Data-driven support for policy and decision-making in university research management: A case study from Germany

Abstract

The management of universities requires data on teaching and research performance. While teaching quality can be measured via student performance and teacher evaluation programs, the connection between research output and its antecedents is much harder to check, test, and understand. To inform research governance and policy-making at universities, we clarify the relationship between grant money and research performance. We examine the interdependence structure between third-party expenses, publications, citations, and academic age. To describe the relationship between these factors, we analyze individual-level data from a sample of professorships and a Scopus database from 2001 to 2015. Using estimates from a panel vector autoregressive model with exogenous variables, impulse response functions, and forecast error variance decomposition, we show that decision-making based on university-level data is inappropriate and does not reflect the behavior of individual faculties. Our results quantify the difference between the quality and quantity of research output, a better understanding of which is important to design incentive schemes and promotion programs. The paper also proposes a visualization of the cooperation between faculties and research interdisciplinarity via the co-authorship structure among publications. We discuss the implications for policy and decision-making and make recommendations for university research management.