Skip to main content
SpringerLink
Log in

Predicting the research performance of early career scientists

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

This paper examines how early career-related factors can predict the future research performance of computer and information scientists. Although a few bibliometric studies have previously investigated multiple factors relating to early career scientists that significantly predict their future research performance, there have been limited studies on early career-related factors affecting scientists in the fields of information science and computer science. This study analyzes 4102 scientists whose publishing careers started in the same year. The criteria used to quantify future research performance of the target scientists included the number of publications and citation counts of publications in a 4-year citation window to indicate future research productivity and research impact, respectively. These criteria were regressed on 13 early career-related factors. The results showed that these factors accounted for about 27% and 23% of the future productivity of the target scientists in terms of journal articles and conference papers, respectively; these 13 factors were also responsible for 19% of the future impact of target scientists’ journal articles and 19% of the future impact of their conference papers. The factor that most contributed to explaining the future research performance (i.e. publication numbers) and future research impact (i.e. citation counts of publications) was the number of publications (both journal articles and conference papers) produced by the target scientists in their early career years.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Abramo, G., & D’Angelo, C. A. (2014). How do you define and measure research productivity? Scientometrics,101(2), 1129–1144.

    Google Scholar 

  • Acuna, D. E., Allesina, S., & Kording, K. P. (2012). Predicting scientific success. Nature,489, 201.

    Google Scholar 

  • Adam Holland, G. (2008). Information science: An interdisciplinary effort? Journal of Documentation,64(1), 7–23.

    Google Scholar 

  • Aksnes, D. W. (2003). A macro study of self-citation. Scientometrics,56(2), 235–246.

    Google Scholar 

  • Biscaro, C., & Giupponi, C. (2014). Co-authorship and bibliographic coupling network effects on citations. PLoS ONE,9(6), e99502.

    Google Scholar 

  • Bornmann, L., Mutz, R., & Daniel, H.-D. (2008a). Are there better indices for evaluation purposes than the h index? A comparison of nine different variants of the h index using data from biomedicine. Journal of the American Society for Information Science and Technology,59(5), 830–837.

    Google Scholar 

  • Bornmann, L., Wallon, G., & Ledin, A. (2008b). Does the committee peer review select the best applicants for funding? An investigation of the selection process for two European Molecular Biology Organization Programmes. PLoS ONE,3(10), e3480.

    Google Scholar 

  • Bornmann, L., & Williams, R. (2017). Can the journal impact factor be used as a criterion for the selection of junior researchers? A large-scale empirical study based on ResearcherID data. Journal of Informetrics,11(3), 788–799.

    Google Scholar 

  • Carayol, N., & Matt, M. (2004). Does research organization influence academic production?: Laboratory level evidence from a large European university. Research Policy,33(8), 1081–1102.

    Google Scholar 

  • Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences. New York: Routledge.

    Google Scholar 

  • Colugnati, F. A., Carneiro, A. M., & Salles Filho, S. (2011). Multidimensional evaluation of a program for early-career researcher in Brazil-the Young Investigator in Emerging Centers program. In 2011 Atlanta conference on science and innovation policy, 2011 (pp. 1–8). IEEE.

  • Costas, R., & Bordons, M. (2011). Do age and professional rank influence the order of authorship in scientific publications? Some evidence from a micro-level perspective. Scientometrics,88(1), 145–161.

    Google Scholar 

  • Dong, Y., Johnson, R. A., & Chawla, N. V. (2015). Will this paper increase your h-index?: Scientific impact prediction. In Proceedings of the eighth ACM international conference on web search and data mining, 2015 (pp. 149–158). ACM.

  • Frandsen, T. F., & Nicolaisen, J. (2012). Effects of academic experience and prestige on researchers’ citing behavior. Journal of the American Society for Information Science and Technology,63(1), 64–71.

    Google Scholar 

  • García-Pérez, M. A. (2013). Limited validity of equations to predict the future h index. Scientometrics,96(3), 901–909.

    Google Scholar 

  • Guan, J., Yan, Y., & Zhang, J. J. (2017). The impact of collaboration and knowledge networks on citations. Journal of Informetrics,11(2), 407–422.

