Contract Theory-Based Collection, Updating, and Packaging Combination Strategies in Data Trading.

The article focuses on optimal data transaction strategies for a monopoly data supplier selling to multiple heterogeneous data buyers, emphasizing the roles of data volume and currency (freshness) in determining data value. It develops mathematical models under two scenarios: information asymmetry, where the supplier knows only the distribution of buyer types, and information symmetry, where buyer types are fully known. Key findings include that data suppliers do not fully satisfy all buyers’ demands due to collection and update costs, with asymmetric information leading to reduced data collection volumes, lower buyer purchases, and decreased profits; however, suppliers compensate by increasing data update frequency to maintain data utility. The study also explores pricing, packaging, and update frequency strategies, highlighting the trade-offs between collection costs and update costs, and extends the analysis to buyer heterogeneity in update preferences and pay-as-you-go pricing models. These insights inform data suppliers and market regulators on managing data collection, updates, and sales to improve transaction efficiency while addressing information asymmetry challenges.