Online Service Outsourcing Auctions with Endogenous Reviews

Abstract

In the emerging online service outsourcing platforms where services are commonly transacted through buyer-determined auctions, review systems are usually provided to alleviate information asymmetry and opportunistic behavior between transaction parties. This paper develops a game-theoretic model of online service outsourcing auctions with endogenous reviews, where freelancers (service providers) with private information on service expertise compete the client's service contract through bidding, and the winning freelancer exerts effort to improve service quality, in anticipation of possible penalty and negative review for low-quality delivery. We obtain the optimal decisions of the client (service scope, penalty) and the freelancers (bidding strategy, effort), and examine the impacts of online review on the decision results of the transaction parties as well as the platform. Results show that the online review system drives the client to set smaller service scope and lower penalty, while leading to higher service effort and lower bidding price from freelancers. Both the client and the freelancers benefit from the review system. Numerical simulations also show that the platform should charge lower commission fees when the online review system is more effective.