A Taxonomy of Business Models for Data Intermediaries

Background: Both the rising dominance of large platforms in data markets and the increasing exchange of personal data among organizations within data ecosystems have led to significant asymmetries, as individuals have limited control over data-related decisions and restricted access to the value generated from data use. Scholars have called for data intermediaries to help address these asymmetries, yet little research has examined how their business models function within data ecosystems that facilitate personal data exchange. Aim: This study investigates the key characteristics and archetypes of business models of data intermediaries in data ecosystems that facilitate the exchange of personal data. Method: Using Nickerson’s taxonomy methodology and the Al-Debei business model ontological structure, a comprehensive taxonomy of data intermediary business models is developed. Hierarchical clustering is then applied to identify distinct archetypes. Results: The research develops a taxonomy of data intermediary business models in data ecosystems centered on personal data exchange and derives archetypes through hierarchical clustering. The identified taxonomy dimensions clarify how these business models operate and create value within personal data ecosystems from a network-level business model perspective. Based on this framework, eight archetypes are identified, illustrating how data intermediaries prioritize interests of different ecosystem actors. Dataset: This dataset contains: A summary of the 107 cases analyzed in this research, and how the researchers mapped the dimensions and characteristics from the taxonomy onto these cases. Definitions of the dimensions and characteristics in the taxonomy. A cross-table analysis showing how frequently the dimensions and characteristics appear in the eight identified archetypes.背景：数据市场中大型平台的话语权日益提升，同时数据生态系统（data ecosystems）内各机构间的个人数据流转愈发频繁，二者共同引发了显著的信息不对称问题——个人对数据相关决策的控制权有限，且难以获取数据使用所产生的价值。学界呼吁通过数据中介机构（data intermediaries）来缓解这类信息不对称，但目前鲜有研究探讨数据中介机构在促进个人数据流转的数据生态系统中的商业模式运作逻辑。 研究目的：本研究旨在探究促进个人数据流转的数据生态系统中，数据中介机构商业模式的核心特征与原型类型。 研究方法：本研究采用尼克森分类学方法论（Nickerson’s taxonomy methodology）与阿尔-德比商业模式本体结构（Al-Debei business model ontological structure），构建了数据中介机构商业模式的完整分类框架；随后通过层级聚类（Hierarchical clustering）法识别出差异化的原型类型。 研究结果：本研究构建了以个人数据流转为核心的数据生态系统中数据中介机构商业模式的分类框架，并通过层级聚类推导得到原型类型。所确定的分类维度从网络级商业模式视角，阐明了此类商业模式在个人数据生态系统中的运作与价值创造逻辑。基于该框架，本研究识别出八种原型类型，用以展现数据中介机构如何优先兼顾生态系统内不同参与者的利益。 数据集：本数据集包含以下内容： 1. 本研究分析的107个案例的汇总信息，以及研究人员如何将分类框架中的维度与特征映射至这些案例的过程说明； 2. 分类框架中各维度与特征的定义说明； 3. 交叉表分析结果，用以展示各维度与特征在八种已识别原型类型中的出现频率。