Rethinking consumer data management under end-user privacy-enhancing technologies
研究了消费者使用隐私增强技术如何导致数据缺失和测量误差，提出了两个框架帮助企业评估和调整数据分析，并通过产品推荐模拟案例展示了不同隐私技术特征对分析结果的影响。
As consumers increasingly adopt privacy-enhancing technologies (PETs) to protect personal information, firms face growing challenges in preserving consumer data integrity and the reliability of downstream analytics. By intervening at the point of data collection, end-user PETs introduce systematic distortions that reshape the data environment on which business analytics depend, yet their implications remain insufficiently understood. To address this gap, this study develops two complementary conceptual frameworks. The Data Integrity Framework characterizes how different end-user PETs generate missing values and measurement errors across attributes, entities, and relationships, offering a structured lens for conceptualizing privacy-induced data distortions. Building on this foundation, the Analytics Adaptation Framework provides guidance on how firms can assess and adapt their data analytics in response to these data distortions. To demonstrate their applicability, an illustrative simulation case study in product recommendation shows how key characteristics of end-user PET adoption—adoption rate and pattern, protection mechanism and intensity—systematically shape analytics outcomes. Together, the study advances IS research on data management by linking consumer privacy protection to data integrity and analytics adaptation, highlighting how consumer-driven privacy technologies fundamentally alter firms’ data and analytical environments.