Analyzing the Knowledge Transfer Performance of China’s Universities: a Heterogeneous Stochastic Frontier Approach

Evaluating the knowledge transfer performance of universities facilitates the tracking of and improvement in the factor utilization efficiency of R&D activities. In this study, a heterogeneous stochastic frontier analysis model is used to measure the total factor productivity (TFP) growth rate, output elasticity, and factor bias index of knowledge transfer in Chinese universities from 2007 to 2016. The results show that the overall TFP of knowledge transfer at the university level in China has been improving. The increase in technical efficiency is the primary reason for this phenomenon. The output elasticity of R&D capital input is much larger than that of R&D labor input, indicating that currently, knowledge transfer is still driven by capital. This finding is particularly clear in 211-Project universities. R&D labor in the knowledge transfer process receives more preference in universities (especially non-211-Project universities) in the later period (i.e., 2012–2016). Therefore, the government should encourage the enthusiasm of researchers and improve the substantial contribution of R&D labor, thus facilitating an improvement in the subsequent knowledge transfer performance.