Abstract
Research collaborations are crucial for scientific progress, but their success is often compromised by internal collaboration problems. While previous work is often small-scaled and largely based on case studies and qualitative work, we present a large-scale, quantitative, and representative study to investigate important drivers behind research collaboration problems in various disciplines. Based on an input-process-output framework and with a focus on research clusters, we investigated the occurrence of four crucial research collaboration problems: fairness, commitment, difference, and cohesion problems. Based on a sample of 5.306 researchers, we identified several input and process variables that could reduce collaboration problems in research collaborations including gender heterogeneity, conflict mediation by a cluster’s spokesperson, the synthesis of results, and the collaborative development of common goals. We discuss that these problems are often rooted in the science system itself and provide important guidelines and implications for stakeholders, funding bodies, and involved researchers on how to reduce collaboration problems in research collaborations.
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Notes
This selection was made for two reasons: Our study focuses on the origin of research collaboration problems that arise at the cluster level of RC (Fig. 2). The additional consideration of collaboration problems occurring within sub-projects would have (1) led to an over-complexity of our survey. Furthermore, (2) the generation of a representative sample of all scientific staff, doctoral students, and postdocs of the DFG research clusters would not have been possible without disproportionately high effort.
DFG RC differ from normal (author-)teams in several respects: They are highly institutionalised and have formalised memberships, goals and purposes. RC of the DFG are furthermore organised in a project form, are financed by third-party funding, often have long and fixed terms, fixed organisational structures and are designed to promote and enable extensive research in specific areas (for a more detailed overview see: Defila et al., 2008; Torka, 2012).
For an overview and more detailed description of the variables used in our four analytical models, see Table A3 in the Appendix.
Translated by the authors.
The n = 5.306 PIs and Spokesperson are clustered into n = 948 research collaborations and are thus not statistically independent of each other.
In the course of the model specification, we have assumed that the coherent use of terms across sub-projects is correlated with the use of common theories (Defila et al., 2006).
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Acknowledgements
The authors would like to thank Bernd Kleimann and Judith Block for their valuable comments on earlier versions of this manuscript.
Funding
This research was supported by the German Federal Ministry of Education and Research [Grant Number M527800].
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Appendix
Appendix
See Figs. A1, A2, A3 and Tables A1, A2, A3, A4.
Relative frequencies of the disciplinary affiliation of the PIs and spokespersons in the population and the sample
Relative frequency of researcher’s gender and roles, RC status and their funding line in the population and in the sample
Frequencies of the proportions of female PIs in research clusters according to subject areas of the German research foundation
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Hückstädt, M., Leisten, L.M. Internal factors promoting research collaboration problems: an input-process-output analysis. Scientometrics 129, 2007–2035 (2024). https://doi.org/10.1007/s11192-024-04957-w
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DOI: https://doi.org/10.1007/s11192-024-04957-w