摘要
遴选和学习适合的相对科研标杆,有利于研究机构弥补因现行定量科研评价引发的负面效应。借鉴标杆管理、质量评价、数据包络分析法,从效率与质量视角,构建了一套基于相对标杆的科研质量评价体系(评价目标、准则、思路与方法)。以中国内地31所典型985高校为对象,在科研效率评价的基础上,诊断和分析某高校的科研质量问题,遴选了可供学习的相对科研标杆,给出了科研质量提升建议。研究发现:学习相对标杆,有利于克服了传统标杆管理的局限性;基于相对标杆的科研质量评价方法,为高校有效识别和改进科研质量问题提供了有益启示。
The selection and learning of suitable relative scientific research benchmarking is beneficial for research institutions to compensate for the negative effects caused by the current quantitative scientific research evaluation.This research takes a page from benchmarking,quality evaluation,and data envelopment analysis method and constructs a relatively benchmark-based scientific research quality evaluation system from the perspective of efficiency and quality,which includes evaluation objectives,criteria,ideas,and methods.This research takes 31 typical 985 universities in China's Mainland as the target,and diagnoses and analyzes the scientific research quality problems of a university on the base of the evaluation of research efficiency.In addition,the paper selects relative scientific research benchmarks for learning,and gives suggestions for improving scientific research quality.This study found that:learning relative benchmarking is helpful to overcome the limitations of traditional benchmarking management;the research quality evaluation method based on relative benchmarking provides useful insights for universities to effectively identify and improve research quality issues.
作者
周文泳
柏方云
张婧
ZHOU Wenyong;BAI Fangyun;ZHANG Jing(School of Economics and Management,Tongji University,Shanghai 200092,China;SAIC General Motors Corp.Ltd.,Shanghai 201206,China)
出处
《同济大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2023年第5期674-681,共8页
Journal of Tongji University:Natural Science
基金
上海市“科技创新行动计划”软科学研究项目(22692100600)
国家社会科学基金(07CJY007)。
关键词
相对标杆
科研质量
质量评价
数据包络分析
relative benchmarking
scientific research quality
quality evaluation
data envelopment analysis(DEA)
作者简介
第一作者:周文泳(1969-),男,教授,博士生导师,管理学博士,主要研究方向为创新生态与科技评价、产业创新与科技政策、科学质量与技术经济、物流与供应链管理。E-mail:zhouwyk@163.com;通信作者:柏方云(1992-),女,管理学博士,主要研究方向为数据挖掘和算法优化。E-mail:baifyr@163.com。
