A Nonradial Super Efficiency DEA Framework Using a MCDM to Measure the Research Efficiency of Disciplines at Chinese Universities

Evaluating the efficiency of scientific research plays an important role in accelerating technological innovation and optimizing the allocation of research resources. Most studies have focused on measuring research efficiency from a macro perspective, ignoring differences within disciplines. Furthermore, existing methods have failed to discriminate between evaluation results and the fact that research has variable returns to scale. To address this, in this paper we propose a multiple-criteria decision-making (MCDM) nonradial super efficiency data envelopment analysis (NRSDEA) model, which uses an output-oriented nonradial SDEA method to manage nonsolution problems and integer decision variable constraints. In addition, we used a Malmquist index to decompose the productivity changes of statistics discipline's research efficiency at different universities in China. Finally, we verified the rationality and effectiveness of the proposed method.