Abstract
This paper provides useful insights for the design of networks that promote research productivity. The results suggest that the different dimensions of social capital affect scientific performance differently depending on the area of knowledge. Overall, dense networks negatively affect the creation of new knowledge. In addition, the analysis shows that a division of labor in academia, in the sense of interdisciplinary research, increases the productivity of researchers. It is also found that the position in a network is critical. Researchers who are central tend to create more knowledge. Finally, the findings suggest that the number of ties have a positive impact on future productivity. Related to areas of knowledge, Exact Sciences is the area in which social capital has a stronger impact on research performance. On the other side, Social and Humanities, as well as Engineering, are the ones in which social capital has a lesser effect. The differences found across multiple domains of science suggest the need to consider this heterogeneity in policy design.
Similar content being viewed by others
Notes
For this purpose, the ISI subject category of the papers was used. ISI uses 105 different subject category to classify papers/journals.
The Mexican National System of Researchers was created in 1984 to enhance the quality and productivity of researchers in Mexico. It gives pecuniary compensation, as a complement of salary, to the most productive researchers. The selection process is done by peer review committees organized by the same areas of knowledge that are used in this paper, except that in SNI Humanities and Social Sciences are separate areas. However, both areas have similar ISI publication patterns.
Researchers in SNI self-select their own area of knowledge.
The publications were obtained by matching the database of researchers in SNI, with Mexican articles from the ISI database from 1981–2002 (ISI, 2003). This matching was done manually, first checking the last names and initials, then verifying the affiliation institution, and finally the field of knowledge. To verify the reliability of the matching process, names of researchers were randomly selected, and their productivity corroborated against their official CVs (CVU), in a joint effort with the National Council for Science and Technology (Conacyt).
The threshold of no more than 8 authors was chosen to capture around 95 % of the total number of articles. According to the ISI database, only 4 % of the articles reported in the Mexican database had more than 8 authors. Most of these articles are in the Exact Sciences. The number of articles with no more than 7 authors is 94 % and with no more than 9 authors is 98 %.
As a measure of reputation, the number of publications with a foreign address is used.
The classification was constructed by considering 11 different fields of knowledge that match the 105 ISI categories into subject groups. This mapping was based on an analysis of journal usage by researchers working in different subject departments in UK universities (Adams, 1998). The categories used are the ones published by ISI in 2003.
The “adjacent” matrix is a matrix composed of as many rows and columns as there are researchers, and where the elements represent the ties between actors, this is, the number of joint publications.
We also run regressions using a 4 and a 5 year window. However, no significant differences were found in the coefficients of the network variables.
This classification is the same one used by the Mexican System of Researchers (SNI).
References
Adams, J. (1998). Benchmarking International Research. Nature, 396, 615.
Adams, J. D., Black, G. C., Clemmons, J. R., & Stephan, P. E. (2005). Scientific teams and institutional collaborations: Evidence from U.S. universities, 1981–1999. Research Policy, 34(3), 259–285.
Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: A longitudinal study. Administrative Science Quarterly, 45(3), 425–455.
Barnett et al. (1988). The rising evidence of coauthorshipin economics: further evidence. The Review of Economics and Statistics, 70(3), 539–543.
Borgatti, S.P. (1995). Centrality and AIDS. Connections, 18(1), 112–115.
Burt, R. S. (1992). Structural holes: The social structure of competition. Cambridge: Harvard University Press.
Burt, R. S. (2001). Structural holes versus network closure as social capital. In N. Lin, K. S. Cook, & R. S. Burt (Eds.), Social capital: Theory and research. Aldine de Gruyter.
Burt, R. S. (2004). Structural holes and good ideas. American Journal of Sociology, 110, 349–399.
Coleman, J. S. (1998). Social capital in the creation of human capital. American Journal of Sociology, 94, 95–120.
CONACYT. (2003). Indicadores de Actividades Científicas y Tecnológicas 2002, Mexico.
Cross, R., & Cumming, J. N. (2004). Tie and network correlates of individual performance in knowledge-intensive work. Academy of Management Journal, 7(6), 928–937.
Cummings, J., & Kiesler, S. (2005). Collaborative research across disciplinary and organizational boundaries. Social Studies of Science, 35(5), 703–722.
Dasgupta, P. (2005). Economics of Social Capital. The Economic Record, 81, s1–s21.
