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Reconcile the Personalization-Privacy Paradox: Exploring Privacy Boundaries in Online Personalized Advertising

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Version 3 2023-01-10, 20:19
Version 2 2022-12-18, 22:57
journal contribution
posted on 2023-01-10, 20:19 authored by Yu-Qian Zhu, Kritsapas Kanjanamekanant, Yi-Te Chiu

To reconcile the personalization-privacy paradox, we adopt the privacy as a state view and define privacy as a state of information boundary rule-following. We further identify five types of boundaries underlying some of the important implicit rules of maintaining privacy: communication channel, platform, device, temporal, and purpose boundaries. Using an online vignette survey, we investigated how each of these boundary types affected users’ privacy perceptions when they were subjected to personalized advertisements. Using fixed- and random-effects models, we investigated how violating different boundary rules leads to changes in perceived privacy. Our results show that all five boundary types are significant predictors of perceived privacy within individuals. The communication channel, device, and business versus private purpose are significant predictors of perceived privacy across the whole sample. Temporal boundaries and platform boundaries failed to achieve statistical significance when evaluated simultaneously with the other factors across the whole sample. This means that for each individual, observing the rules of these five boundary types leads to higher perceived privacy than not observing these conditions. Taken as a whole, observing communication channel, device, and business versus private purpose boundaries also leads to higher averages of perceived privacy across the whole sample. Theoretical and practical implications are discussed based on the results.

History

Preferred citation

Zhu, Y.-Q., Kanjanamekanant, K. & Chiu, Y.-T. (2022). Reconcile the Personalization-Privacy Paradox: Exploring Privacy Boundaries in Online Personalized Advertising. Journal of the Association of Information Systems.

Journal title

Journal of the Association of Information Systems

Volume

24

Issue

1

Publication date

2022-10-31

Publisher

Association for Information Systems

Publication status

Published online

Contribution type

Article

Online publication date

2022-08-31

ISSN

1536-9323