[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Scientific relatedness and intellectual base: a citation analysis of un-cited and highly-cited papers in the solar energy field

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

In the solar energy field, scientists publish numerous scientific articles every year. Some are highly-cited, while others may not even be cited. In this paper, we introduce two underlying scientific properties of a paper to explain this paper’s highly-cited or un-cited probability: scientific relatedness and intellectual base. We utilize two main network techniques, knowledge element coupling network (concurrence-based) and paper citation network (citation-based) analyses, to measure scientific relatedness and intellectual base, respectively. What’s more, we conduct descriptive analyses of un-cited and highly-cited papers at the country, organization and journal levels. Then we map knowledge element co-occurrence networks and paper citation networks to compare the network characteristics of un-cited and highly-cited papers. Further, we use article data in the solar energy field between 2004 and 2010 to examine our hypotheses. Findings from Ordered Logit Models indicate that when the scientific relatedness of a paper is high, this paper is more likely to be un-cited, whereas less likely to be highly-cited. The paper with higher intellectual base has a higher possibility to be highly-cited, whereas a low possibility to be un-cited. Overall, this paper provides important insights into the determinant factors of a paper’s citation levels, which is helpful for researchers maximizing the scientific impact of their efforts.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Ahmed, T., Johnson, B., Oppenheim, C., & Peck, C. (2004). Highly cited old papers and the reasons why they continue to be cited. Part II., The 1953 Watson and Crick article on the structure of DNA. Scientometrics, 61(2), 147–156.

    Article  Google Scholar 

  • Akhavan, P., Ebrahim, N. A., Fetrati, M. A., & Pezeshkan, A. (2016). Major trends in knowledge management research: A bibliometric study. Scientometrics. doi:10.1007/s11192-016-1938-x.

    Google Scholar 

  • Amemiya, T. (1981). Qualitative response models: A survey. Journal of Economic Literature, 19(4), 1483–1536.

    Google Scholar 

  • Asheim, B. T., & Coenen, L. (2005). Knowledge bases and regional innovation systems: Comparing Nordic clusters. Research Policy, 34(8), 1173–1190.

    Article  Google Scholar 

  • Bornmann, L., de Moya Anegón, F., & Leydesdorff, L. (2010). Do scientific advancements lean on the shoulders of giants? A bibliometric investigation of the Ortega hypothesis. PLoS ONE, 5(10), e13327.

    Article  Google Scholar 

  • Bornmann, L., Mutz, R., Marx, W., Schier, H., & Daniel, H. D. (2011). A multilevel modelling approach to investigating the predictive validity of editorial decisions: Do the editors of a high profile journal select manuscripts that are highly cited after publication? Journal of the Royal Statistical Society: Series A (Statistics in Society), 174(4), 857–879.

    Article  MathSciNet  Google Scholar 

  • Bornmann, L., Schier, H., Marx, W., & Daniel, H.-D. (2012). What factors determine citation counts of publications in chemistry besides their quality? Journal of Informetrics, 6(1), 11–18.

    Article  Google Scholar 

  • Burrell, Q. L. (2003). Predicting future citation behavior. Journal of the American Society for Information Science and Technology, 54(5), 372–378.

    Article  Google Scholar 

  • Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377.

    Article  Google Scholar 

  • Cheon, Y.-J., Choi, S. K., Kim, J., & Kwak, K. T. (2015). Antecedents of relational inertia and information sharing in SNS usage: The moderating role of structural autonomy. Technological Forecasting and Social Change, 95, 32–47.

    Article  Google Scholar 

  • Du, H., Li, N., Brown, M. A., Peng, Y., & Shuai, Y. (2014). A bibliographic analysis of recent solar energy literatures: The expansion and evolution of a research field. Renewable Energy, 66, 696–706.

    Article  Google Scholar 

  • Falagas, M. E., Pitsouni, E. I., Malietzis, G. A., & Pappas, G. (2008). Comparison of PubMed, Scopus, web of science, and Google scholar: Strengths and weaknesses. The FASEB Journal, 22(2), 338–342.

