stitutability and complementarity between different production factors, single factor
productivity can never accurately and effectively reflect the status and change of
university productivity characterized by multiple inputs and outputs. By searching
for related literature in economics, volumes of studies have applied Malmquist
Productivity Index (MPI) approach to obtain Total Factor Productivity
(TFP) indexes based on DEA. With MPI, researchers are not only able to evaluate
and compare the inter-period change of productivity, but also able to decompose the
index to explore the underlying causes of the change. Hence, this method has been
broadly employed in economic research. Therefore, this chapter will construct
DEA-based Malmquist Productivity index model to give a dynamic evaluation on
research productivity of 211Us.
Compared to the broad usage of MPI in economic productivity research, this
method used in the research areas of higher education evaluation is much fewer. Hu
and Liang’s( 2007 ) article is an early endeavor. They applied Malmquist index to
analyze the research productivity changes before and after the mergers of many
Chinese universities. In 2010, Zhou and Li ( 2010 ) used this method to carry out
dynamic analyses of teaching productivity in the context of China’s higher edu-
cation expansion. Another work done by Zhou ( 2010 ) with MPI was to evaluate the
research performance in both natural sciences and social sciences of“985 Project”
universities (henceforth 985Us). A mostly recent study came from Yang ( 2012 ),
which applied MPI to evaluate the effectiveness of combination of manufacturing,
teaching and research in colleges of agriculture and forestry. Overall, these studies
have tried to carry out scientific evaluation on the productivity of the whole uni-
versity or part of its production activities. However, there is much more work
needed to be done. First, the themes of existing studies are not focused enough,
especially lack in-depth studies dealing with research productivity. Second,“ 211
Project”has been implemented over three phases, from 1995 to 2015, yet there are
few studies taking 211Us as the evaluation objects. Third, as Malmquist index is a
non-parametric approach based on DEA estimation, it’s necessary to guarantee the
homogeneity of DMUs for achieving reliable outcomes. As research activities in
disciplines like natural sciences, engineering, agriculture and medicine (henceforth
NEAM) are completely different from those in humanity and social sciences
(henceforth HSS), so efforts must be made in evaluating according to their
features.
Above all, this chapter takes an effort to employ DEA-Malmquist method to
conduct dynamic evaluation on the research productivity of 211Us. In the context
of building up world-class universities and world-class disciplines, reviewing and
summarizing the effects of“211 Project”can provide helpful references to gov-
ernments and research administration. The research questions needed to be
addressed in this chapters are: in the thirdfive-year cycle,
3
what about the growth
(^3) Since“211 Project”began in 1995, it have lasted for 15 years in 2010. And the 15 years was
divided into threefive-year cycles depending on the planning, 2006–2010 is the thirdfive-year
cycle.
220 5 Dynamic Evaluation on Research Productivity...