Ten reasons why research collaborations succeed—a random forest approach

Malte Hückstädt ()
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Malte Hückstädt: German Centre for Higher Education Research and Science Studies

Scientometrics, 2023, vol. 128, issue 3, No 21, 1923-1950

Abstract: Abstract The state of research in the Science of Team Science is characterised by a wide range of findings on how successful research collaboration should be structured. However, it remains unclear how the multitude of findings can be put into a hierarchical order with regard to their significance for the success of cooperation. This is where the article comes in: based on the state of research, the question of which intra- and interpersonal factors are most significant for the success of a research team is investigated. In order to explore the ten most important reasons for the success of a research collaboration, a Random Forest classifier is specified that predicts the success of research collaborations on the basis of 51 input variables. The analyses presented in the paper are based on representative survey data on n = 1.417 principal investigators and spokespersons of ongoing and completed research clusters funded by the German Research Foundation. The success of a research cluster is operationalised as the extent to which it has achieved the goals that it communicated to the funding agency before it began. Highly realistic and clear research objectives are central to the success of research clusters, as are comprehensive agreement on objectives, close interconnection of the subprojects’ research work and a fair and trusting cooperation climate.

Keywords: Research collaboration success; Team science; Collaboration effectiveness; Random forest; Machine Learning (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11192-022-04629-7

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