    Google Scholar 

  • Han, P., Shi, J., Li, X., Wang, D., Shen, S., & Su, X. (2014). International collaboration in LIS: Global trends and networks at the country and institution level. Scientometrics,98(1), 53–72.

    Google Scholar 

  • Havemann, F., & Larsen, B. (2015). Bibliometric indicators of young authors in astrophysics: Can later stars be predicted? Scientometrics,102(2), 1413–1434.

    Google Scholar 

  • Hirsch, J. E. (2007). Does the h index have predictive power? Proceedings of the National Academy of Sciences,104(49), 19193.

    Article  Google Scholar 

  • Hönekopp, J., & Khan, J. (2012). Future publication success in science is better predicted by traditional measures than by the h index. Scientometrics,90(3), 843–853.

    Google Scholar 

  • Hu, Z., Chen, C., & Liu, Z. (2014). How are collaboration and productivity correlated at various career stages of scientists? Scientometrics,101(2), 1553–1564.

    Google Scholar 

  • Huang, M.-H., & Chang, Y.-W. (2011). A study of interdisciplinarity in information science: Using direct citation and co-authorship analysis. Journal of Information Science,37(4), 369–378.

    Google Scholar 

  • Jensen, P., Rouquier, J.-B., & Croissant, Y. (2009). Testing bibliometric indicators by their prediction of scientists promotions. Scientometrics,78(3), 467–479.

    Google Scholar 

  • Jin, B. (2006). H-index: An evaluation indicator proposed by scientist. Science Focus,1(1), 8–9.

    Google Scholar 

  • Kademani, B., Kumar, V., Surwase, G., Sagar, A., Mohan, L., Gaderao, C., et al. (2005). Scientometric dimensions of innovation communication productivity of the Chemistry Division at Bhabha Atomic Research Centre. Malaysian Journal of Library & Information Science,10(1), 65–89.

    Google Scholar 

  • Keith, T. Z. (2014). Multiple regression and beyond: An introduction to multiple regression and structural equation modeling. New York: Routledge.

    Google Scholar 

  • Kim, J. (2019). Author-based analysis of conference versus journal publication in computer science. Journal of the Association for Information Science and Technology,70(1), 71–82.

    Google Scholar 

  • Kousha, K., & Thelwall, M. (2007). Google Scholar citations and Google Web/URL citations: A multi-discipline exploratory analysis. Journal of the American Society for Information Science and Technology,58(7), 1055–1065.

    Google Scholar 

  • Larivière, V., Sugimoto, C. R., & Cronin, B. (2012). A bibliometric chronicling of library and information science’s first hundred years. Journal of the American Society for Information Science and Technology,63(5), 997–1016.

    Google Scholar 

  • Lee, D. H. (2019). Predictive power of conference-related factors on citation rates of conference papers. Scientometrics,118(1), 281–304.

    Google Scholar 

  • Levitt, J. M., & Thelwall, M. (2016). Long term productivity and collaboration in information science. Scientometrics,108(3), 1103–1117.

    Google Scholar 

  • Li, E. Y., Liao, C. H., & Yen, H. R. (2013). Co-authorship networks and research impact: A social capital perspective. Research Policy,42(9), 1515–1530.

    Google Scholar 

  • Lindahl, J. (2018). Predicting research excellence at the individual level: The importance of publication rate, top journal publications, and top 10% publications in the case of early career mathematicians. Journal of Informetrics,12(2), 518–533.

    Google Scholar 

  • Lindahl, J., & Danell, R. (2016). The information value of early career productivity in mathematics: A ROC analysis of prediction errors in bibliometricly informed decision making. Scientometrics,109(3), 2241–2262.

    Google Scholar 

  • Lisée, C., Larivière, V., & Archambault, É. (2008). Conference proceedings as a source of scientific information: A bibliometric analysis. Journal of the American Society for Information Science and Technology,59(11), 1776–1784.

    Google Scholar 

  • McCarty, C., Jawitz, J. W., Hopkins, A., & Goldman, A. (2013). Predicting author h-index using characteristics of the co-author network. Scientometrics,96(2), 467–483.

