Competition in data-based service provision: Nash equilibrium characterization
[EN] This paper analyzes the economic feasibility of a market for data-based services, which is a scenario envisioned in sectors that are adopting Internet of Things and/or Big Data technologies. A business model for the provision of a data-based service is proposed. In this model, service providers...
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Zusammenfassung: | [EN] This paper analyzes the economic feasibility of a market for data-based services, which is a scenario envisioned in sectors that are adopting Internet of Things and/or Big Data technologies. A business model for the provision of a data-based service is proposed. In this model, service providers build services from data that is collected and sold by data providers in a market, and provide services to final users. Service providers compete strategically against each other in the data market and in the service market. The strategic interaction between the service providers is analyzed by means of non-cooperative game theory. A thorough mathematical analysis of the Nash equilibria is carried out and existence and uniqueness conditions are proved and discussed.
We conclude that if the sensitivity of users to the data-rate-to-price ratio is above the unity and the number of service provider does not exceed a given limit, a unique and meaningful equilibrium exists. We also conclude that the intensity of competition is beneficial for the users and detrimental for the service providers, while for the data providers, maximum surplus is obtained in an intermediate intensity of competition. Finally, an increase in the users¿ sensitivity to the data-rate-to-price ratio causes a shift on the surplus from both the service providers and data providers to the users.
This work has been supported by the Spanish Ministry of Economy and Competitiveness through project TIN2013-47272-C2-1-R.
Guijarro, L.; Pla, V.; Vidal Catalá, JR.; Naldi, M. (2019). Competition in data-based service provision: Nash equilibrium characterization. Future Generation Computer Systems. 96:35-50. https://doi.org/10.1016/j.future.2019.01.044 |
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