Toward a taxonomy of corporate data protection malpractices and their causal mechanisms: A regulatory view

• Published in: Journal of information technology 2023-09, Vol.38 (3), p.319-333
• Main Authors: Zhao, Haiping, Jiang, Na, Cai, Zhao, Lim, Eric T. K., Tan, Chee-Wee
• Format: Article
• Language: English
• Subjects: Empirical analysis; General Data Protection Regulation; Iterative methods; Legislation; Organizations; Taxonomy
• ISSN: 0268-3962; 1466-4437
• DOI: 10.1177/02683962231155937

Corporate data protection malpractices are not uncommon, especially in contemporary technological environments. Embracing a regulatory view, this study attempts to advance a taxonomy of prevailing corporate data protection practices and their causal mechanisms by analyzing cases where organizations were fined for violating data protection legislation. Selecting the General Data Protection Regulation (GDPR) enacted by the European Union (EU) as our benchmark, this study employs an iterative taxonomy development technique as guidance and conducts a thematic analysis on 875 cases of GDPR enforcement. In so doing, we derive a conceptual model comprising 6 focal categories and 28 subcategories of prevailing corporate data protection malpractices existing within organizations as well as 4 main categories and 22 subcategories of causal mechanisms underlying these identified malpractices. Empirical findings from this study not only reinforce corporate data protection malpractices established in prior research but also yield novel malpractices that have been neglected in previous work. From a pragmatic standpoint, this study yields invaluable insights into the prevention and resolution of corporate data protection malpractices for practitioners.