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.
Highlights
In recent years, large-scale, web-based service outsourcing marketplaces, such as Upwork, Freelancer, and Amazon Mechanical Turk, are emerging rapidly
Regarding the auction format, we focus on the buyer-determined auction, because it is the most widely adopted trading mechanism in online service outsourcing platforms
Motivated by the emergence of online service outsourcing platforms where buyer-determined auctions are commonly used as the service trading mechanism and review systems are provided to facilitate transaction trust, this paper develops a game-theoretical model of online service outsourcing auctions with endogenous reviews
Summary
Large-scale, web-based service outsourcing marketplaces, such as Upwork, Freelancer, and Amazon Mechanical Turk, are emerging rapidly. In online services outsourcing platforms, services are commonly traded through buyer-determined auctions (e.g., [7], [8]), in which freelancers interested in a client’s service request compete with each other by bidding the prices at which they are willing to provide the service, and the client is free to choose any submitted bid (not necessarily the lowest-price bid) as the winning bid. Expecting the impacts of the review system on the clients’ and freelancers’ decision results (such as transaction volumes and prices), the platform needs to change its policies (e.g., commission rates) in order for profit maximization. As for question (a), we derive the optimal decisions of the client in designing the service scope and penalty (in case of low-quality delivery) and in selecting the winning freelancers, and we obtain the freelancers’ optimal bidding strategy and the winning freelancer’s optimal service effort. The last section summarizes the main findings and concludes the study
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