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The relationship between the research performance of scientists and their position in co-authorship networks in three fields

Author

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  • Bordons, María
  • Aparicio, Javier
  • González-Albo, Borja
  • Díaz-Faes, Adrián A.

Abstract

Research networks play a crucial role in the production of new knowledge since collaboration contributes to determine the cognitive and social structure of scientific fields and has a positive influence on research. This paper analyses the structure of co-authorship networks in three different fields (Nanoscience, Pharmacology and Statistics) in Spain over a three-year period (2006–2008) and explores the relationship between the research performance of scientists and their position in co-authorship networks. A denser co-authorship network is found in the two experimental fields than in Statistics, where the network is of a less connected and more fragmented nature. Using the g-index as a proxy for individual research performance, a Poisson regression model is used to explore how performance is related to different co-authorship network measures and to disclose interfield differences. The number of co-authors (degree centrality) and the strength of links show a positive relationship with the g-index in the three fields. Local cohesion presents a negative relationship with the g-index in the two experimental fields, where open networks and the diversity of co-authors seem to be beneficial. No clear advantages from intermediary positions (high betweenness) or from being linked to well-connected authors (high eigenvector) can be inferred from this analysis. In terms of g-index, the benefits derived by authors from their position in co-authorship networks are larger in the two experimental fields than in the theoretical one.

Suggested Citation

  • Bordons, María & Aparicio, Javier & González-Albo, Borja & Díaz-Faes, Adrián A., 2015. "The relationship between the research performance of scientists and their position in co-authorship networks in three fields," Journal of Informetrics, Elsevier, vol. 9(1), pages 135-144.
    • Handle: RePEc:eee:infome:v:9:y:2015:i:1:p:135-144
    DOI: 10.1016/j.joi.2014.12.001
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    as
    1. Gernot Grabher & Walter W. Powell (ed.), 2004. "Networks," Books, Edward Elgar Publishing, volume 0, number 2771.
    2. Gordon Walker & Bruce Kogut & Weijian Shan, 1997. "Social Capital, Structural Holes and the Formation of an Industry Network," Organization Science, INFORMS, vol. 8(2), pages 109-125, April.
    3. Isin Guler & Atul Nerkar, 2012. "The impact of global and local cohesion on innovation in the pharmaceutical industry," Strategic Management Journal, Wiley Blackwell, vol. 33(5), pages 535-549, May.
    4. Kamal Badar & Julie M. Hite & Yuosre F. Badir, 2013. "Examining the relationship of co-authorship network centrality and gender on academic research performance: the case of chemistry researchers in Pakistan," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(2), pages 755-775, February.
    5. Chien Hsiang Liao, 2011. "How to improve research quality? Examining the impacts of collaboration intensity and member diversity in collaboration networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(3), pages 747-761, March.
    6. Abbasi, Alireza & Altmann, Jörn & Hossain, Liaquat, 2011. "Identifying the effects of co-authorship networks on the performance of scholars: A correlation and regression analysis of performance measures and social network analysis measures," Journal of Informetrics, Elsevier, vol. 5(4), pages 594-607.
    7. Bing He & Ying Ding & Chaoqun Ni, 2011. "Mining enriched contextual information of scientific collaboration: A meso perspective," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(5), pages 831-845, May.
    8. Grit Laudel, 2002. "What do we measure by co-authorships?," Research Evaluation, Oxford University Press, vol. 11(1), pages 3-15, April.
    9. Katz, J. Sylvan & Martin, Ben R., 1997. "What is research collaboration?," Research Policy, Elsevier, vol. 26(1), pages 1-18, March.
    10. Ray Reagans & Ezra W. Zuckerman, 2001. "Networks, Diversity, and Productivity: The Social Capital of Corporate R&D Teams," Organization Science, INFORMS, vol. 12(4), pages 502-517, August.
    11. Leo Egghe, 2006. "Theory and practise of the g-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(1), pages 131-152, October.
    12. Thomas Heinze & Gerrit Bauer, 2007. "Characterizing creative scientists in nano-S&T: Productivity, multidisciplinarity, and network brokerage in a longitudinal perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 70(3), pages 811-830, March.
    13. Li, Eldon Y. & Liao, Chien Hsiang & Yen, Hsiuju Rebecca, 2013. "Co-authorship networks and research impact: A social capital perspective," Research Policy, Elsevier, vol. 42(9), pages 1515-1530.
    14. Haiyan Hou & Hildrun Kretschmer & Zeyuan Liu, 2008. "The structure of scientific collaboration networks in Scientometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(2), pages 189-202, May.
    15. María Bordons & Javier Aparicio & Rodrigo Costas, 2013. "Heterogeneity of collaboration and its relationship with research impact in a biomedical field," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(2), pages 443-466, August.
    16. Barabási, A.L & Jeong, H & Néda, Z & Ravasz, E & Schubert, A & Vicsek, T, 2002. "Evolution of the social network of scientific collaborations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 311(3), pages 590-614.
    17. Erjia Yan & Ying Ding, 2009. "Applying centrality measures to impact analysis: A coauthorship network analysis," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(10), pages 2107-2118, October.
    18. Rodrigo Costas & María Bordons, 2008. "Is g-index better than h-index? An exploratory study at the individual level," Scientometrics, Springer;Akadémiai Kiadó, vol. 77(2), pages 267-288, November.
    19. Bing He & Ying Ding & Chaoqun Ni, 2011. "Mining enriched contextual information of scientific collaboration: A meso perspective," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(5), pages 831-845, May.
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