Skip to main content

Social network analytics for advanced bibliometrics: referring to actor roles of management journals instead of journal rankings

Scientometrics volume 112, pages 1631–1657 (2017)Cite this article

Abstract

Impact factors are commonly used to assess journals relevance. This implies a simplified view on science as a single-stage linear process. Therefore, few top-tier journals are one-sidedly favored as outlets, such that submissions to top-tier journals explode whereas others are short of submissions. Consequently, the often claimed gap between research and practical application in application-oriented disciplines as business administration is not narrowing but becoming entrenched. A more complete view of the scientific system is needed to fully capture journals´ contributions in the development of a discipline. Simple citation measures, as e.g. citation counts, are commonly used to evaluate scientific work. There are many known dangers of miss- or over-interpretation of such simple data and this paper adds to this discussion by developing an alternative way of interpreting a discipline based on the positions and roles of journals in their wider network. Specifically, we employ ideas from the network analytic approach. Relative positions allow the direct comparison between different fields. Similarly, the approach provides a better understanding of the diffusion process of knowledge as it differentiates positions in the knowledge creation process. We demonstrate how different modes of social capital create different patterns of action that require a multidimensional evaluation of scientific research. We explore different types of social capital and intertwined relational structures of actors to compare journals with different bibliometric profiles. Ultimately, we develop a multi-dimensional evaluation of actor roles based upon multiple indicators and we test this approach by classifying management journals based on their bibliometric environment.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Notes

  1. This approach however stops at the level of single-stage relationships and thus at the comparison of direct citation frequencies. It does not encompass indirect and multi-level relationships between journals.

  2. A network is constituted by a limited number of nodes, in this context, journals and lines that connect the nodes (in this case citation relationships). A matrix represents the network and with one-directional relationships this matrix is asymmetrical. Reflexive relations, in this case own citations, lie on the main diagonal line. The network-analysis procedure refers mainly to three levels of actors: (1) Integration of the actor into the network, (2) properties of the whole network, (3) identification and description of the groups of actors.

  3. A clique in the sense of graph theory contains at least three connected actors. The concept of the n-Clique, in which every actor can reach every other actor in n steps, is less rigid.

  4. Neither adjustments on the age of sources nor on the annual amount of articles per journal have been undertaken. Following arguments underlie this: Concerning age, we argue that any literature quoted stills contains a high richness respectively has not been replaced by newer sources, thus it is equally relevant. Concerning amount of articles, we argue that citation networks measure the average effect. An adjustment corresponds to (1) not such a understanding in which low-circulation journals have a high (relative) effect attributed. (2) Furthermore in succession to the argument of limited information capacities an adjustment can occur in the other direction so that the citations of an article with a high-circulation is more worth more than the citation of an article in a journal with low-circulation.

  5. Annotation to Fig. 2: The figure displays the diffusion of knowledge in the network. The thickness of the arrows portrays the extent of reciprocal referencing; the direction portrays the direction of the transfer. So, for example, group 4 gets quoted often by group 1 meaning that knowledge flows from group 4 into group 1. Furthermore, within groups the corresponding percentage of the size of the groups is indicated; for example group 4 accounts for 13.2% of all journals of the network. In the legend there is an indication concerning the age of the quoted and quoting literature and the relation of the sources of literature that have not been included into the network of management journals. The delimitation of the differences in the groups occurred with help pf the Scheffe´-test with a significance level of 5%.Group 4 for example quoted on average 44% different sources that are not included in the network. The age of the literature quoted on average is from the year 1986,5: the age of the quoting sources from group 4 is on average 1988.

  6. This distinction between “obsolescence” and “hardness” is analogous to citing Half-life und cited half-life of a journal (Burton and Kebler 1960, critically Szava-Kovats 2002). Our measurement of obsolescence includes “other” literature not respected in the network.

References

  • Baumgartner, H., & Pieters, R. (2000). The influence of marketing journals: A citation analysis of the discipline and its sub-areas. Tilburg: Marketing.

