Analyzing personal data privacy protection: a DEMATEL-ISM-MICMAC approach
TL;DR
A five-dimensional policy package is proposed that reinforces regulatory drivers, institutionalises trust-repair mechanisms, leverages trauma-induced learning effects, optimises transmission pathways, and implements full-cycle collaborative governance, thereby offering a systemic intervention framework to resolve the dilemma of fragmented governance.
Abstract
In the digital era, personal data privacy protection has become a key element of sustainable development. However, most existing literature examines technical, legal, and behavioural aspects in isolation, overlooking the nonlinear propagation mechanisms across different levels. This study integrates a literature review and expert interviews to comprehensively survey existing works and identifies thirteen determinants involving data subjects, data collectors, and regulatory agencies. Building on this foundation, we innovatively integrate a three-stage hybrid methodology—DEMATEL-ISM-MICMAC—to systematically map causal dependencies, hierarchical structures, and attribute robustness among factors. The results reveal that the completeness of legal frameworks and enforcement intensity function as core drivers, whereas trust constitutes a highly dependent yet fragile node. Critical transmission paths encompass the longest chain of “institution–enforcement–cognition–trust” and the shortest chain of “legal provisions→processing transparency”. On this basis, we propose a five-dimensional policy package that (i) reinforces regulatory drivers, (ii) institutionalises trust-repair mechanisms, (iii) leverages trauma-induced learning effects, (iv) optimises transmission pathways, and (v) implements full-cycle collaborative governance, thereby offering a systemic intervention framework to resolve the dilemma of fragmented governance.