    Article  Google Scholar 

  • Friedman, D. D., Landes, W. M., & Posner, R. A. (1991). Some economics of trade secret law. The Journal of Economic Perspectives, 5(1), 61–72.

    Article  Google Scholar 

  • Garfield, E. (1979). Cirution indexing. New York: Wiley.

    Google Scholar 

  • Guan, J. C., & Liu, N. (2015). Invention profiles and uneven growth in the field of emerging nano-energy. Energy Policy, 76, 146–157.

    Article  Google Scholar 

  • Guan, J. C., & Yan, Y. (2016). Technological proximity and recombinative innovation in the alternative energy field. Research Policy. doi:10.1016/j.respol.2016.05.002.

    Google Scholar 

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

    Article  Google Scholar 

  • HaCohen-Kerner, Y. (2003). Automatic extraction of keywords from abstracts. In V. Palade, R. J. Howlett, & L. Jain (Eds.), Knowledge-based intelligent information and engineering systems: Proceedings of the 7th international conference, KES 2003, Oxford, UK, September 2003, Part I (pp. 843–849). Berlin, Heidelberg: Springer.

    Chapter  Google Scholar 

  • Hagstrom, W. O. (1965). The scientific community. New York: Basic books.

    Google Scholar 

  • Hammarfelt, B. (2010). Interdisciplinarity and the intellectual base of literature studies: Citation analysis of highly cited monographs. Scientometrics, 86(3), 705–725.

    Article  Google Scholar 

  • Karsai, M., Perra, N., & Vespignani, A. (2014). Time varying networks and the weakness of strong ties. Scientific Reports. doi:10.1038/srep04001.

    Google Scholar 

  • Kazi, P., Patwardhan, M., & Joglekar, P. (2016). Towards a new perspective on context based citation index of research articles. Scientometrics, 107(1), 103–121.

    Article  Google Scholar 

  • Koschützki, D., & Schreiber, F. (2008). Centrality analysis methods for biological networks and their application to gene regulatory networks. Gene Regulation and Systems Biology, 2, 193.

    Google Scholar 

  • Lee, B., & Jeong, Y.-I. (2008). Mapping Korea’s national R&D domain of robot technology by using the co-word analysis. Scientometrics, 77(1), 3–19.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Lewis, N. S., & Nocera, D. G. (2006). Powering the planet: Chemical challenges in solar energy utilization. Proceedings of the National Academy of Sciences, 103(43), 15729–15735.

    Article  Google Scholar 

  • Lewison, G., Thornicroft, G., Szmukler, G., & Tansella, M. (2007). Fair assessment of the merits of psychiatric research. The British Journal of Psychiatry, 190(4), 314–318.

    Article  Google Scholar 

  • Leydesdorff, L. (2008). On the normalization and visualization of author co-citation data: Salton’s Cosine versus the Jaccard index. Journal of the American Society for Information Science and Technology, 59(1), 77–85.

    Article  Google Scholar 

  • Li, X., Chen, H., Huang, Z., & Roco, M. C. (2007). Patent citation network in nanotechnology (1976–2004). Journal of Nanoparticle Research, 9(3), 337–352.

    Article  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.

    Article  Google Scholar 

  • MacRoberts, M. H., & MacRoberts, B. R. (2010). Problems of citation analysis: A study of uncited and seldom-cited influences. Journal of the American Society for Information Science and Technology, 61(1), 1–12.

    Article  Google Scholar 

  • McClellan, J. E. (2003). Specialist control: The publications committee of the Académie Royale des Sciences (Paris) 1700–1793. Transactions of the American Philosophical Society, 93(3), i-134.

    Article  Google Scholar 

  • Merton, R. K. (1979). The sociology of science: An episodic memoir. Carbondale: Southern Illinois University Press.

    Google Scholar 

  • Merton, R. K. (1988). The Matthew effect in science, II: Cumulative advantage and the symbolism of intellectual property. Isis, 79(4), 606–623.

    Article  Google Scholar 

  • Muñoz-Leiva, F., Viedma-del-Jesús, M. I., Sánchez-Fernández, J., & López-Herrera, A. G. (2012). An application of co-word analysis and bibliometric maps for detecting the most highlighting themes in the consumer behaviour research from a longitudinal perspective. Quality & Quantity, 46(4), 1077–1095.

