Organizational basics of operating digital platforms

V.M. Gorbachuk, S.O. Gavrylenko, G.V. Golotsukov, M.M. Pustovoit

Abstract


Network effects are determined by the influence of an additional user of a product or service on the value that other users attach to this product or service. Platforms are then defined as entities that connect economic agents, actively managing network effects among the digital copies (images) of those agents. Network effects are distinguished by their sources: such sources can be users of the only group or users of several groups. Because, on a digital platform, network effects are generated jointly by all users, regardless of the groups to which they belong, and interest in the platform increases when the volume of interaction this platform manages increases, it is difficult to distinguish between different sources of network effects. User participation in the platform and their application of platform features can be important because their active evaluation of products and services, together with information provided by user actions (for platforms that collect and apply big data), gives an understanding of those actions, allows providing better services by the platform or adding specific offers. When consumers search for a product, they face travel costs, price information costs, and product feature comparison costs. When suppliers are looking for a willing buyer, they incur travel costs and communication costs regarding their products. Intermediaries reduce transaction costs by centralizing the exchange. In the presence of a random-matching market, there are profitable opportunities for intermediaries to conduct centralized exchanges, since buyers and sellers are influenced by the type of their matching partner, and intermediation allows self-selecting for types of economic agents. Intermediated trade can partially or completely replace decentralized trade and lead to more socially efficient allocations.

Prombles in programming 2022; 3-4: 491-501 

 


Keywords


network effects; intermediated trade; self-selection; decentralized pricing; matching; big data

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DOI: https://doi.org/10.15407/pp2022.03-04.491

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