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

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Abstract

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.

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Notes

  1. These 13 subject categories include “Business”, “Business, Finance”, “Communication”, “Economics”, “Education and Education Research”, “Education, Special”, “Information Science and Library Science”, “Management”, “Political Science”, “Psychology, Applied”, “Psychology, Social”, “Sociology”, and “Psychology, Multidisciplinary”. “Fortune” and “Forbes” are excluded in the study as they are considered more as popular magazines rather than scholarly journals. “Science” and “Nature” are included in the list as they are considered as two prominent flagship journals by the general scientific community.

  2. Three query words (i.e., Internet, web, and cyberspace/cyber-space) were used to search titles/abstracts/keywords of Internet-relevant articles from 2000 to 2010. Article language was limited to English, and document type was limited to scholarly journal articles.

  3. Specifically, out of 1,413 journals listed in 13 subject categories, 871 (61.6 %) journals published Internet studies and were cited by Internet studies, 293 (20.7 %) were only cited in Internet studies without any Internet studies published, 46 (3.3 %) only published Internet studies with any citations received, and 203 (14.4 %) didn’t publish Internet studies and were not cited by Internet studies.

  4. Clauset et al. (2009) combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov–Smirnov statistic and likelihood ratios to discern and quantify power-law behavior in empirical data. First, power-law models are fitted to the observed long-tail distributions of in-degree/out-degree. Two coefficients are estimated for both distributions: scaling parameters α and lower bound on the scaling region x min (in-degree distribution: α = 1.77, x min = 46; out-degree distribution: α = 1.93, x min = 97). Then, goodness-of-fit tests are performed to tell whether the power-law model is a good match to the data or not. It turns out that the power-law model is a poor fit for both distributions (p < 0.001).

  5. Spearman rank-order correlation between in-degrees and out-degrees is 0.56 (p < 0.001).

  6. The average clustering coefficient for the random network is 0.027.

  7. HLM outperforms traditional approaches (e.g., repeated ANOVA, MANOVA) in analyzing longitudinal data in several aspects, such as decomposition of fixed and random effects, treatment of time predictor, and inclusion of time-variant predictors (for a detailed discussion of HLM for longitudinal data, please refer to Raudenbush 2001; Hedeker 2004; Peugh and Enders 2005).

  8. In educational research, ICC with cross-sectional design generally ranges between 0.05 and 0.20 (Snijders and Bosker 1999). The high ICC from the study is due to the longitudinal nature of the data, as ICC is also a measure of the average autocorrelation of the outcome variable over time (Singer and Willett 2003; Kwok et al. 2008).

  9. Technically, four conditional linear growth models are estimated with four blocks of variables (i.e., control variables, network brokerage variables, network closure variables, and interaction terms) added to the model in a stepwise way.

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Acknowledgments

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

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  1. Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore

    Tai Quan Peng

  2. Department of Media and Communication, City University of Hong Kong, Kowloon, Hong Kong

    Zhen-Zhen Wang

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Correspondence to Tai Quan Peng.

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Peng, T.Q., Wang, ZZ. Network closure, brokerage, and structural influence of journals: a longitudinal study of journal citation network in Internet research (2000–2010). Scientometrics 97, 675–693 (2013). https://doi.org/10.1007/s11192-013-1012-x

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