    Google Scholar 

  • Bordons, M., Fernández, M., & Gómez, I. (2002). Advantages and limitations in the use of impact factor measures for the assessment of research performance. Scientometrics, 53(2), 195–206.

    Article  Google Scholar 

  • Borgatti, S., Everett, M., & Freeman, L. (1996). UCINET IV version 1.64 reference manual.

  • Bourdieu, P. (1983). Oekonomisches Kapital, kulturelles Kapital. In R. Kreckel (Ed.), Soziale Ungleichheiten (pp. 183–198). Göttingen: Otto Schwartz & Co.

    Google Scholar 

  • Burt, R. S. (1976). Positions in networks. Social Forces, 55, 93–122.

    Article  MathSciNet  Google Scholar 

  • Burt, R. S. (1982). Toward a structural theory of action network models of social structure, perception, and action. New York: Columbia University.

    Google Scholar 

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

    Google Scholar 

  • Burton, R. E., & Kebler, R. W. (1960). The half-life of some scientific and technical literatures. American Documentation, 11(1), 18–22.

    Article  Google Scholar 

  • Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94(Supplement), 95–120.

    Article  Google Scholar 

  • Coleman, J. S. (1990). Foundation of social theory. Bd. 1–2. London: The Belknap Press of Harvard University Press.

    Google Scholar 

  • Freeman, L. C. (1979). Centrality in social networks: conceptual clarification. Social Networks, 1, 215–239.

    Article  Google Scholar 

  • Garfield, E. (1973). What scientific journals can tell us about scientific journals. IEEE Transactions on Professional Communication, 16(4), 200–203.

    Article  MathSciNet  Google Scholar 

  • Garfield, E. (1979). Citation indexing -its theory and application in science, technology, and humanities. New York: Wiley.

    Google Scholar 

  • Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78, 481–510.

    Article  Google Scholar 

  • Groh, G., & Fuchs, C. (2011). Multi-modal social networks for modeling scientific fields. Scientometrics, 89(2), 569–590.

    Article  Google Scholar 

  • Hubbell, C. H. (1965). An input–output approach to clique identification. Sociometry, 28, 377–399.

    Article  Google Scholar 

  • Johnson, J., & Podsakoff, P. (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.

    Article  Google Scholar 

  • Khan, G. F., Lee, S., Park, J. Y., & Park, H. W. (2016). Theories in communication science: a structural analysis using webometrics and social network approach. Scientometrics, 108(2), 531–557.

    Article  Google Scholar 

  • Koseoglu, M. A. (2016). Mapping the institutional collaboration network of strategic management research: 1980–2014. Scientometrics, 109(1), 203–226.

    Article  MathSciNet  Google Scholar 

  • Leydesdorff, L., Wouters, P., & Bornmann, L. (2016). Professional and citizen bibliometrics: complementarities and ambivalences in the development and use of indicators—a state-of-the-art report. Scientometrics, 109(3), 2129–2150.

    Article  Google Scholar 

  • Luhmann, N. (1997). Die Gesellschaft der Gesellschaft. Frankfurt am Main: Suhrkamp.

    Google Scholar 

  • March, J. G., & Simon, H. A. (1976). Organisation und Individuum. Wiesbaden: Gabler.

    Book  Google Scholar 

  • Marx, W., Schier, H., & Wanitschek, M. (1998). Kann man Forschungsqualität messen? Zitierungszahlen als Maß für Resonanz auf wissenschaftliche Aktivität. MPG-Spiegel, 3, 24–30 [Online Document]. http://www.fkf.mpg.de/ivs/citations.html. 28.09.2005.

  • Mayntz, R. (1993). Modernization and the logic of interorganizational networks. In J. Child (Ed.), Societal change between market and organization (pp. 3–18). Aldershot: Avebury.

    Google Scholar 

  • Pappi, F. U. (Ed.) (1987). Techniken der empirischen Sozialforschung, Bd. 1. Methoden der Netzwerkanalysen. Oldenburg: De Gruyter.

