Skip to main content
SpringerLink
Log in

The impact of a paper’s new combinations and new components on its citation

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

A paper’s novelty enhances its impact and citation. In this paper, we examine two dimensions of a paper’s novelty: new combinations and new components. We define new combinations as new pairs of knowledge elements in a related research area, and new components as new knowledge elements that have never appeared in a related research area previously. The importance of both dimensions is stressed, and we analyze the mechanisms that affect the frequency of a paper’s citation; we believe that a paper’s new combinations and new components both have an inverted U-shaped effect on its citation count. Utilizing a text-mining approach, we develop a novel method for constructing new combinations and new components using a paper’s keywords. Using keywords from papers published in the wind energy field between 2002 and 2015 as our sample, we conduct an empirical analysis on the above-mentioned relationships. To do so, we use the negative binomial regression method and several robustness tests. The results provide support for our hypotheses that propose a paper’s new combinations and new components significantly affect its impact. Specifically, new combinations and new components lead to more citation counts up to a specific threshold. When the number of new combinations and new components exceed the threshold, the paper is likely to be cited less frequently. Finally, we discuss the theoretical contributions, methodological contributions, and practical implications of these findings.

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

Notes

  1. A knowledge element refers to a socially defined category, including a set of tentative findings by the scientific or technological research community about facts, theories, operations, and procedures surrounding a subject (Wang et al. 2014). In this paper, we use keywords provided in scientific papers to indicate knowledge elements. Publication keywords are considered essential elements of identifying the primary focus of research and are often used to reveal knowledge structures in bibliometric research (Su and Lee 2010; Zhang et al. 2015). Meanwhile, keywords are generally treated as the main method by which papers on related topics are identified and retrieved (McCain 1989). Therefore, we utilized a text mining approach to analyze knowledge structures and measured the new combinations and new components by using the keywords of a paper, which is different from prior studies using reference or citation approaches to measure scientific novelty (Uzzi et al. 2013). This approach is consistent with the viewpoint that innovation is an evolutionary search process which involves knowledge combination and generation (Lee et al. 2015).

References

  • Abbasi, A., & Jaafari, A. (2013). Research impact and scholars’ geographical diversity. Journal of Informetrics,7(3), 683–692.

    Google Scholar 

  • Agovino, M., Aldieri, L., Garofalo, A., & Vinci, C. P. (2017). Quality and quantity in the innovation process of firms: A statistical approach. Quality & Quantity,51(4), 1579–1591.

    Google Scholar 

  • Ahuja, G., & Lampert, C. M. (2001). Entrepreneurship in the large corporation: A longitudinal study of how established firms create breakthrough inventions. Strategic Management Journal,22(6/7), 521–543.

    Google Scholar 

  • Ajiferuke, I., & Famoye, F. (2015). Modelling count response variables in informetric studies: Comparison among count, linear, and lognormal regression models. Journal of Informetrics,9(3), 499–513.

    Google Scholar 

  • Arthur, W. B. (2009). The nature of technology: What it is and how it evolves. New York: Free Press.

    Google Scholar 

  • Batista, P. D., Campiteli, M. G., & Kinouchi, O. (2006). Is it possible to compare researchers with different scientific interests? Scientometrics,68(1), 179–189.

    Google Scholar 

  • Bensman, S. J. (2008). Distributional differences of the impact factor in the sciences versus the social sciences: An analysis of the probabilistic structure of the 2005 journal citation reports. Journal of the American Society for Information Science and Technology,59(9), 1366–1382.

    Google Scholar 

  • Bornmann, L., & Daniel, H. (2006). Selecting scientific excellence through committee peer review—a citation analysis of publications previously published to approval or rejection of post-doctoral research fellowship applicants. Scientometrics,68(3), 427–440.

    Google Scholar 

  • Bornmann, L., & Daniel, H. (2008). What do citation counts measure? A review of studies on citing behavior. Journal of Documentation,64(1), 45–80.

    Google Scholar 

  • Bornmann, L., Mutz, R., Neuhaus, C., & Daniel, H. D. (2008). Citation counts for research evaluation: Standards of good practice for analyzing bibliometric data and presenting and interpreting results. Ethics in Science and Environmental Politics,8(1), 93–102.

    Google Scholar 

  • Boyack, K. W., & Börner, K. (2003). Indicator-assisted evaluation and funding of research: Visualizing the influence of grants on the number and citation counts of research papers. Journal of the American Society for Information Science and Technology,54(5), 447–461.

    Google Scholar 

  • Cannella, A. A., & McFadyen, M. A. (2016). Changing the exchange: The dynamics of knowledge worker ego networks. Journal of Management,42(4), 1005–1029.

    Google Scholar 

  • Carayol, N., Lahatte, A., & Llopis, O. (2019). The right job and the job right: Novelty, impact and journal stratification in science. Cahiers du GREThA, n°2019-05.

  • Chandonia, J., & Brenner, S. E. (2006). The impact of structural genomics: Expectations and outcomes. Science,311(5759), 347–351.

    Google Scholar 

  • Criscuolo, P., Dahlander, L., Grohsjean, T., & Salter, A. (2017). Evaluating novelty: The role of panels in the selection of R&D projects. Academy of Management Journal,60(2), 2014–2861.

