, Volume 97, Issue 3, pp 675–693 | Cite as

Network closure, brokerage, and structural influence of journals: a longitudinal study of journal citation network in Internet research (2000–2010)

  • Tai Quan Peng
  • Zhen-Zhen Wang


The study aims to assess journals’ structural influence in Internet research and uncover the impacts of network structures on journals’ structural influence drawing on theories of network closure and structural holes. The data of the study are the citation exchanges among 1,210 journals in Communication and other seven social scientific fields (i.e., Business, Economics/Finance, Education, Information Science, Political Science, Psychology, and Sociology) in Internet research. The top two most influential journals in Internet research are American Economic Review and Journal of Personality and Social Psychology. Journals in “Communication” field emerge to be an important source of influence in Internet research, whose mean structural influence ranks third among the eight fields, below “Business” and “Economics/Finance”, but above other five fields. Journals’ structural influences are found to grow over time and the growth rates vary across journals. Network brokerage is found to exert a significant impact on journals’ structural influence, while the impact of network closure on journals’ structural influences is not significant. The impact of network brokerage on journals’ structural influence will increase over time.


Internet research Network closure Brokerage Citation network Scholarly influence 



The study was supported in part by a GRF Grant (CityU154412) from the Hong Kong Research Grants Council.


  1. 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.CrossRefGoogle Scholar
  2. Abbasi, A., Chung, K. S. K., & Hossain, L. (2012a). Egocentric analysis of co-authorship network structure, position and performance. Information Processing and Management, 48(4), 671–679.CrossRefGoogle Scholar
  3. Abbasi, A., Hossain, L., & Leydesdorff, L. (2012b). Betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks. Journal of Informetrics, 6, 403–412.CrossRefGoogle Scholar
  4. Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: A longitudinal study. Administrative Science Quarterly, 45(3), 425–455.MathSciNetCrossRefGoogle Scholar
  5. Amsterdamska, O., & Leydesdorff, L. (1989). Citations: Indicators of significance? Scientometrics, 15, 449–471.CrossRefGoogle Scholar
  6. Barabasi, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.MathSciNetCrossRefGoogle Scholar
  7. Baron, N. S. (2005). Who wants to be a discipline? Information Society, 21(4), 269–271.MathSciNetCrossRefGoogle Scholar
  8. Baumgartner, H., & Pieters, R. (2003). The structural influence of marketing journals: A citation analysis of the discipline and its subareas over time. Journal of Marketing, 67(2), 123–139.CrossRefGoogle Scholar
  9. Berg, S., Duncan, J., & Friedman, P. (1982). Joint venture and corporate innovation. Cambridge, MA: Oelgeschlager, Gunn and Hain.Google Scholar
  10. Borgatti, S. P. (1995). Centrality and aids. Connections, 18(1), 112–114.Google Scholar
  11. Bourdieu, P., & Wacquant, L. J. D. (1992). An invitation to reflexive sociology. Chicago: University of Chicago Press.Google Scholar
  12. Brown, L. D., & Gardner, J. C. (1985). Using citation analysis to assess the impact of journals and articles on contemporary accounting research. Journal of Accounting Research, 23(1), 84–109.CrossRefGoogle Scholar
  13. Bryant, J., & Miron, D. (2004). Theory and research in mass communication. Journal of Communication, 54(4), 662–704.CrossRefGoogle Scholar
  14. Burt, R. S. (1992). Structural holes: The social structure of competition. Cambridge, MA: Harvard University Press.Google Scholar
  15. Burt, R. S. (1997). The contingent value of social capital. Administrative Science Quarterly, 42(2), 339–365.CrossRefGoogle Scholar
  16. Burt, R. S. (2000). The network structure of social capital. Research in Organization Behavior, 22, 345–423.CrossRefGoogle Scholar
  17. Burt, R. S. (2001). Structural holes versus network closure as social capital. In N. Lin, K. Cook, & R. S. Burt (Eds.), Social capital: Theory and research (pp. 31–56). New York: Aldine de Gruyter.Google Scholar