Etzkowitz, H., & Leydesdorff, L. (1997). Universities and the global knowledge economy: A triple helix of university–industry–government relations. London: Pinter.
European Commission (1995). White paper on education and learning—towards the learning society, November, COM, 590.
Freeman, C. (1982). The economics of industrial innovation. Cambridge, MA: MIT Press.
Gonzalez-Brambila, C., & Veloso, F. (2007). The determinants of research output and impact: A study of mexican researchers. Research Policy, 36, 1035–1051.
Gonzalez-Brambila, C., Veloso, F., & Krackhardt, D. (2013). The impact of network embeddedness on research output. Research Policy, 42(9), 1555–1567.
Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380.
Hanneman, R. (2001). Introduction to social network methods. California: Department of Sociology, University of California.
Hausman, J., Hall, B., & Griliches, Z. (1984). Econometric models for count data with an application to the patents–R&D relationship. Econometrica, 52(July), 909–938.
Inkpen, A. C., & Tsang, E. W. K. (2005). Social capital, networks, and knowledge transfer. Academy of Management Review, 30(1), 146–165.
Institute of Scientific Information, ISI. (2003, April 18). National Citation Report for Mexico. Articles from ISI database years 1981–2002 cited in the period 1981–2002. Citing and cited papers are included. Project 2487.
Katz, J. S. (1994). Geographical proximity of scientific collaboration. Scientometrics, 31(1), 31–43.
Levin, S., & Stephan, P. (1991). Research productivity over the life cycle: Evidence for academic scientists. The American Economic Review, 81, 114–132.
McFadyen, M. A., & Cannella, A. (2004). Social capital and knowledge creation: Diminishing returns of the number and strength of exchange relationships. Academy of Management Journal, 47(5), 735–746.
Melin, G. (1999). Impact of national size on research collaboration: A comparison between Northern European and American Universities. Scientometrics, 46(1), 161–170.
Melin, G., & Persson, O. (1996). Studying research collaborations using co-authorship. Scientometrics, 36(3), 363–377.
Merton, R. (1968). The Matthew effect in science. The reward and communication systems of science are considered. Science, 159(3810), 56–63.
Nahapiet, J., & Ghoshal, S. (1998). Social capital, intellectual capital, and the organizational advantage. The Academy of Management Review, 23(2), 242–266.
Narin, F., Stevens, K., & Whitlow, E. S. (1991). Scientific cooperation in Europe and the citation of multinationally authored papers. Scientometrics, 21, 313–323.
Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5(1), 14–37.
Pasterkamp, G., Rotmans, J., Kleijn, D., & Borst, C. (2007). Citation frequency: A biased measure of research impact significantly influenced by the geographical origin of research articles. Scientometrics, 70, 153–165.
Reagans, R., McEvily, B. (2003). Network structure and knowledge transfer: The effectsof cohesion and range. Administrative Science Quarterly, 48(2), 240–267.
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.
Singh, J. (2007). External collaboration, social networks and knowledge creation: Evidence from scientific publications. Mimeo: INSEAD.
Stephan, P. (1996). The Economics of Science”. Journal of Economic Literature, 34, 1199–1235.
Stephan, P., & Levin, S. (1997). The critical importance of careers in collaborative scientific research. Revue d’Economie Industrielle, 79(1), 45–61.
Tsai, W., & Ghoshal, S. (1998). Social capital and value creation: The role of intrafirm networks. Academy of Management Journal, 41, 464–476.
Turner, L., & Mairesse, J. (2003). Explaining individual productivity differences in scientific research productivity: How important are institutional and individual determinants? An econometric analysis of the publications of French CNRS physicists in condensed.
Van Raan, A. F. J. (1998). The influence of international collaboration on the impact of research results. Scientometrics, 42(3), 423–428.
Wang, Y., Wu, Y., Pan, Y., Ma, Z., & Rousseau, R. (2005). Scientific collaboration in china as reflected in co-authorship. Scientometrics, 62(2), 183–198.
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.
Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data. Cambridge: The MIT Press.
Acknowledgments
The author would like to thank the Instituto Tecnológico Autónomo de México (ITAM) and the Asociación Mexicana de Cultura AC for their support of this work.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gonzalez-Brambila, C.N. Social capital in academia. Scientometrics 101, 1609–1625 (2014). https://doi.org/10.1007/s11192-014-1424-2
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-014-1424-2