    Google Scholar 

  • Dirk, L. (1999). A measure of originality: The elements of science. Social Studies of Science,29(5), 765–776.

    Google Scholar 

  • Fleming, L. (2001). Recombinant uncertainty in technological search. Management Science,47(1), 117–132.

    Google Scholar 

  • Foster, J. G., Rzhetsky, A., & Evans, J. A. (2015). Tradition and innovation in scientists’ research strategies. American Sociological Review,80(5), 875–908.

    Google Scholar 

  • Gallouj, F., & Weinstein, O. (1997). Innovation in services. Research Policy,26(4–5), 537–556.

    Google Scholar 

  • Garfield, E. (1979). Is citation analysis a legitimate evaluation tool? Scientometrics,1(4), 359–375.

    Google Scholar 

  • Guan, J. C., & Yan, Y. (2016). Technological proximity and recombinative innovation in the alternative energy field. Research Policy,45(7), 1460–1473.

    Google Scholar 

  • Guan, J. C., Yan, Y., & Zhang, J. J. (2015a). How do collaborative features affect scientific output? Evidences from wind power field. Scientometrics,102(1), 333–355.

    Google Scholar 

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

    Google Scholar 

  • Guan, J. C., Zhang, J. J., & Yan, Y. (2015b). The impact of multilevel networks on innovation. Research Policy,44(3), 545–559.

    Google Scholar 

  • Guetzkow, J., Lamont, M., & Mallard, G. (2004). What is originality in the humanities and the social sciences. American Sociological Review,69(2), 190–212.

    Google Scholar 

  • Haans, R. F. J., Pieters, C., & He, Z. L. (2016). Thinking about u: Theorizing and testing u- and inverted u-shaped relationships in strategy research. Strategic Management Journal,37(7), 1177–1195.

    Google Scholar 

  • Jamali, H. R., & Nikzad, M. (2011). Article title type and its relation with the number of downloads and citations. Scientometrics,88(2), 653–661.

    Google Scholar 

  • Jansen, B. J., & Pooch, U. (2001). A review of Web searching studies and a framework for future research. Journal of the American Society for Information Science and Technology,52(3), 235–246.

    Google Scholar 

  • Kaplan, S., & Vakili, K. (2015). The double-edged sword of recombination in breakthrough innovation. Strategic Management Journal,36(10), 1435–1457.

    Google Scholar 

  • Lee, D. H., & Brusilovsky, P. (2019). The first impression of conference papers: Does it matter in predicting future citations? Journal of the Association for Information Science and Technology,70(1), 83–95.

    Google Scholar 

  • Lee, Y., Walsh, J. P., & Wang, J. (2015). Creativity in scientific teams: Unpacking novelty and impact. Research Policy,44(3), 684–697.

    Google Scholar 

  • Lenzner, T., Kaczmirek, L., & Lenzner, A. (2010). Cognitive burden of survey questions and response times: A psycholinguistic experiment. Applied Cognitive Psychology,24(7), 1003–1020.

    Google Scholar 

  • Letchford, A., Preis, T., & Moat, H. S. (2016). The advantage of simple paper abstracts. Journal of Informetrics,10(1), 1–8.

    Google Scholar 

  • Leydesdorff, L., Bornmann, L., & Wagner, C. S. (2019). The relative influences of government funding and international collaboration on citation impact. Journal of the Association for Information Science and Technology,70(2), 198–201.

    Google Scholar 

  • Leydesdorff, L., Wagner, C. S., & Bornmann, L. (2018). Betweenness and diversity in journal citation networks as measures of interdisciplinarity—A tribute to Eugene Garfield. Scientometrics,114(2), 567–592.

    Google Scholar 

  • Lutz, T., Herrig, A., Würz, W., Kamruzzaman, M., & Krämer, E. (2017). Design and wind-tunnel verification of low-noise airfoils for wind turbines. AIAA Journal,45(4), 779–785.

    Google Scholar 

  • Mahoney, M. J. (1977). Publication prejudices: An experimental study of confirmatory bias in the peer review system. Cognitive Therapy and Research,1(2), 161–175.

    Google Scholar 

  • McCain, K. W. (1989). Descriptor and citation retrieval in the medical behavioral sciences literature: Retrieval overlaps and novelty distribution. Journal of the American Society for Information Science,40(2), 110–114.

    Google Scholar 

  • Min, C., Bu, Y., Sun, J., & Ding, Y. (2018). Is scientific novelty reflected in citation patterns? Proceedings of the Association for Information Science and Technology,55(1), 875–876.

    Google Scholar 

  • Nelson, R. R., & Winter, S. G. (1982). An evolutionary theory of economic change. Cambridge, MA: The Belknap Press of Harvard University Press.

    Google Scholar 

  • Phene, A., Fladmoe-Lindquist, K., & Marsh, L. (2006). Breakthrough innovations in the US biotechnology industry: The effects of technological space and geographic origin. Strategic Management Journal,27(4), 369–388.

