Analysis and countermeasures of scientific research performance of higher vocational colleges in Sichuan province using the DEA-BCC model

Abstract

This study aimed to: 1) analyze and evaluate the scientific research performance of higher vocational colleges in Sichuan province, China, 2) analyze the impact and importance of research input indicators on the research performance of higher education colleges and 3) establish a comprehensive evaluation model for analyzing the scientific research performance of higher vocational colleges in Sichuan province. The study samples included research performance input and output data from 30 higher vocational colleges in Sichuan province from 2019 to 2021. Correlation analysis between research input and output was conducted using the DEA-BCC model, the Malmquist index model, and the Rough Set theory. The computation of the DEA-BCC model and the Malmquist index model was performed using DEAP2.1 software, while the Rough Set theory was analyzed using the Python programming language. The results showed that: 1) five colleges demonstrated good scientific research performance as analyzed by DEA-BCC and Malmquist index models. Despite an overall upward trend in scientific research performance among higher vocational colleges in Sichuan, the levels remain relatively modest, 2) internal expenditure for scientific research funds and senior and above professional teachers were found to have a significant impact on scientific research efficiency according to the Rough Set analysis and 3) the integrated use of the DEA-BCC model, Malmquist index model, and Rough Set theory can scientifically and effectively evaluate the research performance of higher vocational colleges. It can be concluded that improving research management, resource utilization, and technological innovation can enhance efficiency and bridge gaps among higher vocational colleges in Sichuan province.