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基于复杂网络的第三方电子商务平台临界用户规模研究
引用本文:段文奇. 基于复杂网络的第三方电子商务平台临界用户规模研究[J]. 中国管理科学, 2014, 22(12): 93-101
作者姓名:段文奇
作者单位:浙江师范大学经济与管理学院, 浙江 金华 321004
基金项目:教育部人文社科资助项目(11YJA630014);浙江省自然科学基金项目(Y6110018);国家自然科学基金资助项目(71271193);浙江省自然基金重点项目(LZ14G010001)
摘    要:产业竞争实践表明:第三方电子商务平台具有网络化市场的典型特征,用户只有超过临界规模后平台才会在正反馈机制作用下实现自激励增长,反之会在负反馈机制作用下逐渐消亡。因此,揭示影响临界用户规模的因素并探索其估计方法就变得十分重要。基于复杂网络方法构造了一个描述新平台导入市场后用户规模随时间演化的动态模型,并基于该模型分析了平台临界用户规模的影响因素及其作用机制。研究结果表明:(1)平台用户网络结构和用户决策阈值是影响平台临界用户规模的主要因素;(2)网络结构异质性(度分布的方差)与平台临界用户规模有近似幂函数关系;(3)通过改变用户的决策阈值,新平台服务质量改进程度、用户从已有平台切换到新平台的学习成本和新旧平台的兼容性三个因素对平台临界用户规模有很强的间接影响。结合算例,还讨论了平台临界用户规模估计方法的运用。

关 键 词:临界用户规模  电子商务平台  复杂网络  
收稿时间:2012-05-02
修稿时间:2013-03-24

Critical Mass of Third-party E-business Platform Based on Complex Network Method
DUAN Wen-qi. Critical Mass of Third-party E-business Platform Based on Complex Network Method[J]. Chinese Journal of Management Science, 2014, 22(12): 93-101
Authors:DUAN Wen-qi
Affiliation:College of Economics and Management, Zhejiang Normal University, Jinhua 321004, China
Abstract:Industrial competition practices have shown that the third-party E-business platform has typical characteristics of networked market, in which the positive feedback mechanism leads to platform success provided that the number of platform users surpasses its critical mass. Otherwise, the platform would diminish and disappear in the market under the effect of the negative feedback mechanism. Therefore, it is very important to reveal the influence factors of the critical mass of platform users and explore its estimation method. This paper, apply the complex network method to develop a dynamical model to describe the number of platform users,which is varying with the time after the platform introduced into market. Based on the proposed platform diffusion model, the influence factors and mechanisms are analyzed. Results show that (1) the platform user network structure and the users' decision threshold value are the main factors to determine the critical mass of platform users (2) the heterogeneity of network structure (the standard deviation of degree distributions) is in roughly power function of the critical mass of platform users (3) the improvement degree of service quality in the new platform, the learning cost of users switching from the existing platform to the new platform, and the compatibility between the existing and the new platforms have strong influence on the critical mass of platform users through changing users' decision threshold value. Combining a sampling case, it has also been discussed how the companies apply the estimation method of platform critical mass in practice.
Keywords:critical mass of users  E-Business platform  complex networks  
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