Management Information Systems Quarterly
Abstract
The increasing societal concern for consumer information privacy has led to the enforcement of privacy regulations worldwide. In an effort to adhere to privacy regulations such as the General Data Protection Regulation (GDPR), many companies’ privacy policies have become increasingly lengthy and complex. In this study, we adopted the computational design science paradigm to design a novel privacy policy evolution analytics framework to help identify how companies change and present their privacy policies based on privacy regulations. The framework includes a self-attentive annotation system (SAAS) that automatically annotates paragraph-length segments in privacy policies to help stakeholders identify data practices of interest for further investigation. We rigorously evaluated SAAS against state-of-the-art machine learning (ML) and deep learning (DL)-based methods on a well-established privacy policy dataset, OPP-115. SAAS outperformed conventional ML and DL models in terms of F1-score by statistically significant margins. We demonstrate the proposed framework’s practical utility with an in-depth case study of GDPR’s impact on Amazon’s privacy policies. The case study results indicate that Amazon’s post-GDPR privacy policy potentially violates a fundamental principle of GDPR by causing consumers to exert more effort to find information about first-party data collection. Given the increasing importance of consumer information privacy, the proposed framework has important implications for regulators and companies. We discuss several design principles followed by the SAAS that can help guide future design science-based e-commerce, health, and privacy research.