ArticlePloS one2024
Evaluating the efficiency, productivity change, and technology gaps of China's provincial higher education systems: A comprehensive analytical framework.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Article
- The impact of work concerns on teaching effectiveness: a structural equation modeling and multiple regression analysis in Chinese private universities.Frontiers in psychology · 2025Article
- Malmquist productivity index for two-stage network systems under data uncertainty: A real-world case study.PloS one · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.