PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
May 29, 2026Expert Systems0 citationsOpen Access

Personalisation‐Privacy Paradox: Systematic Review and Survey Evidence on Personal Data Stores

View Full Paper
MHMing-Wei HsuGPGlenn ParryINIrene C. L. Ng

Key Points

Key points are not available for this paper at this time.

Abstract

ABSTRACT The personalisation–privacy paradox captures the tension between using personal data for personalised services and respecting individuals' privacy. This study adopts a holistic research framework to clarify the paradox's core challenges, review existing approaches, and examine a user‐centric solution. First, this study presents a systematic review of research on the personalisation–privacy paradox, drawing on studies published in information systems, marketing and management journals. The review shows that current approaches often frame the paradox as a ‘dilemma,’ prioritising privacy at the expense of personalisation, rather than recognising it as a persistent tension between the two. We then review broader categories of privacy‐enhancing technologies (PETs), highlighting that user‐side, server‐side, and channel‐side solutions provide partial responses but rarely balance both demands. Against this backdrop, personal data stores (PDS) are examined as a hybrid approach that aims to give individuals greater control over their data while enabling more effective personalisation through selective sharing. Survey results indicate that privacy concerns are reduced when users better understand PDS, act rationally in weighing benefits and risks, and trust existing data protection regulations. These findings suggest that PDS have the potential to offer a synthetic response to the paradox by supporting both personalisation benefits and privacy protection.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Hsu et al. (2026) studied this question.

synapsesocial.com/papers/6a1c191ebc71fb1015a93dedhttps://doi.org/10.1111/exsy.70314
Ask AI
Helpful
Bookmark
Share
View Full Paper