中国产业研发绩效调查:一个两阶段前景数据包络分析方法

IF 6 2区 管理学 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Hui-hui Liu , Guo-liang Yang , Jian-wei Gao , Ya-ping Wang , Guo-hua Ni
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引用次数: 0

摘要

随着中国产业研发(R&D)创新投入的不断增长,评估产业研发资源是否得到有效利用至关重要。因此,本研究提出了一种两阶段前景数据包络分析方法,以描述企业决策者(DM)在评估中的风险态度。利用这种方法,我们从整体上对中国 28 个行业的研发活动进行了评估,并通过敏感性分析和比较分析,探讨了企业管理者的风险心理对评估结果的实际影响。此外,我们将中国 28 个行业的研发绩效划分为四个象限进行分析,并重点关注石油和天然气开采、黑色金属矿采选和烟草制造等重点行业的研发绩效。根据分析结果,我们就中国产业的研发活动提出了一系列政策建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigating the research and development performance of Chinese industry: A two-stage prospect data envelopment analysis approach
With growing investments inindustry research and development (R&D) innovation in China, evaluating whether R&D resources assigned to industries areeffectively used is essential. However, limited research has been conducted on the assessment of R&D effectiveness in Chinese industries that encompasses both the internal process of R&D production and the psychological risks encountered by decision-makers (DMs).Hence, this study puts forward a two-stage prospect data envelopment analysis approach that can characterise the risk attitude of DM in evaluation. By employing this approach, we assess the R&D activities of 28 industries in China from an overall perspective and explore the actual influence of DMs’ risk psychology on the evaluation results through sensitivity and comparative analyses. Furthermore, we categorise the R&D performance of 28 Chinese industries into four quadrants for analysis and focus on the R&D performance of key industries such as extraction of petroleum and natural gas, mining of ferrous metal ores and manufacture of tobacco. Based on the findings, we provide a range of policy recommendations regarding the R&D activities of Chinese industries.
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来源期刊
European Journal of Operational Research
European Journal of Operational Research 管理科学-运筹学与管理科学
CiteScore
11.90
自引率
9.40%
发文量
786
审稿时长
8.2 months
期刊介绍: The European Journal of Operational Research (EJOR) publishes high quality, original papers that contribute to the methodology of operational research (OR) and to the practice of decision making.
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