    Google Scholar 

  • Monge, P. R., & Contractor, N. S. (2003). Theories of communication networks. Oxford: Oxford University Press.

    Google Scholar 

  • Nederhof, A. J. (2006). Bibliometric monitoring of research performance in the social sciences and the humanities: A review. Scientometrics,66(1), 81–100.

    Google Scholar 

  • Newman, M. E. J. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences,101(suppl 1), 5200–5205.

    Google Scholar 

  • Onodera, N., & Yoshikane, F. (2015). Factors affecting citation rates of research articles. Journal of the Association for Information Science and Technology,66(4), 739–764.

    Google Scholar 

  • Pasterkamp, G., Rotmans, J., de Kleijn, D., & Borst, C. (2007). Citation frequency: A biased measure of research impact significantly influenced by the geographical origin of research articles. Scientometrics,70(1), 153–165.

    Google Scholar 

  • Penner, O., Pan, R. K., Petersen, A. M., Kaski, K., & Fortunato, S. (2013). On the predictability of future impact in science. Scientific Reports,3, 3052.

    Google Scholar 

  • Petersen, A. M., Jung, W.-S., Yang, J.-S., & Stanley, H. E. (2011). Quantitative and empirical demonstration of the Matthew effect in a study of career longevity. Proceedings of the National Academy of Sciences,108(1), 18–23.

    Google Scholar 

  • Procter, R., Williams, R., Stewart, J., Poschen, M., Snee, H., Voss, A., et al. (2010). Adoption and use of Web 2.0 in scholarly communications. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences,368(1926), 4039–4056.

    Google Scholar 

  • Schreiber, M. (2007). Self-citation corrections for the Hirsch index. EPL (Europhysics Letters),78(3), 30002.

    Google Scholar 

  • Symonds, M. R., Gemmell, N. J., Braisher, T. L., Gorringe, K. L., & Elgar, M. A. (2006). Gender differences in publication output: Towards an unbiased metric of research performance. PLoS ONE,1(1), e127.

    Google Scholar 

  • Tahamtan, I., Safipour Afshar, A., & Ahamdzadeh, K. (2016). Factors affecting number of citations: A comprehensive review of the literature. Scientometrics,107(3), 1195–1225.

    Google Scholar 

  • Takeda, Y., & Kajikawa, Y. (2010). Tracking modularity in citation networks. Scientometrics,83(3), 783–792.

    Google Scholar 

  • Thelwall, M., & Wilson, P. (2014). Regression for citation data: An evaluation of different methods. Journal of Informetrics,8(4), 963–971.

    Google Scholar 

  • Thunnissen, M., Boselie, P., & Fruytier, B. (2013). Talent management and the relevance of context: Towards a pluralistic approach. Human Resource Management Review,23(4), 326–336.

    Google Scholar 

  • Uddin, S., Hossain, L., & Rasmussen, K. (2013). Network effects on scientific collaborations. PLoS ONE,8(2), e57546.

    Google Scholar 

  • Waltman, L. (2016). A review of the literature on citation impact indicators. Journal of Informetrics,10(2), 365–391.

    Google Scholar 

  • Waltman, L., & Van Eck, N. J. (2009). A taxonomy of bibliometric performance indicators based on the property of consistency. Paper presented at the Proceedings of the 12th international conference on Scientometrics and Informetrics.

  • Waltman, L., van Eck, N. J., & Wouters, P. (2013). Counting publications and citations: Is more always better? Journal of Informetrics,7(3), 635–641.

    Google Scholar 

Download references

Acknowledgements

I would like to express my deep gratitude to the anonymous reviewer for his/her detailed and insightful comments, which have improved the overall quality of this manuscript.

Author information

Authors and Affiliations

  1. Department of Software, Sangmyung University, 31, Sangmyungdae-gil, Dongnam-gu, Cheonan-si, Chungcheongnam-do, 31066, South Korea

    Danielle H. Lee

Authors
  1. Danielle H. Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Danielle H. Lee.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, D.H. Predicting the research performance of early career scientists. Scientometrics 121, 1481–1504 (2019). https://doi.org/10.1007/s11192-019-03232-7

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-019-03232-7

Keywords

Search

Navigation