In the evolving landscape of data product exchange platforms, traditional economic valuation models fall short due to the non-rival nature of data and the prevalence of non-monetary data product exchanges. This paper introduces a normative, choice-based metric for valuing data products within intracompany exchanges, where conventional pricing mechanisms are absent. By modeling consumer attention and preferences, the proposed metric quantifies the value of data offerings based solely on user selection behavior, without relying on cost, demand, or competitive pricing data. We show that this metric can be formally cast as a cooperative game with a closed-form Shapley value, providing a principled and fairness-based allocation of value across offerings. The model rewards uniqueness and discriminative consumption, effectively addressing the limitations of popularity-based metrics and incentivizing the creation of high-value, long-tail data products. Through theoretical analysis and illustrative examples, the metric is shown to align with economic principles, support equitable valuation, and contribute to a robust framework for measuring gross data product value. Future research directions include exploring bundling strategies and quantifying product complementarity.

翻译：在数据产品交换平台不断演进的背景下，由于数据的非竞争性特征以及非货币化数据产品交换的普遍存在，传统经济估值模型已无法适用。本文针对企业内部数据交换场景（缺乏传统定价机制）提出了一种基于规范选择的价值度量方法。通过建模用户注意力与偏好，该度量方法仅依据用户选择行为即可量化数据产品的价值，无需依赖成本、需求或竞争性定价数据。我们证明该度量可形式化为具有闭式沙普利值的合作博弈模型，从而为各数据产品提供基于公平原则的价值分配机制。该模型通过奖励数据独特性与差异化消费行为，有效弥补了基于流行度指标的局限性，并激励高价值长尾数据产品的创造。理论分析与示例验证表明，该度量方法符合经济学原理，支持公平估值，并为数据产品总价值衡量建立了稳健框架。未来研究方向包括探索捆绑销售策略与产品互补性量化。