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

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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.

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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.

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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
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  2. Thorsten Teichert
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  3. Alan Pilkington
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Correspondence to Thorsten Teichert.

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Table 7 Overall sample
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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

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