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A bilateral comparison of research performance at an institutional level

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Abstract

An extensive body of research indicated that the USA and China were the first two largest producers in the nanoscience and nanotechnology field while China performed better than USA in terms of quantity; it had produced inferior quality publications. Yet, no studies investigated whether the specific institutions are consistent with these conclusions or not. In this study, we identify two institutions National Center for Nanoscience and Technology (NCNST) from China and University of California Los Angeles-California Nanosystems Institute (CNSI) from the USA) and compare their scientific research. Further, we develop and exploit a novel and updated dataset on paper co-authorship to assess their scientific research. Our analysis reveals NCNST has many advantages in regards to author and paper quantities, growth rate and the strength of collaborations but loses dominance with respect to research quality. We do find that the collaboration networks of both NCNST and CNSI have small-world and scale-free properties. Besides, the analysis of knowledge networks shows that they have similar research interests or hotspots. Using statistical models, we test and discover that degree centrality has a significant inverted-U shape effect on scientific output and influence. However, we fail to find any significant effect of structural holes.

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References

  • Abbasi, A., Altmann, J, & Hossain, L. (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, 5(4), 594–607.

    Article  Google Scholar 

  • Abbasi, A., Hossain, L., & Leydesdorff, L. (2012). Betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks. Journal of Informetrics, 6, 403–412.

    Article  Google Scholar 

  • Adams, J. (2012). Collaborations: The rise of research networks. Nature, 490(7420), 335–336.

    Article  Google Scholar 

  • Adams, J. (2013). Collaborations: The fourth age of research. Nature, 497(7451), 557–560.

    Article  Google Scholar 

  • Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: A longitudinal study. Administrative Science Quarterly, 45(3), 425–455.

    Article  MathSciNet  Google Scholar 

  • Ajiferuke, I., Burell, O., & Tague, J. (1988). Collaborative coefficient: A single measure of the collaboration in research. Scientometrics, 14, 421–433.

    Article  Google Scholar 

  • Albert, R., Jeong, H., & Barabási, A. L. (2000). Attack and error tolerance of complex networks. Nature, 406, 378–382.

    Article  Google Scholar 

  • Bajwa, R. S., Yaldram, K., & Rafique, S. (2013). A scientometric assessment of research output in nanoscience and nanotechnology: Pakistan perspective. Scientometrics, 94(1), 333–342.

    Article  Google Scholar 

  • Balconi, M., Breschi, S., & Lissoni, F. (2004). Networks of inventors and the role of academia: An exploration of Italian patent data. Research Policy, 33(1), 127–145.

    Article  Google Scholar 

  • Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.

    Article  MathSciNet  Google Scholar 

  • Bornmann, L., & Leydesdorff, L. (2014). Topical connections between the institutions within an organisation (institutional co-authorships, direct citation links and co-citations). Scientometrics,. doi:10.1007/s11192-014-1425-1.

    Google Scholar 

  • Burt, R. S. (1992). Structural holes: The social structure of competition. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Burt, R. S. (2004). Structural holes and good ideas. American Journal of Sociology, 110(2), 349–399.

    Article  Google Scholar 

  • Cainelli, G., Maggioni, M. A., Uberti, T. E., & Felice, A. D. (2015). The strength of strong ties: How co-authorship affect productivity of academic economists? Scientometrics, 102(1), 673–699.

    Article  Google Scholar 

  • Carnabuci, G., & Bruggeman, J. (2009). Knowledge specialization, knowledge brokerage and the uneven growth of technology domains. Social Forces, 88(2), 607–642.

    Article  Google Scholar 

  • CAS. (2003). http://www.cas.cn/xw/kjsm/gjdt/200906/t20090608_623423.shtml. Accessed September 25, 2014

  • Chen, Z. F., & Guan, J. C. (2010). The impact of small world on innovation: an empirical study of 16 countries. Journal of Informetrics, 4, 97–106.

    Article  MathSciNet  Google Scholar 

  • CNSI. (2014a). http://www1.cnsi.ucla.edu/index. Accessed September 25, 2014

  • CNSI. (2014b). http://www.cnsi.ucsb.edu/about/. Accessed September 25, 2014

  • CNSI. (2014c). http://www.cnsi.ucsb.edu/about/cnsi_brochure.pdf. Accessed September 25, 2014

  • CNSI. (2014d). http://www1.cnsi.ucla.edu/external-affairs/page2.html. Accessed September 25, 2014

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

    Article  Google Scholar 

  • Fleming, L., Mingo, S., & Chen, D. (2007). Brokerage and collaborative creativity. Administrative Science Quarterly, 52(3), 443–475.

