考虑质量与数量双维下的数据交易定价决策研究

Data Transaction Pricing Decisions Considering Both Quality and Quantity Dimensions

摘要: 本研究针对数据交易市场中普遍存在的数据交易价格与实际价值不匹配问题，深入探讨如何从质量和数量两个维度进行科学合理的数据交易定价，以推动市场健康发展。通过分析由数据供应商、数据交易平台和数据消费者组成的数据供应链系统，研究发现：在分散定价下，原始数据销售价格与数据产品销售价格始终与原始数据数量呈正相关，在收益共享契约中，原始数据销售价格始终与原始数据数量呈正相关；在集中定价下，数据供应商提供高质量、低数量的原始数据可使其获得最大收益；在分散定价与收益共享契约下，数据供应商提供低质量、高数量的原始数据可使其获得最大收益。研究结论为数据供应商如何提供有效的原始数据给予了理论指导，同时对数据定价及利益相关者提供有效的决策依据。

Abstract: This study addresses the widespread mismatch between data transaction prices and actual value in the data trading market, exploring how to scientifically and rationally price data transactions from both quality and quantity perspectives to promote healthy market development. Through analysis of a data supply chain system comprised of data providers, data trading platforms, and data consumers, the study finds that: under decentralized pricing, the sales price of raw data and data product sales prices are consistently positively correlated with the quantity of raw data; in revenue-sharing contracts, the sales price of raw data is consistently positively correlated with the quantity of raw data; under centralized pricing, data providers maximize their profits by providing high-quality, low-quantity raw data; and under both decentralized pricing and revenue-sharing contracts, data providers maximize their profits by providing low-quality, high-quantity raw data. These findings provide theoretical guidance for data providers on how to offer effective raw data and offer effective decision-making support for data pricing and stakeholders.