Optimal pricing for a peer-to-peer sharing platform under network externalities

Author(s):  
Yunpeng Li ◽  
Costas Courcoubetis ◽  
Lingjie Duan ◽  
Richard Weber
Author(s):  
Yunpeng Li ◽  
Costas A. Courcoubetis ◽  
Lingjie Duan ◽  
Richard Weber

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Filippo Belloc

AbstractWe study hours worked by drivers in the peer-to-peer transportation sector with cross-side network effects. Medallion lease (regulated market), commission-based (Uber-like pay) and profit-sharing (“pure” taxi coop) compensation schemes are compared. Our static model shows that network externalities matter, depending on the number of active drivers. When the number of drivers is limited, in the presence of positive network effects, a regulated system always induces more hours worked, while the commission fee influences the comparative incentives towards working time of Uber-like pay versus profit-sharing. When the number of drivers is infinite (or close to it), the influence of network externalities on optimal working time vanishes. Our model helps identifying which is the pay scheme that best remunerates longer working times and offers insights to regulators seeking to improve the intensive margin of coverage by taxi services.


2004 ◽  
Vol 15 (2) ◽  
pp. 155-174 ◽  
Author(s):  
Atip Asvanund ◽  
Karen Clay ◽  
Ramayya Krishnan ◽  
Michael D. Smith

2018 ◽  
Vol 18 (3) ◽  
pp. 23-46
Author(s):  
Kelly Carvalho Vieira ◽  
Eduardo Gomes Carvalho ◽  
Joel Yutaka Sugano ◽  
José Willer do Prado

Author(s):  
Atip Asvanund ◽  
Karen B. Clay ◽  
Ramayya Krishnan ◽  
Michael D. Smith

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Wenjie Wang ◽  
Lei Xie

Ridesharing two-sided platforms link the stochastic demand side and the self-scheduling capacity supply side where there are network externalities. The main purpose of this paper is to establish the optimal pricing model of ridesharing platforms to dynamically coordinate uncertain supply and stochastic demand with network externalities in order to maximize platforms’ revenue and social welfare. We propose dynamic pricing strategies under two demand scenarios that minimize order loss in the surge demand period and maximize social welfare in the declining demand period. The numerical simulation results show that dynamic pricing strategies could stimulate the supply to reduce delayed orders in the surge demand scenario and adjust the demand to maximize social welfare under declining demand scenario. Additionally, we further find that the direct network externalities positively influence the platforms’ revenue, and the indirect network externalities have a negative effect on social welfare in the declining demand scenario, and a higher wage ratio cannot enhance the platforms’ revenue.


Sign in / Sign up

Export Citation Format

Share Document