    Google Scholar 

  • Fu, T. Z. J., Song, Q. Q., & Chiu, D. M. (2014). The academic social network. Scientometrics, 101, 203–239.

    Article  Google Scholar 

  • Gonzalez-Brambila, C. N., Veloso, F. M., & Krackhardt, D. (2013). The impact of network embeddedness on research output. Research Policy, 42, 1555–1567.

    Article  Google Scholar 

  • Guan, J. C., & Gao, X. (2008). Comparison and evaluation of Chinese research performance in the field of bioinformatics. Scientometrics, 75(2), 357–379.

    Article  Google Scholar 

  • Guan, J. C., & Liu, N. (2014). Measuring scientific research in emerging nano-energy field. Journal of Nanoparticle Research, 16, 2356.

    Article  Google Scholar 

  • Guan, J. C., & Ma, N. (2007). China’s emerging presence in nanoscience and nanotechnology: A comparative bibliometric study of several nanoscience ‘giants’. Research Policy, 36(6), 880–886.

    Article  Google Scholar 

  • Guan, J. C., & Wang, G. B. (2010). A comparative study of research performance in nanotechnology for China’s inventor–authors and their non-inventing peers. Scientometrics, 84, 331–343.

    Article  Google Scholar 

  • Guan, J. C., Yan, Y., & Zhang, J. J. (2014). How do collaborative features affect scientific output? Evidences from wind power field. Scientometrics,. doi:10.1007/s11192-014-1311-x.

    Google Scholar 

  • Heinze, T. (2004). Nanoscience and nanotechnology in Europe: Analysis of publications and patent applications including comparisons with the United States. Nanotechnology Law & Business, 1(4), 427–445.

    Google Scholar 

  • James, A. E., & Jacob, G. F. (2011). Metaknowledge. Science, 331, 721–725.

    Article  MATH  MathSciNet  Google Scholar 

  • Karpagam, R., Gopalakrishnan, S., Natarajan, M., & Ramesh Babu, B. (2011). Mapping of nanoscience and nanotechnology research in India: A scientometric analysis, 1990–2009. Scientometrics, 89, 501–522.

    Article  Google Scholar 

  • Kavyasrujana, D., & Rao, B. C. (2015). Hierarchical clustering for sentence extraction using cosine similarity measure. Advances in Intelligent Systems and Computing, 337, 185–191.

    Google Scholar 

  • Kostoff, R. N. (2012). China/USA nanotechnology research output comparison—2011 update. Technological Forecasting and Social Change, 79, 986–990.

    Article  Google Scholar 

  • Kostoff, R. N., Barth, R. B., & Lau, C. G. Y. (2008). Quality vs quantity of publications in nanotechnology field from the Peoples Republic of China. Chinese Science Bulletin, 53(8), 1272–1280.

    Article  Google Scholar 

  • Kostoff, R. N., Koytcheff, R. G., & Lau, C. G. Y. (2007). Global nanotechnology research metrics. Scientometrics, 70(3), 565–601.

    Article  Google Scholar 

  • Lee, J. (2010). Heterogeneity, brokerage, and innovative performance: Endogenous formation of collaborative inventor networks. Organization Science, 21(4), 804.

    Article  Google Scholar 

  • Lee, D. H., Seo, I. W., Choe, H. C., & Kim, H. D. (2012). Collaboration network patterns and research performance: The case of Korean public research institutions. Scientometrics, 91(3), 925–942.

    Article  Google Scholar 

  • Leydesdorff, L., & Wagner, C. (2009). Is the United States losing ground in science? A global perspective on the world science system. Scientometrics, 78(1), 23–36.

    Article  Google Scholar 

  • Mahapatra, M. (1985). On the validity of the theory of exponential growth of scientific literature. In Proceedings of the 15th IASLIC conference, Bangalore (pp. 61–70). Bangalore.

  • McFadyen, M. A., & Cannella, A. A. (2004). Social capital and knowledge creation: Diminishing returns of the number and strength of exchange. Academy of Management, 47(5), 735–746.

    Article  Google Scholar 

  • NCNST. (2014a). http://www.nanoctr.cn/. Accessed September 25, 2014.

  • NCNST. (2014b). http://www.nanoctr.cn/xwdt/xshd/201001/t20100128_2737716.html. Accessed March 25, 2014.

  • Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404–409.

    Article  MATH  MathSciNet  Google Scholar 

  • Phelps, C., Heidl, R., & Wadhwa, A. (2012). Knowledge, networks, and knoweldge networks: A review and research agenda. Journal of Management, 38(4), 1115–1166.

