in Mathematics," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-23, October.
    4. Cassidy R. Sugimoto & Thomas J. Sugimoto & Andrew Tsou & Staša Milojević & Vincent Larivière, 2016. "Age stratification and cohort effects in scholarly communication: a study of social sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 997-1016, November.
    5. James Hartley & Guillaume Cabanac, 2016. "Are two authors better than one? Can writing in pairs affect the readability of academic blogs?," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 2119-2122, December.
    6. Danielle H. Lee, 2019. "Predicting the research performance of early career scientists," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1481-1504, December.
    7. Frode Eika Sandnes, 2018. "Do Norwegian academics who publish more earn higher salaries?," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 263-281, April.
    8. Marjan Cugmas & Anuška Ferligoj & Luka Kronegger, 2016. "The stability of co-authorship structures," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(1), pages 163-186, January.
    9. Christoph Endenich & Rouven Trapp, 2016. "Cooperation for Publication? An Analysis of Co-authorship Patterns in Leading Accounting Journals," European Accounting Review, Taylor & Francis Journals, vol. 25(3), pages 613-633, September.
    10. Jinyuan Ma & Fan Jiang & Liujian Gu & Xiang Zheng & Xiao Lin & Chuanyi Wang, 2020. "Patterns of the Network of Cross-Border University Research Collaboration in the Guangdong-Hong Kong-Macau Greater Bay Area," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
    11. Zhigang Hu & Wencan Tian & Jiacheng Guo & Xianwen Wang, 2020. "Mapping research collaborations in different countries and regions: 1980–2019," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 729-745, July.

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