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Cloud Services and Pricing Strategies for Sustainable Business Models: Analytical and Numerical Approaches

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  • Se-Hak Chun

    (Department of Business Administration, Seoul National University of Science and Technology, 232 Gongreung-Ro, Nowon-Gu, Seoul 01811, Korea)

Abstract

Previous studies have introduced different potential pricing strategies for cloud services. However, not much research has been done comparing subscription pricing and pay-per-use pricing, which are commonly used pricing schemes. Also, there are very few studies which analyze a two-part tariff pricing scheme for cloud services, even though this option may increasingly attract service providers as the cloud market becomes more competitive and the profit margin grows narrower. Previous research has focused on firms’ profitability rather than social welfare due to the limitations of free services. This study uses theoretical and numerical analysis to compare the social welfare and profitability of three pricing schemes commonly used by firms: subscription pricing, pay-per-use pricing, and two-part tariff pricing. It shows that the pay-per-use pricing is the best solution from the perspective of social welfare, which contrasts with the conclusion of a previous study stating that social welfare is maximized under a two-part tariff. This paper also shows that the two-part tariff is the most profitable pricing scheme for firms.

Suggested Citation

  • Se-Hak Chun, 2019. "Cloud Services and Pricing Strategies for Sustainable Business Models: Analytical and Numerical Approaches," Sustainability, MDPI, vol. 12(1), pages 1-15, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2019:i:1:p:49-:d:299679
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    References listed on IDEAS

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    1. Walter Y. Oi, 1971. "A Disneyland Dilemma: Two-Part Tariffs for a Mickey Mouse Monopoly," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 85(1), pages 77-96.
    2. Marc Walterbusch & Benedikt Martens & Frank Teuteberg, 2013. "Evaluating cloud computing services from a total cost of ownership perspective," Management Research Review, Emerald Group Publishing Limited, vol. 36(6), pages 613-638, May.
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    Cited by:

    1. George Fragiadakis & Evangelia Filiopoulou & Christos Michalakelis & Thomas Kamalakis & Mara Nikolaidou, 2023. "Applying Machine Learning in Cloud Service Price Prediction: The Case of Amazon IaaS," Future Internet, MDPI, vol. 15(8), pages 1-19, August.

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