    Article  Google Scholar 

  • Podolny, J. M., & Baron, J. N. (1997). Resources and relationships: Social networks and mobility in the workplace. American Sociological Review, 62(5), 673–693.

    Article  Google Scholar 

  • Rodan, S. (2010). Structural holes and managerial performance: Identifying the underlying mechanisms. Social Networks, 32(3), 168–179.

    Article  Google Scholar 

  • Rodan, S., & Galunic, C. (2004). More than network structure: How knowledge heterogeneity influences managerial performance and innovativeness. Strategic Management Journal, 25, 541–562.

    Article  Google Scholar 

  • Rotolo, D., & Messeni Petruzzelli, A. (2013). When does centrality matter? Scientific productivity and the moderating role of research specialization and cross-community ties. Journal of Organizational Behavior, 34, 648–670.

    Article  Google Scholar 

  • Sabidussi, Gert. (1966). The centrality index of a graph. Psychometrika, 31(4), 581–603.

    Article  MATH  MathSciNet  Google Scholar 

  • Savanur, K., & Srikanth, R. (2010). Modified collaborative coefficient: A new measure for quantifying the degree of research collaboration. Scientometrics, 84(2), 365–371.

    Article  Google Scholar 

  • Shipilov, A. V. (2009). Firm scope experience, historic multimarket contact with partners, centrality, and the relationship between structural holes and performance. Organization Science, 20, 85–106.

    Article  Google Scholar 

  • Tang, L., & Shapira, P. (2011). China–US scientific collaboration in nanotechnology: patterns and dynamics. Scientometrics, 88(1), 1–16.

    Article  Google Scholar 

  • UCOP (University of California Office of the President). (2014). http://www.ucop.edu/california-institutes/about/about.htm. Accessed September 25, 2014

  • Uzzi, B., & Spiro, J. (2005). Collaboration and creativity: The small world problem. American Journal of Sociology, 111(2), 447–504.

    Article  Google Scholar 

  • Wang, G. B., & Guan, J. C. (2010). The role of patenting activity for scientific research: A study of academic inventors from China’s nanotechnology. Journal of Informetrics, 4(3), 338–350.

    Article  Google Scholar 

  • Wang, G. B., & Guan, J. C. (2011). Measuring science-technology interactions using patent citations and author–inventor links: An exploration analysis from Chinese nanotechnology. Journal of Nanoparticle Research, 13(12), 6245–6262.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Wang, X. W., Xu, S. M., Liu, D., & Liang, Y. X. (2012). The role of Chinese-American scientists in China–US scientific collaboration: A study in nanotechnology. Scientometrics, 91(3), 737–749.

    Article  Google Scholar 

  • Wang, X. W., Xu, S. M., Wang, Z., Peng, L., & Wang, C. L. (2013). International scientific collaboration of China: collaborating countries, institutions and individuals. Scientometrics, 95, 885–894.

    Article  Google Scholar 

  • Watts, D. J. (1999). Small worlds: The dynamics of networks between order and randomness. Princeton: Princeton University Press.

    Google Scholar 

  • Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of small-world. Nature, 393(6684), 440–442.

    Article  Google Scholar 

  • Yayavaram, S., & Ahuja, G. (2008). Decomposability in knowledge structures and its impact on the usefulness of inventions and knowledge-base malleability. Administrative Science Quarterly, 53, 333–362.

    Article  Google Scholar 

  • Zhao, Y. L. (2013). Nanosciences at NCNST: From fundamental research to industrial applications. Small (Weinheim an der Bergstrasse, Germany), 14, 2381.

    Article  Google Scholar 

  • Zhou, P., & Bornmann, L. (2014). An overview of academic publishing and collaboration between China and Germany. Scientometrics,. doi:10.1007/s11192-014-1418-0.

    Google Scholar 

  • Zhou, P., & Leydesdorff, L. (2006). The emergence of China as a leading nation in science. Research Policy, 35(1), 83–104.

    Article  Google Scholar 

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Acknowledgments

This research is funded by National Natural Science Foundation of China (Project No. 71373254). The authors are very grateful for the valuable comments and suggestions of the anonymous reviewers and Editor in Chief Wolfgang Glänzel, which significantly improved the article.

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Correspondence to Jiancheng Guan.

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Jiancheng Guan and He Wei are alphabetically ordered and they contributed equally to this paper.

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Guan, J., Wei, H. A bilateral comparison of research performance at an institutional level. Scientometrics 104, 147–173 (2015). https://doi.org/10.1007/s11192-015-1599-1

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