In the digital economy, two-sided platforms increasingly operate as data intermediaries, establishing vital revenue streams and competitive differentiation. This study aims to explore how competing two-sided platforms, acting as data intermediaries, strategically optimize their collection and pricing of user-generated data under cost heterogeneity and varying affiliation behaviors.
This study uses the Hotelling model to construct a multi-stage game framework, analyzing data collection and the related pricing decisions for competitive platforms. Through equilibrium analysis and practical validation, we reveal how duopolistic platforms strategically select full/partial data collection regimes while optimizing pricing, explicitly incorporating per-unit collection costs and cross-sided network effects.
A significant cost advantage prompts differentiated collection strategies, whereas smaller cost gaps favor consistent partial collection by both. Under partial collection, the cost-disadvantaged platform gains disproportionately from increased data intake, driving more aggressive price adjustments. Rising marginal value of data usage can lead platforms to lower price to expand their user base and leverage higher per-unit data utility for greater profitability. Consumer affiliation predominantly shifts optimal pricing, while provider affiliation significantly alters collection decision.
This study innovatively analyzes platform pricing from the perspective of data intermediation. Departing from the most existing literature focused on product/service intermediation, this research advances the understanding of platform pricing by examining data's unique role as a strategic asset. Formalizing non-rivalrous traits and near-zero re-production costs, this study reveals platforms' strategic trade-offs between differentiated/consistent data strategies, offering insights for leveraging data assets to enhance competitiveness and support sustainable platform growth.
