Legally compliant personalised prioritisation of privacy policy information shows no effect on user engagement, comprehension, or workload

Privacy policies function as both legal documents and information sources for users, but their length and complexity often discourage engagement. In this paper, we investigate whether a personalised approach can address this issue by prioritising information that concerns individual users most while maintaining a policy’s legal compliance on disclosure. We first explored whether personal characteristics can be used to predict a person’s most concerned category and, hence, serve as a baseline for personalisation. We then conducted an eye-tracking experiment and interviews (n = 30) to understand the effectiveness of personalised reordering of privacy policies. In the interviews, many participants perceived personalised reordering as helpful, although others raised concerns about the invasion of privacy through this personalisation. The eye-tracking results indicate that personalised reordering leads to higher engagement for the first few sentences of a privacy policy. Based on our findings, we present design recommendations for creating legally compliant forms of privacy disclosures that encourage user engagement as well as discussions and implications on privacy disclosure compliance.

Authors

Institutions

Publication Details

Journal
Behaviour and Information Technology
Published
2026-06-29
DOI
https://doi.org/10.1080/0144929x.2026.2692098
Primary Topic
Privacy, Security, and Data Protection
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Legally compliant personalised prioritisation of privacy policy information shows no effect on user engagement, comprehension, or workload

Simon Mayer, Clement Guitton, Kenan Bektaş, Jannis Strecker-Bischoff et al.
Behaviour and Information Technology
Privacy, Security, and Data Protection
article

Legally compliant personalised prioritisation of privacy policy information shows no effect on user engagement, comprehension, or workload

Simon Mayer, Clement Guitton, Kenan Bektaş, Jannis Strecker-Bischoff, Meihe Xu, Aurelia Tamo-Larrieux
article en

Abstract

Privacy policies function as both legal documents and information sources for users, but their length and complexity often discourage engagement. In this paper, we investigate whether a personalised approach can address this issue by prioritising information that concerns individual users most while maintaining a policy’s legal compliance on disclosure. We first explored whether personal characteristics can be used to predict a person’s most concerned category and, hence, serve as a baseline for personalisation. We then conducted an eye-tracking experiment and interviews (n = 30) to understand the effectiveness of personalised reordering of privacy policies. In the interviews, many participants perceived personalised reordering as helpful, although others raised concerns about the invasion of privacy through this personalisation. The eye-tracking results indicate that personalised reordering leads to higher engagement for the first few sentences of a privacy policy. Based on our findings, we present design recommendations for creating legally compliant forms of privacy disclosures that encourage user engagement as well as discussions and implications on privacy disclosure compliance.

Behaviour and Information Technology
University of St.Gallen (CH), Maastricht University (NL), University of Lausanne (CH)
Openalex Percentile: Top 4%
Privacy, Security, and Data Protection
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.