关键词: 产能分享, 平台, 交叉网络外部性, 匹配率, 社会福利

Abstract: In recent years, due to the rapid development of technologies such as the Internet of Things, big data, and cloud computing, along with the emergence of idle and excess resources, the “sharing economy” has seen swift growth as a new business model in the internet era. The advent of advanced concepts like Industry 4.0, cloud services, and cloud sharing has led to the rise in the “capacity sharing” model within the sharing economy. Capacity sharing primarily refers to an economic paradigm where internet platforms serve as the base, characterized by the sharing of processing equipment usage rights. It focuses on integrating and configuring diversified manufacturing resources and capabilities around each processing segment of the manufacturing process to maximize production efficiency in the manufacturing sector. The sharing of manufacturing capacity harbors significant opportunities and is set to become the main battleground for the future sharing economy. Our research primarily investigates the profit models and pricing decisions of intermediary-type capacity sharing platforms. Although there are studies on traditional platform pricing and sharing platform pricing, literature that simultaneously considers cross-network externalities and platform matching rates while choosing among platform profit models is still scarce. Therefore, our research constructs a two-stage game model to study and compare the profits obtained by capacity sharing platforms under different objectives within a targeted time frame when adopting transaction service fee models versus registration fee models. We explore the optimal profit model and its conditions, and analyzes the impact of cross-network externalities and platform matching rates on the platform’s maximum profit. The four scenarios considered in this paper are: (1)the platform aims to maximize its own profit by charging service fees only to suppliers; (2)the platform aims to maximize its own profit by charging registration fees to both suppliers and demanders; (3)the platform aims to maximize social welfare by charging service fees only to suppliers; (4)the platform aims to maximize social welfare by charging registration fees to both suppliers and demanders. Besides model construction and computation, this paper also conducts a numerical analysis for verification. The research in this paper draws three main conclusions:
(1)When the platform aims for profit maximization, under certain conditions of product price, cross-network externality coefficient, and matching rates, the platform can obtain greater profits by charging registration fees to both suppliers and demanders. Additionally, there are special conditions under which the platform would earn the same profit if it adopts a registration fee or service fee model.
(2)The platform’s maximum profit is positively correlated with the cross-network externality coefficient and the platform’s matching rate for suppliers, but negatively correlated with the matching rate for demanders. This is because the increased revenue brought by a higher matching rate for demanders is less than the loss caused by the reduction in the number of demanders. Therefore, platforms should focus on the matching rate for suppliers to achieve profit maximization.
(3)Without government subsidies, when the platform chooses a profit model that charges registration fees to both suppliers and demanders, it will need to provide certain subsidies to demanders at equilibrium, resulting in negative profits for the platform. In contrast, choosing a service fee model can yield greater profits.
The limitation of this paper lies in its focus on the choice of profit models for capacity sharing platforms within a certain time frame, without considering the long-term nature of the problem. If the long-term perspective is considered, companies might prefer a service fee model related to order volume to achieve maximum profit, as opposed to fixed registration fees. In studying the maximization of social welfare, this paper does not consider the impact of policy subsidies on platform behavior decisions. Therefore, future research related to the design of pricing mechanisms for capacity sharing platforms could take policy factors into account.

Key words: capacity sharing, platform, cross-network externality, matching rate, social welfare