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电子商务企业全要素生产率变化及影响因素分析
引用本文:杨卓凡,石勇.电子商务企业全要素生产率变化及影响因素分析[J].系统工程理论与实践,2017,37(2):431-439.
作者姓名:杨卓凡  石勇
作者单位:1. 中国科学院大学 经济与管理学院, 北京 100190;2. 中国科学院 虚拟经济与数据科学研究中心, 北京 100190;3. 中国科学院 大数据挖掘与知识管理重点实验室, 北京 100190
基金项目:国家自然科学基金重点项目(71331005);国家自然科学基金国际(地区)合作与交流项目(71110107026)
摘    要:本文运用DEA Malmquist生产率指数模型,从效率变化、规模变化和技术变化的动态视角,探讨了电子商务企业的全要素生产率变化及其影响因素.研究结果显示,电子商务企业生产率增长经常伴随着技术进步、效率提升和规模扩张,这表明电子商务企业全要素生产率的变化源于以上三种要素的组合,但技术变化在驱动全要素生产率变化中起着更为关键的作用.从电子商务商业模式的角度来说,B2C电子商务模式比B2B电子商务模式和在线旅游OTA业务有更高的技术创新能力和全要素生产率水平;在线旅游OTA业务虽然实现了技术效率的改进和规模的扩张,但技术能力下滑导致其全要素生产率水平下滑;B2B电子商务模式全要素生产率水平下降的原因则在于效率、技术和规模的同步下滑.这些发现启示企业管理者应该正确处理技术、规模和效率之间的关系,同时应针对不同的商业模式采取不同的全要素生产率提升策略.

关 键 词:电子商务  data  envelopment  analysis  (DEA)  规模  全要素生产率  效率  
收稿时间:2015-05-26

Analysis on e-commerce firm-level total factor productivity change and its impact factors
YANG Zhuofan,SHI Yong.Analysis on e-commerce firm-level total factor productivity change and its impact factors[J].Systems Engineering —Theory & Practice,2017,37(2):431-439.
Authors:YANG Zhuofan  SHI Yong
Institution:1. School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China;2. Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing 100190, China;3. Key Research Lab on Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China
Abstract:From a view of efficiency change, scale change and technical change, this paper explores e-commerce firm-level total factor productivity (TFP) change and its impact factors using DEA Malmquist models. It shows that total factor productivity growth is often attributed to technical progress, efficiency improvement, scale expansion or the combinations of these three factors, but technical change plays a more important role in driving total factor productivity (TFP) change. Specifically, B2C (business-to-customer) e-commerce firms have higher technical innovation ability and total factor productivity level than B2B (business-to-business) and OTA (online-travel-agent) e-commerce firms. Although OTA e-commerce firms achieve technical efficiency improvement and scale expansion, it is technical regression to lead to total factor productivity regression. B2B e-commerce firms' total factor productivity regression are more due to the combination of efficiency change, technical change and scale change. These findings enlighten managers should correctly handle the relationship of technology, scale and efficiency, and should improve total factor productivity (TFP) based on different e-business models.
Keywords:e-commerce  data envelopment analysis (DEA)  scale  total factor productivity  efficiency
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