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

In the data transaction process, where a single supplier collects and sells data to heterogeneous data buyers, its transaction strategy relies on the data’s value. In this paper, we measure the value of data in terms of volume and currency and analyze the whole process from data collection and update management to sales. The data supplier determines the maximum amount of data to be collected based on the willingness to pay and the cost of collection and decides the optimal update frequency based on the update cost paid to improve the data utility. Because data sets can be replicated and split nearly costlessly, data suppliers maximize revenue by combining data sets of different sizes. In the information asymmetry scenario, contract theory is applied to obtain the optimal data collection quantity, update frequency, and packaging combination scheme to maximize the profit of data suppliers under data buyer incentive compatibility and individual rationality and compare them with the information symmetry scenario. The results show that data suppliers will only provide personalized data sets to some data buyers, and the needs of some buyers will not be met. Information asymmetry causes data suppliers to reduce the number of data collections and the actual purchases by data buyers, and data suppliers’ profits tend to decrease. The surprising conclusion is that data suppliers should set up more frequent update strategies in the information asymmetry case than in the information symmetry case to sustain the decrease in data utility caused by insufficient quantity.