Evaluating the efficiency, productivity change, and technology gaps of China’s provincial higher education systems: A comprehensive analytical framework

Abstract

China’s higher education system is one of the largest and most complex in the world, with a vast number of higher education institutions scattered across different provinces. Evaluating the efficiency, productivity change, and technology gaps of these institutions is significant for understanding their performance and identifying areas for improvement. In this context, this study employs three different approaches, DEA super-SBM, Malmquist Productivity Index, and Meta-Frontier Analysis, to evaluate the efficiency, productivity change, and technology gaps of China’s provincial higher education systems. The study results revealed that the average higher education efficiency in China is 1.0015 for the study period of 2010–2021. A rapid and continuous increase was witnessed in higher education efficiency in China from 2014 to 2020. Meta-frontier and Group-frontier, higher education efficiency scores of low-level literate provinces are greater than middle and high-level literate provinces. However, the TGR of higher and middle-level literate provinces is greater than low-level literate provinces, indicating a superior technological level. The average MI score is 1.0034, indicating growth in productivity change. Efficiency change is the main determinant in higher education productivity growth instead of technological growth. The Middle and Low-level literate provinces witnessed growth in higher education productivity, while high-level literate provinces observed a decline in productivity change. The Kruskal-Wallis test provides evidence that a significant statistical difference exists among the three groups of education levels for the average scores of MI, EC, TC, and TGR.

Synergy of higher education resources and digital infrastructure construction in China: Regional differences, dynamic evolution and trend forecasting

“…Previous studies have found that there is a relatively obvious imbalance higher education development in China [ 12 – 14 ]. Meanwhile, considering the economic development in different regions, researchers have also found significant regional differences in digital development [ 15 , 16 ].…”

Malmquist productivity index for two-stage network systems under data uncertainty: A real-world case study

“…Efficiency and productivity assessments are crucial for organizations seeking continuous improvement and growth in today’s highly competitive and dynamic business environment [ 1 – 3 ]. Various methodologies and techniques have been developed to evaluate the performance of decision-making units (DMUs) in different areas and applications such as agriculture, banking, communication, education, energy, finance, fishery, forestry, healthcare, insurance, manufacturing, power, supply chain, transportation, and tourism [ 4 – 6 ].…”