Classified evaluation of R&D activities in Chinese universities: An application of new meta-frontier directional distance function framework

Abstract

Standardized evaluation has been applied in the research and development (R&D) activities of Chinese universities for a long time, which ignores the technological heterogeneity between these activities. Standardized evaluation has thus been difficult to meet the evaluation needs of the Chinese government, and classified evaluation is imperative for the diversified development of R&D activities in Chinese universities. Therefore, this paper introduces directional distance function (DDF) model with endogenous technique into meta-frontier data envelopment analysis framework to construct a new theoretical tool of classified evaluation, and this new method is applied to evaluate the efficiency of R&D activities of 939 Chinese universities. The theoretical contribution is to unify the endogenous directions relative to different frontiers, and then a new meta-frontier DDF framework is constructed; while the practical contribution is to provide the following conclusions and implications: (1) classified evaluation and standardized evaluation have different applicability to different categories of Chinese universities; (2) the homogeneity of the improvement direction is serious in Chinese universities; (3) different categories of Chinese universities have different improvement needs in different inputs and outputs, and the distances between most universities and their two frontiers are not far; (4) the selections of both improvement direction and indicator are important factors affecting the evaluation results. Finally, some useful suggestions are provided to improve the efficiency of R&D activities in Chinese universities.