  18. Burt, R. S. (2004). Structural holes and good ideas. American Journal of Sociology, 110(2), 349–399.CrossRefGoogle Scholar
  19. Burt, R. S. (2005). Brokerage and closure: An introduction to social capital. Cambridge: Oxford University Press.Google Scholar
  20. Burt, R. S. (2010). Neighbor networks. Oxford: Oxford University Press.Google Scholar
  21. Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2009). Power-law distributions in empirical data. Siam Review, 51(4), 661–703.MathSciNetCrossRefzbMATHGoogle Scholar
  22. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Erlbaum.Google Scholar
  23. Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, S95–S120.CrossRefGoogle Scholar
  24. Cote, J. A., Leong, S. M., & Cote, J. (1991). Assessing the influence of journal of consumer research: A citation analysis. Journal of Consumer Research, 18, 402–410.CrossRefGoogle Scholar
  25. De Leeuw, J., & Kreft, I. G. G. (1995). Questioning multilevel methods. Journal of Educational and Behavioral Statistics, 20(2), 171–189.Google Scholar
  26. Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. New York: Cambridge University Press.CrossRefGoogle Scholar
  27. Franceschet, M. (2012). The large-scale structure of journal citation networks. Journal of the American Society for Information Science and Technology, 63(4), 837–842.MathSciNetCrossRefGoogle Scholar
  28. Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.CrossRefGoogle Scholar
  29. Garfield, E. (1979). Citation indexing: Its theory and application in science, technology, and humanities. New York: Wiley.Google Scholar
  30. Garfield, E. (2006). The history and meaning of the journal impact factor. Journal of the American Medical Association, 295, 90–93.CrossRefGoogle Scholar
  31. Gargiulo, M., & Benassi, M. (2000). Trapped in your own net? Network cohesion structural holes, and the adaptation of social capital. Organization Science, 11(2), 183–196.CrossRefGoogle Scholar
  32. Hedeker, D. (2004). An introduction to growth modeling. In D. Kaplan (Ed.), Quantitative methodology for the social sciences (pp. 215–234). Thousand Oaks, CA: Sage.Google Scholar
  33. Hite, J. M., & Hesterly, W. S. (2001). The evolution of firm networks: From emergence to early growth of the firm. Strategic Management Journal, 22(3), 275–286.CrossRefGoogle Scholar
  34. Hox, J. (2002). Multilevel analysis: Techniques and applications. Mahwah, NJ: Erlbaum.Google Scholar
  35. Jansen, D., von Gortz, R., & Heidler, R. (2010). Knowledge production and the structure of collaboration networks in two scientific fields. Scientometrics, 83(1), 219–241.CrossRefGoogle Scholar
  36. Johnson, J. L., & Podsakoff, P. M. (1994). Journal influence in the field of management: An analysis using Salancik’s index in a dependency network. Academy of Management Journal, 37(5), 1392–1407.CrossRefGoogle Scholar
  37. Kim, M. T. (1992). A comparison of 3 measures of journal status—influence weight, importance index, and measure of standing. Library & Information Science Research, 14(1), 75–96.Google Scholar
  38. Klenk, N. L., Hickey, G. M., & MacLellan, J. I. (2010). Evaluating the social capital accrued in large research networks: The case of the sustainable forest management network (1995–2009). Social Studies of Science, 40(6), 931–960.CrossRefGoogle Scholar
  39. Kwok, O. M., Underhill, A. T., Berry, J. W., Luo, W., Elliott, T. R., & Yoon, M. (2008). Analyzing longitudinal data with multilevel models: An example with individuals living with lower extremity intra-articular fractures. Rehabilitation Psychology, 53(3), 370–386.CrossRefGoogle Scholar
  40. Leibowitz, S. J., & Palmer, J. P. (1984). Assessing the relative impacts of economic journals. Journal of Economic Literature, 22, 77–88.Google Scholar
  41. Leydesdorff, L. (2003). Can networks of journal–journal citations be used as indicators of change in the social sciences? Journal of Documentation, 59(1), 84–104.CrossRefGoogle Scholar
  42. Milgram, S. (1967). The small world problem. Psychology Today, 1(1), 60–67.Google Scholar
  43. Moed, H. F., & Van Leeuwen, T. N. (1996). Impact factors can mislead. Nature, 381, 186.CrossRefGoogle Scholar
