PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Research Efficiency Analysis of Universities in 31 Chinese Provinces Using Three-Stage DEA Model and Dynamic Malmquist Index-Based on Provincial Panel Data from 2012 to 2022
Version 1
: Received: 7 June 2024 / Approved: 10 June 2024 / Online: 10 June 2024 (14:34:20 CEST)
How to cite:
Liang, Q.; Yin, F. Research Efficiency Analysis of Universities in 31 Chinese Provinces Using Three-Stage DEA Model and Dynamic Malmquist Index-Based on Provincial Panel Data from 2012 to 2022. Preprints2024, 2024060596. https://doi.org/10.20944/preprints202406.0596.v1
Liang, Q.; Yin, F. Research Efficiency Analysis of Universities in 31 Chinese Provinces Using Three-Stage DEA Model and Dynamic Malmquist Index-Based on Provincial Panel Data from 2012 to 2022. Preprints 2024, 2024060596. https://doi.org/10.20944/preprints202406.0596.v1
Liang, Q.; Yin, F. Research Efficiency Analysis of Universities in 31 Chinese Provinces Using Three-Stage DEA Model and Dynamic Malmquist Index-Based on Provincial Panel Data from 2012 to 2022. Preprints2024, 2024060596. https://doi.org/10.20944/preprints202406.0596.v1
APA Style
Liang, Q., & Yin, F. (2024). Research Efficiency Analysis of Universities in 31 Chinese Provinces Using Three-Stage DEA Model and Dynamic Malmquist Index-Based on Provincial Panel Data from 2012 to 2022. Preprints. https://doi.org/10.20944/preprints202406.0596.v1
Chicago/Turabian Style
Liang, Q. and Fang Yin. 2024 "Research Efficiency Analysis of Universities in 31 Chinese Provinces Using Three-Stage DEA Model and Dynamic Malmquist Index-Based on Provincial Panel Data from 2012 to 2022" Preprints. https://doi.org/10.20944/preprints202406.0596.v1
Abstract
Efficiency serves as a crucial metric in assessing the advancement of scientific research for universities. Reasonable inputs of human resources, finances, materials, and other resources, along with high-quality research output, are important guarantees for the research efficiency of Chinese universities. This study delves into the patterns of changes of research efficiency among universities across the 31 provinces and municipalities of China from 2012 to 2022. By scrutinizing input-output perspectives and utilizing data sourced from the China Statistical Yearbook, the analysis unveils prevailing scenarios of input redundancy and output insufficiency in research efficiency across universities. The static and dynamic trends in research efficiency were further investigated by employing a static non-parametric Data Envelopment Analysis (DEA)-BCC model and a dynamic Malmquist index. Key findings from the analysis from 2012 to 2022 include: (1) notable growth in investments in teaching, research equipment assets, and research and development funds among nationwide universities; (2) a steady increase in the mean value of technical efficiency (TE) from 0.97 to 0.99, indicating continuous enhancement in overall efficiency in research input resource allocation across the provinces; (3) the mean Malmquist productivity index of 1.285, with an average annual increase of 28.5%, signifying a consistent improvement in university research efficiency throughout the study period; and (4) the provinces experiencing varying degrees of input redundancy or output insufficiency were selected to suggest the need for tailored strategies in resource allocation. The study underscores the importance of a holistic evaluation system involving stakeholders from government, universities, and society to optimize research efficiency and promote scientific innovation and social development.
Keywords
research efficiency; face validity; input redundancy; interprovincial data ; technical efficiency
Subject
Social Sciences, Education
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
