系统管理学报

系统管理学报 ›› 2020, Vol. 29 ›› Issue (3): 425-433.DOI: 10.3969/j.issn.1005-2542.2020.03.002

• 运营管理 • 上一篇    下一篇

长尾市场中平台的最优规模和竞争策略

张翼飞，陈宏民
  

  1. 上海交通大学 安泰经济与管理学院，上海  200030
  • 出版日期:2020-05-29 发布日期:2020-07-09
  • 作者简介:张翼飞（1983—），男，博士生。研究方向为互联网经济与平台规制。
  • 基金资助:
    国家自然科学基金资助项目(71472121)；国家社会科学基金重大项目(14ZDB137)

Optimal Scale and Competitive Strategies of Platform in Long-Tailed Market

ZHANG Yifei, CHEN Hongmin   

  1. Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, China
  • Online:2020-05-29 Published:2020-07-09

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摘要：

在平台上交易量为长尾分布的背景下，从平台双方交易量的相互影响出发，建立了一般化数学模型对平台的最优规模和双平台的竞争性均衡进行了分析，解释了现实中平台型企业的有限规模和多平台共存的市场特征，并探讨了推荐系统在电子商务产业中的作用。用户异质性是垄断平台存在有限的最优规模的必要条件。当平台上的卖方销量服从长尾分布时，两个相互竞争的平台之间会存在唯一的市场均衡。推荐系统可以通过重新分配交易量来提高平台的利润和规模。

关键词: 双边市场, 平台, 互联网经济, 长尾分布

Abstract:

In the context of the long-tailed distribution of the trading volume of a platform, and based on the  interaction of trading volumes between end-users, this paper establishes an analytical model to examine the optimal scale of the platform and its strategies in a competitive market, explaining limited scales and co-existence between similar platforms, showing how recommendation systems can benefit the platforms in an online B2C market. User heterogeneity is the necessary condition for a monopoly platform to have a limited optimal scale. When the trading volumes of sellers conform to long-tailed distribution, there will be a unique market equilibrium between two competitive platforms. The recommendation system can increase both the profits and the scale of a platform by redistributing the total trading volume.

Key words: two-sided market, platform, internet economy, long-tailed distribution

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