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Unobserved preferences and dynamic platform pricing under positive network externality

Hannu Huuki & Rauli Svento

Netnomics2020https://doi.org/10.1007/s11066-020-09140-warticle
AJG 1ABDC C
Weight
0.45

What the paper says

Abstract We study the dynamic optimization of platform pricing in industries with positive direct network externalities. The utility of the network for the consumer is modeled as a function of three components. Platform price and participation rate affect the consumer’s decision to join the platform. The platform operator is assumed to know the consumer’s sensitivities with respect to these components. In addition, the consumer’s utility is a function of other attributes, such as network privacy policies and environmental effects of the service. We assume that the distribution of these unobserved preferences in the potential customer base is known to the platform operator. We show analytically how the unobserved preferences affect the dynamic platform price design. Both static and rational expectations with respect to the platform participation are presented. We simulate an electricity market demand side management service application and show that the platform operator sets low prices in the launch phase. The platform operator can set higher launching prices if it can affect customers’ preferences, expectations or adjustment friction.

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https://doi.org/https://doi.org/10.1007/s11066-020-09140-w

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@article{hannu2020,
  title        = {{Unobserved preferences and dynamic platform pricing under positive network externality}},
  author       = {Hannu Huuki & Rauli Svento},
  journal      = {Netnomics},
  year         = {2020},
  doi          = {https://doi.org/https://doi.org/10.1007/s11066-020-09140-w},
}

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Unobserved preferences and dynamic platform pricing under positive network externality

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Evidence weight

0.45

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.27 × 0.4 = 0.11
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.