    Article  Google Scholar 

  • Narin, F. (1976). Evaluative bibliometrics: The use of publication and citation analysis in the evaluation of scientific activity. Washington, DC: Computer Horizons.

    Google Scholar 

  • O’brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5), 673–690.

    Article  Google Scholar 

  • Oppenheim, C., & Renn, S. P. (1978). Highly cited old papers and the reasons why they continue to be cited. Journal of the American Society for Information Science, 29(5), 225–231.

    Article  Google Scholar 

  • Persson, O. (1994). The intellectual base and research fronts of “jasis” 1986–1990. Journal of the American Society for Information Science, 45(1), 31–38.

    Article  Google Scholar 

  • Price, D. J. (1970). Citation measures of hard science, soft science, technology, and nonscience. Communication among scientists and engineers. Lexington: D.C. Heath and Company.

    Google Scholar 

  • Real, R., & Vargas, J. M. (1996). The probabilistic basis of Jaccard’s index of similarity. Systematic Biology, 45(3), 380–385.

    Article  Google Scholar 

  • Redner, S. (1998). How popular is your paper? An empirical study of the citation distribution. The European Physical Journal B-Condensed Matter and Complex Systems, 4(2), 131–134.

    Article  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.

    Article  Google Scholar 

  • Schubert, A., & Glänzel, W. (1983). Statistical reliability of comparisons based on the citation impact of scientific publications. Scientometrics, 5(1), 59–73.

    Article  Google Scholar 

  • Seglen, P. O. (1997). Why the impact factor of journals should not be used for evaluating research. BMJ: British Medical Journal, 314(7079), 498–502.

    Article  Google Scholar 

  • Simard, C., & West, J. (2006). Knowledge networks and the geographic locus of innovation. Open innovation: Researching a new paradigm. Oxford: Oxford University Press.

    Google Scholar 

  • Small, H. (1981). The relationship of information science to the social sciences: A co-citation analysis. Information Processing and Management, 17(1), 39–50.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Takeda, Y., & Kajikawa, Y. (2008). Optics: A bibliometric approach to detect emerging research domains and intellectual bases. Scientometrics, 78(3), 543–558.

    Article  Google Scholar 

  • Tijssen, R., Visser, M., & van Leeuwen, T. (2002). Benchmarking international scientific excellence: Are highly cited research papers an appropriate frame of reference? Scientometrics, 54(3), 381–397.

    Article  Google Scholar 

  • Timilsina, G. R., Kurdgelashvili, L., & Narbel, P. A. (2012). Solar energy: Markets, economics and policies. Renewable and Sustainable Energy Reviews, 16(1), 449–465.

    Article  Google Scholar 

  • Uzzi, B. (1997). Social structure and competition in interfirm networks: The paradox of embeddedness. Administrative Science Quarterly, 42(1), 35–67.

    Article  Google Scholar 

  • Wang, J. (2016). Knowledge creation in collaboration networks: Effects of tie configuration. Research Policy, 45(1), 68–80.

    Article  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.

    Article  Google Scholar 

  • Weitzman, M. L. (1998). Recombinant growth. Quarterly Journal of Economics, 113(2), 331–360.

    Article  MATH  Google Scholar 

  • Yamashita, Y., & Yoshinaga, D. (2014). Influence of researchers’ international mobilities on publication: A comparison of highly cited and uncited papers. Scientometrics, 101(2), 1475–1489.

    Article  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.

    Article  Google Scholar 

Download references

Acknowledgments

This study is supported by Grants from National Natural Science Foundation of China (Nos. 71373254 and 71540034).

Author information

Authors and Affiliations

  1. School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, People’s Republic of China

    JingJing Zhang & Jiancheng Guan

Authors
  1. JingJing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiancheng Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiancheng Guan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, J., Guan, J. Scientific relatedness and intellectual base: a citation analysis of un-cited and highly-cited papers in the solar energy field. Scientometrics 110, 141–162 (2017). https://doi.org/10.1007/s11192-016-2155-3

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-016-2155-3

Keywords

Search

Navigation