  • Pendlebury, D. A., & Adams, J. (2012). Comments on a critique of the Thomson Reuters journal impact factor. Scientometrics, 92(2), 395–401.

    Article  Google Scholar 

  • Pisoschi, A. M., & Pisoschi, C. G. (2016). Is open access the solution to increase the impact of scientific journals? Scientometrics, 109(2), 1075–1095.

    Article  Google Scholar 

  • Portes, A., & Sensenbrenner, J. (1993). Embeddedness and immigration: notes on the social derterminants of economic action. American Journal of Sociology, 98(6), 1320–1350.

    Article  Google Scholar 

  • Powell, W. W. (1990). Neither market not hierarchy. Network forms of organization. Research in Organizational Behavior, 12, 295–336.

    Google Scholar 

  • Salancik, G. R. (1986). An index of subgroup influence in dependency networks. Administrative Science Quarterly, 31(2), 194–211.

    Article  Google Scholar 

  • Schenk, M. (1984). Soziale Netzwerke und Kommunikation. Tübingen: Mohr.

    Google Scholar 

  • Scott, J. (1988). Trend-report social network analysis. Sociology, 22, 109–127.

    Article  Google Scholar 

  • Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69(1), 99–118.

    Article  Google Scholar 

  • Small, H. (1973). Cocitation in scientific literature—new measure of relationship between 2 documents. Journal of the American Society for Information Science, 24(4), 265–269.

    Article  Google Scholar 

  • Sydow, J. (1992). Strategische Netzwerke und Transaktionskosten. Über die Grenzen einer transaktionskostentheoretischen Erklärung der Evolution strategischer Netzwerke. In Stahle, W. H., & Conrad P. (Ed.), Managementforschung (pp. 239–311). Berlin, New York: De Gruyter.

  • Szava-Kovats, E. (2002). Unfounded attribution of the “half-life” index-number of literature obsolescence to Burton and Kebler: A literature science study. Journal of the American Society for Information Science and Technology, 53(13), 1098–1105.

    Article  Google Scholar 

  • Trezzini, B. (1998). Konzepte und Methoden sozialwissenschaftlicher Netzwerkanalyse: Ein aktueller Überblick. Zeitschrift für Soziologie, 27(5), 378–394.

    Article  Google Scholar 

  • Weber, M. (1972). Wirtschaft und Gesellschaft. Tübingen: Mohr.

    Google Scholar 

  • Weber, M. (1985). Wirtschaft und Gesellschaft. Grundriß der verstehenden Soziologie. Tübingen: Mohr.

    Google Scholar 

  • White, D. R. (1984). REGE: A regular graph equivalence algorithm for computing role distances prior to block modeling. Unpublished manuscript. University of California: Irvine.

  • White, D. R., & Reitz, K. P. (1983). Graph and semi-group homomorphisms on networks of relations. Social Networks, 5, 193–235.

    Article  MathSciNet  Google Scholar 

  • Willke, H. (1995). Systemtheorie III. Steuerungstheorie: Grundzüge einer Theorie der Steuerung komplexer Sozialsysteme. Stuttgart, Jena: Lucius & Lucius.

Download references

Author information

Authors and Affiliations

  1. Soziologisches Institut, Universität Zürich, Zurich, Switzerland

    Katja Rost

  2. AB Marketing und Innovation, Universität Hamburg, Hamburg, Germany

    Thorsten Teichert

  3. Business Information Management and Operations, Westminster Business School, London, UK

    Alan Pilkington

Authors
  1. Katja Rost
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thorsten Teichert
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alan Pilkington
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thorsten Teichert.

Appendix

Appendix

Table 7 Overall sample
Full size table

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rost, K., Teichert, T. & Pilkington, A. Social network analytics for advanced bibliometrics: referring to actor roles of management journals instead of journal rankings. Scientometrics 112, 1631–1657 (2017). https://doi.org/10.1007/s11192-017-2441-8

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-017-2441-8

Keywords

  • Social network analysis
  • Journal ranking
  • Management
  • Journal actor roles