  44. Moed, H. F., Van Leeuwen, T. N., & Reedijk, J. (1999). Towards appropriate indicators of journal impact. Scientometrics, 46(3), 575–589.CrossRefGoogle Scholar
  45. Oh, W., Choi, J. N., & Kim, K. (2005). Coauthorship dynamics and knowledge capital: The patterns of cross-disciplinary collaboration in information systems research. Journal of Management Information Systems, 22(3), 265–292.Google Scholar
  46. Peng, T. Q., & Zhu, J. J. H. (2012). Where you publish matters most: A multi-level analysis of factors affecting citations of internet studies. Journal of the American Society for Information Science and Technology, 63(9), 1789–1803.CrossRefGoogle Scholar
  47. Peng, T. Q., Zhang, L., Zhong, Z.-J., & Zhu, J. J. H. (2013). Mapping the landscape of internet studies: Text mining of social science journal articles 2000–2009. New Media & Society,. doi: 10.1177/1461444812462846.Google Scholar
  48. Peugh, J. L., & Enders, C. K. (2005). Using the SPSS mixed procedure to fit cross-sectional and longitudinal multilevel models. Educational and Psychological Measurement, 65(5), 717–741.MathSciNetCrossRefGoogle Scholar
  49. Podolny, J. M. (2001). Networks as the pipes and prisms of the market. American Journal of Sociology, 107(1), 33–60.CrossRefGoogle Scholar
  50. Putnam, R. D. (1993). Making democracy work. Princeton, NJ: Princeton University Press.Google Scholar
  51. Raudenbush, S. W. (2001). Comparing personal trajectories and drawing causal inferences from longitudinal data. Annual Review of Psychology, 52, 501–525.CrossRefGoogle Scholar
  52. Reagans, R., & McEvily, B. (2003). Network structure and knowledge transfer: The effects of cohesion and range. Administrative Science Quarterly, 48(2), 240–267.CrossRefGoogle Scholar
  53. Reagans, R., & Zuckerman, E. W. (2001). Networks, diversity, and productivity: The social capital of corporate R&D teams. Organization Science, 12(4), 502–517.CrossRefGoogle Scholar
  54. Rowley, T., Behrens, D., & Krackhardt, D. (2000). Redundant governance structures: An analysis of structural and relational embeddedness in the steel and semiconductor industries. Strategic Management Journal, 21(3), 369–386.CrossRefGoogle Scholar
  55. Salancik, G. R. (1986). An index of subgroup influence in dependency networks. Administrative Science Quarterly, 31(2), 194–211.CrossRefGoogle Scholar
  56. Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis. New York: Oxford Press.CrossRefGoogle Scholar
  57. Snijders, T., & Bosker, R. (1999). Multilevel analysis: An introduction to basic and advanced multilevel modeling. London: Sage.zbMATHGoogle Scholar
  58. SPSS. (2005). Linear mixed-effects modeling in SPSS: An introduction to the mixed procedure. Retrieved from
  59. Stovel, K., & Shaw, L. (2012). Brokerage. Annual Review of Sociology, 38(1), 139–158.CrossRefGoogle Scholar
  60. van Campenhout, G., van Caneghem, T., & van Uytbergen, S. (2008). A comparison of overall and sub-area journal influence: The case of the accounting literature. Scientometrics, 77(1), 61–90.CrossRefGoogle Scholar
  61. Wang, J. C., Chiang, C. H., & Lin, S. W. (2010). Network structure of innovation: Can brokerage or closure predict patent quality? Scientometrics, 84(3), 735–748.CrossRefGoogle Scholar
  62. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442.CrossRefGoogle Scholar
  63. White, D. R., & Borgatti, S. P. (1994). Betweenness centrality measures for directed graph. Social Networks, 16, 335–346.CrossRefGoogle Scholar
  64. Zhang, X., & Wang, C. (2012). Network positions and contributions to online public goods: The case of Chinese Wikipedia. Journal of Management Information Systems, 29(2), 11–40.CrossRefGoogle Scholar
  65. Zhou, Y. B., Lv, L. Y., & Li, M. H. (2012). Quantifying the influence of scientists and their publications: Distinguishing between prestige and popularity. New Journal of Physics, 14, 1–17.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2013

Authors and Affiliations

  1. 1.Wee Kim Wee School of Communication and InformationNanyang Technological UniversitySingaporeSingapore
  2. 2.Department of Media and CommunicationCity University of Hong KongKowloonHong Kong

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