基于随机前沿分析的上市物流公司技术效率评价

工业工程 ›› 2012, Vol. 15 ›› Issue (2): 41-47.

基于随机前沿分析的上市物流公司技术效率评价

华南理工大学工商管理学院，广东广州 510640

出版日期:2012-04-30 发布日期:2012-05-17
作者简介:景保峰（1983-），男，山西省人，博士研究生，主要研究方向为技术创新、人力资源管理.
基金资助:
广东省软科学研究计划重大项目（2011A070103002）；广东省教育厅人文社会科学重点研究基地重大项目（x2gsN9100140）；广东省软科学研究项目（2010B080203001）

Technical Efficiency Evaluation of Listed Logistics Companies by Using Stochastic Frontier Analysis

School of Business Administration, South China University of Technology,Guangzhou 510640, China

Online:2012-04-30 Published:2012-05-17

摘要/Abstract

摘要： 运用随机前沿分析模型，站在整个物流行业的角度，对中国沪深两市18家上市物流公司2003~2009年间的技术效率进行动态测度。结果表明：资本和管理要素投入对产出的影响显著，具有规模经济性，尤其是资本投入；人力资源投入的影响相当弱，且在统计上不显著；18家物流公司的总体技术效率较低，且有逐年下滑的趋势，但下降幅度相当小；运输类公司的效率显然大于港口类；物流公司的技术效率不仅受区域经济因素影响，而且与自身的经营管理水平密切相关。最后，根据评价结果分析存在的问题并提出相应的政策建议。

关键词: 上市物流公司, 随机前沿分析, 技术效率

Abstract: By using the stochastic frontier analysis model, dynamic technical efficiency is analyzed for eighteen listed logistics companies located in Shenzhen and Shanghai for the period from 2003 to 2009. Results show that the capital and management factors inputs, especially the capital have significant influence on output, which shows the effect of economies of scale. However, the impact of investment in human resources is rather weak and not statistically significant. In general, the overall technical efficiency of the 18 companies is fairly low and has a decreasing trend with a small rate. It also shows that the efficiency of transportation companies is obviously greater than those whose business is in the ports. Another finding is that the logistics companies technical efficiency is affected not only by regional economic factors, but also by their own management level. Based on the results, suggestions are given for technical efficiency improvement.

Key words: listed logistics company, stochastic frontier analysis(SFA), technical efficiency

景保峰，周霞，胡爱媛. 基于随机前沿分析的上市物流公司技术效率评价[J]. 工业工程, 2012, 15(2): 41-47.

Jing Baofeng, Zhou Xia, Hu Aiyuan. Technical Efficiency Evaluation of Listed Logistics Companies by Using Stochastic Frontier Analysis[J]. Industrial Engineering Journal , 2012, 15(2): 41-47.

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