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Knowledge production and the structure of collaboration networks in two scientific fields

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Abstract

In this paper the relationship between knowledge production and the structure of research networks in two scientific fields is assessed. We investigate whether knowledge production corresponds positively or negatively with different types of social network structure. We show that academic fields generate knowledge in different ways and that within the fields, different types of networks act as a stimulant for knowledge generation.

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Notes

  1. We are aware and indeed have highlighted the fact in previous publications, that knowledge generation is not the only task of researchers. Rather, research output is multidimensional and research groups do specialize in different outputs dimensions such as education of new scientists or the maintaining of research infrastructure (Jansen et al. 2007, Schmoch et al. 2009). However, in this paper we concentrate only on the production of new knowledge as one of the core tasks of science because the relationship between knowledge production and network structure has in the past been made repeatedly.

  2. The key-word based search strategy was developed by the Fraunhofer Institute for Systems and Innovation Research ISI. The authors would especially like to thank Ulrich Schmoch and Torben Schubert for collating and providing the relevant data.

  3. A more detailed description of this qualitative way of gathering ego-centred network data is given in Franke and Wald (2006).

  4. Compare http://www.nano.gov/html/facts/whatIsNano.html (as at July 10, 2008).

  5. A key-word based search strategy to identify articles from a field is more precise than a strategy based on the SCI subject categories because journals are assigned to one or more subject categories without discerning for discrete articles. The subject categorisation in the SSCI (which is comparable to the SCI) is discussed in further detail in Glänzel et al. (1999).

  6. The clustering coefficient was introduced by Watts and Strogatz (1998) and can be written as follows:

    $$ Clustering\,Coefficient\,\left( C \right) = {\frac{3\; \times \,Number\,of\,Triangles\,on\,the\,Graph}{Number\,of\,connected\,Triples\,of\,Vertices}} $$

    where a “triangle” is a group of three authors, each of whom is connected to both of the others, and a ‘‘connected triple’’ is a single author connected to two others.

  7. Newman (2001) establishes this for the field of “physics of the condensed matter”, one of the sub-disciplines of nanoscience (compare Fig. 4).

  8. Usually the constraint measure is thought to vary between zero and one, but depending on the size of the network the lower border of the constraint measure will be higher than zero. This is the reason, why the constraint value is higher than zero in our exemplary network B, although there are no crosslinks between the 12 alteri.

  9. We also collected bibliometric data on the number of SCI publications of the research groups between 2004 and 2006 but these data were only available for 49 research groups polled in 2006/07. There is a significant positive correlation between the number of SCI publications and the number of international conference papers of a research group collected by a standardized questionnaire on input and output structure of the research groups (r =0.373**). To allow for a bigger sample size we decided to use the output of international conference papers as the measurement of scientific performance.

  10. The output indicator “number of international conference papers” is a count-variable; therefore only count-data regression models should be applied. First a Poisson-Model was fitted and a test for overdispersion was applied. This test rejected the Poisson-Model on a 0-percent level. A Negative Binomial Model was then computed because the overdispersion-parameters are too high with 0.752 for astrophysics and 0.752 for nanoscience. The dispersion-parameter describes the heteroscedasticity of the model. If the variance is not growing proportional to the expected value of the function, a Negative Binomial Model should be applied, otherwise the significance of the parameters could be overestimated (Hilbe 2007).

    We also performed a standard OLS-Regression where we logarithmised the dependent variable “number of international conference papers”. The direction of the effect of network constraint in the two fields is the same as in the Neg-Bin Model; it is significant at the 10%-level for astrophysics and at the 1%-level for nanoscience.

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Acknowledgements

We gratefully acknowledge funding by the German Research Foundation (Ja 548/5-1, Ja 548/5-2, Ja 548/5-3).

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  1. German Research Institute for Public Administration Speyer, Freiherr-vom-Stein-Str. 2, 67346, Speyer, Germany

    Dorothea Jansen, Regina von Görtz & Richard Heidler

  2. German University of Administrative Sciences Speyer, Freiherr-vom-Stein-Str. 2, 67346, Speyer, Germany

    Dorothea Jansen

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  1. Dorothea Jansen
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Correspondence to Dorothea Jansen.

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Jansen, D., von Görtz, R. & Heidler, R. Knowledge production and the structure of collaboration networks in two scientific fields. Scientometrics 83, 219–241 (2010). https://doi.org/10.1007/s11192-009-0022-1

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