    Google Scholar 

  • Sanz-Casado, E., Garcia-Zorita, J. C., Serrano-López, A. E., Larsen, B., & Ingwersen, P. (2013). Renewable energy research 1995–2009: A case study of wind power research in EU, Spain, Germany and Denmark. Scientometrics,95(1), 197–224.

    Google Scholar 

  • Saviotti, P. P., & Metcalfe, J. S. (1984). A theoretical approach to the construction of technological output indicators. Research Policy,13(3), 141–151.

    Google Scholar 

  • Schilling, M. A., & Green, E. (2011). Recombinant search and breakthrough idea generation: An analysis of high impact papers in the social sciences. Research Policy,40(10), 1321–1331.

    Google Scholar 

  • Schumpeter, J. A. (1934). The theory of economic development. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Skilton, P. F. (2006). A comparative study of communal practice: Assessing the effects of taken-for-granted-ness on citation practice in scientific communities. Scientometrics,68(1), 73–96.

    Google Scholar 

  • Sternberg, R. J. (1999). Handbook of creativity. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Strumsky, D., & Lobo, J. (2015). Identifying the sources of technological novelty in the process of invention. Research Policy,44(8), 1445–1461.

    Google Scholar 

  • Su, H., & Lee, P. (2010). Mapping knowledge structure by keyword co-occurrence: A first look at journal papers in technology foresight. Scientometrics,85(1), 65–79.

    Google Scholar 

  • Tahamtan, I., & Bornmann, L. (2018a). Core elements in the process of citing publications: Conceptual overview of the literature. Journal of Informetrics,12(1), 203–216.

    Google Scholar 

  • Tahamtan, I., & Bornmann, L. (2018b). Creativity in science and the link to cited references: Is the creative potential of papers reflected in their cited references? Journal of Informetrics,12(3), 906–930.

    Google Scholar 

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

    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 

  • Utterback, J. M. (1971). The process of innovation: A study of the origination and development of ideas for new scientific instruments. IEEE Transactions on Engineering Management,18(4), 124–131.

    Google Scholar 

  • Uzzi, B., Mukherjee, S., Stringer, M., & Jones, B. (2013). Atypical combinations and scientific impact. Science,342(6157), 468–472.

    Google Scholar 

  • Verhoeven, D., Bakker, J., & Veugelers, R. (2016). Measuring technological novelty with patent-based indicators. Research Policy,45(3), 707–723.

    Google Scholar 

  • Walters, G. D. (2006). Predicting subsequent citations to articles published in twelve crime-psychology journals: Author impact versus journal impact. Scientometrics,69(3), 499–510.

    Google Scholar 

  • Wang, C., Rodan, S., Fruin, M., & Xu, X. (2014). Knowledge networks, collaboration networks, and exploratory innovation. Academy of Management Journal,57(2), 484–514.

    Google Scholar 

  • Wang, J., & Shapira, P. (2011). Funding acknowledgement analysis: An enhanced tool to investigate research sponsorship impacts: The case of nanotechnology. Scientometrics,87(3), 563–586.

    Google Scholar 

  • Wang, J., Veugelers, R., & Stephan, P. (2017). Bias against novelty in science: A cautionary tale for users of bibliometric indicators. Research Policy,46(8), 1416–1436.

    Google Scholar 

  • Yan, Y., Dong, J. Q., & Faems, D. (2019). Not every coopetitor is the same: The impact of technological, market and geographical overlap with coopetitors on firms’ breakthrough inventions. Long Range Planning. https://doi.org/10.1016/j.lrp.2019.02.006.

    Article  Google Scholar 

  • Zhang, J. J., & Guan, J. C. (2016). Scientific relatedness and intellectual base: A citation analysis of un-cited and highly-cited papers in the solar energy field. Scientometrics,110(1), 1–22.

    MathSciNet  Google Scholar 

  • Zhang, J. J., Yan, Y., & Guan, J. C. (2015). Scientific relatedness in solar energy: A comparative study between the USA and China. Scientometrics,102(2), 1595–1613.

    Google Scholar 

  • Zhang, J. J., Yan, Y., & Guan, J. C. (2019). Recombinant distance, network governance and recombinant innovation. Technological Forecasting and Social Change,143, 260–272.

    Google Scholar 

Download references

Acknowledgements

This study is supported by National Natural Science Foundation of China (Grant No. 71904191), and by University of Chinese Academy of Sciences (Grant No. Y95402JXX2). This study is supported by the joint PhD programme scholarship from Business School, Renmin University of China. The authors are very grateful for the valuable comments and suggestions from Prof. Editor Wolfgang Glänzel and two anonymous reviewers.

Author information

Authors and Affiliations

  1. School of Business, Renmin University of China, Beijing, 100872, People’s Republic of China

    Yan Yan & Shanwu Tian

  2. School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, 100049, People’s Republic of China

    Jingjing Zhang

Authors
  1. Yan Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shanwu Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jingjing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jingjing Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yan, Y., Tian, S. & Zhang, J. The impact of a paper’s new combinations and new components on its citation. Scientometrics 122, 895–913 (2020). https://doi.org/10.1007/s11192-019-03314-6

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-019-03314-6

Keywords

Search

Navigation