Date of Award

5-2026

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Psychology

Committee Chair/Advisor

Dr. Kaileigh A. Byrne

Committee Member

Dr. Bart Knijnenburg

Committee Member

Dr. Richard Pak

Committee Member

Dr. Shubham Agrawal

Abstract

The rapid growth of information communication technology (ICT) requires users to exchange personal information online, enabling access to various services and functionalities but also exposing them to privacy risks such as targeted ads and data misuse via online cookies. Online cookies are small text files stored by websites in users’ browsers that help track users’ activity and preferences for future visits, which can raise privacy concerns. One relatively novel factor that may impair privacy protection behaviors regarding online cookies is privacy fatigue, which has been shown to weaken users’ intentions to protect their privacy. However, its impact on actual privacy behaviors remains unclear as existing research has predominantly focused on behavioral intentions. Additionally, many users lack sufficient knowledge to distinguish cookie functions, and cookie banners often use dark patterns to nudge them away from privacy protection. This research aims to further investigate the role of privacy fatigue on privacy decisions and explore the effect of different interventions designed to promote informed privacy decisions on online cookie acceptance behaviors. Study 1 examines the impact of privacy fatigue on actual privacy behavior and whether it fluctuates after repeated decisions. Participants’ privacy fatigue levels were measured before and after completing tasks involving repeated privacy decisions regarding cookies on simulated websites. Study 1a found no significant changes in privacy fatigue, while Study 1b revealed a significant increase following repeated decisions. Study 2 evaluates the effectiveness of privacy education tutorials on privacy decisions. Individuals’ privacy fatigue, knowledge, and behaviors before and after receiving privacy education tutorial were measured. The tutorials significantly enhanced privacy knowledge and privacy protection behaviors, including online cookie decisions, but privacy fatigue still increased despite the intervention. In Study 3, participants’ behavioral intention and actual decisions regarding cookies were measured through contextual scenarios and simulated websites. Machine learning algorithms were applied to the dataset to develop a set of “smart” default settings for the browser-level online cookie settings, tailored to users’ preferences. These “smart” defaults predicted users’ cookie decisions with an accuracy of 80.64% and allowed them to set their cookie preference with a single click, which could potentially alleviate privacy fatigue by reducing the burden of repeated decisions.

Author ORCID Identifier

0000-0001-6482-2642

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Available for download on Monday, May 31, 2027

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