This work contributes to the theoretical foundation for pricing in data markets and offers practical insights for managing digital data exchanges in the era of big data. We propose a structured pricing model for data exchanges transitioning from quasi-public to market-oriented operations. To address the complex dynamics among data exchanges, suppliers, and consumers, the authors develop a three-stage Stackelberg game framework. In this model, the data exchange acts as a leader setting transaction commission rates, suppliers are intermediate leaders determining unit prices, and consumers are followers making purchasing decisions. Two pricing strategies are examined: the Independent Pricing Approach (IPA) and the novel Perfectly Competitive Pricing Approach (PCPA), which accounts for competition among data providers. Using backward induction, the study derives subgame-perfect equilibria and proves the existence and uniqueness of Stackelberg equilibria under both approaches. Extensive numerical simulations are carried out in the model, demonstrating that PCPA enhances data demander utility, encourages supplier competition, increases transaction volume, and improves the overall profitability and sustainability of data exchanges. Social welfare analysis further confirms PCPA's superiority in promoting efficient and fair data markets.

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To cite this article: Yangming Lyu, Linyi Qian, Zhixin Yang, Jing Yao & Xiaochen Zuo (27 Nov 2025): Optimal pricing approaches for data markets in market-operated data exchanges, Statistical Theory and Related Fields, DOI: 10.1080/24754269.2025.2588857

To link to this article: https://doi.org/10.1080/24754269.2025.2588857