高校人文社科项目制绩效评价及其改进目标——基于联立方程与BP人工神经网络

Research on the Performance Evaluation Mechanism and Improvement Objectives of the Humanities and Social Sciences Project System in Universities:Based on Simultaneous Equations and BP Artificial Neural Network

在深入分析高校人文社科研究投入产出诸要素关系的基础上,针对“双一流”建设高校的人文社科研究数据,采用联立方程模型分析项目制的绩效;并基于Super-SBM模型测度效率,从而明确改进目标;同时以确定最佳投入产出下研究项目的理想绩效为目标,探寻最优解,并借助BP人工神经网络进行稳健性分析。研究结果表明,人文社科项目的绩效有待提高;最佳投入下人文社科项目绩效提升较快,项目绩效水平有待提高;最佳投入下人文社科投入产出关系的匹配程度更高;人文社科投入产出效率总体进步空间巨大,主要原因是不同高校研究水平相差较大,导致纯技术效率水平较低;研究经费绩效较低需要引起足够的重视。

On the basis of in-depth analysis of the input-output relationships of humanities and social sciences research in universities,this study adopts a simultaneous equation model to analyze the performance of project-based research on humanities and social sciences research data of"Double First Class"universities;And measure efficiency based on the SBM-Super model to clarify improvement goals;At the same time,the goal is to determine the ideal performance of the research project under the optimal input output,explore the optimal solution,and use BP artificial neural network for robustness analysis.The results show that:The performance of humanities and social science projects needs to be improved;Under the optimal investment,the performance of humanities and social science projects has improved rapidly,and the level of project performance needs to be improved;The matching degree of input-output relationship in humanities and social sciences is higher under the optimal input;The overall improvement space for input-output eficiency in humanities and social sciences is enormous,mainly due to significant diferences in research levels among different universities,resulting in lower pure technical efficiency levels;The low performance of research funding needs to be given sufficient attention.