| 郭瑛.基于PCA-DEA-Tobit模型的物流企业绩效评价[J].安徽建筑大学学报,2023,31(): |
| 基于PCA-DEA-Tobit模型的物流企业绩效评价 |
| Performance Evaluation of Logistic Companies Based on PCA-DEA-Tobit Model |
| 投稿时间：2023-04-05 修订日期：2023-05-13 |
| DOI： |
| 中文关键词: 绩效评价 PCA-DEA-Tobit 物流企业 |
| 英文关键词: Performance evaluation, PCA-DEA-Tobit, Logistic enterprise |
| 基金项目:2021年度安徽高校人文社会科学研究重点项目“基于双链融合背景下的芜湖市物流园区绩效评价体系研究”(项目编号：SK2021A0925)；2020年度安徽省省级质量工程项目“《生产运营管理》线上课程”(项目编号：2020mooc555)；2020年度安徽省级教学示范课《运营管理》和《京东教学岗位实习》。 |
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| 中文摘要: |
| 在已有研究的基础上，笔者提出了基于PCA-DEA-Tobit的物流企业绩效评价的新方法并实证验证了其有效性和可行性。首先将物流企业绩效的指标区分为内部因子指标和外部因子指标，并将每个一层内部因子指标进一步区分为与物流企业各个运营环节契合的二层内部因子指标。为了满足DEA的使用条件，应用主成分分析法(PCA)消除原二层指标之间的相关性,并按照至少85%的累计贡献率提取出新的二层指标并进行正数化处理，进而应用交叉效率DEA对各一层内部因子指标进行评价，结合由熵权法得到的客观权重得到各决策单元的整体绩效值及相应排序，最后以Tobit回归模型分析外部因子指标与物流企业整体绩效的关联程度及其影响。 |
| 英文摘要: |
| Based on the existing research, the author proposes a new evaluation method of logistics enterprise performance based on PCA-DEA-Tobit and verifies its effectiveness and feasibility by empirical evidence. This paper first divides the indicators of logistics enterprise performance into internal factor indicators and external factor indicators, and further subdivides each first-level internal factor indicator into more specific second-level internal factor indicators. In order to meet the use conditions of DEA, the principal component analysis (PCA) method is firstly applied to eliminate the correlation between the original second-level indicators, and the new 2nd-level indicators are extracted according to the cumulative contribution rate of at least 85% and further processed into positive numbers. Then, the cross-efficiency DEA is used to evaluate the first-level internal factor indicators, where the results will be integrated with the objective weight calculated by the entropy weight method to yield the overall performance of decision-making units and their corresponding ranking. Finally, the Tobit regression model is used to analyze the degree of correlation as well as the impact between external factor indicators and the overall performance of logistics enterprises. |
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