E-privacy in 2nd generation E-commerce: privacy preferences versus actual behavior
Proceedings of the 3rd ACM conference on Electronic Commerce
Tailoring Privacy to Users' Needs
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
Creating an E-commerce environment where consumers are willing to share personal information
Designing personalized user experiences in eCommerce
Impacts of user privacy preferences on personalized systems: a comparative study
Designing personalized user experiences in eCommerce
Internet Users' Information Privacy Concerns (IUIPC): The Construct, the Scale, and a Causal Model
Information Systems Research
Location disclosure to social relations: why, when, & what people want to share
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Personalization versus Privacy: An Empirical Examination of the Online Consumer's Dilemma
Information Technology and Management
Crowdsourcing user studies with Mechanical Turk
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Recommendation Agents for Electronic Commerce: Effects of Explanation Facilities on Trusting Beliefs
Journal of Management Information Systems
Privacy-enhanced web personalization
The adaptive web
The impact of social navigation on privacy policy configuration
Proceedings of the Sixth Symposium on Usable Privacy and Security
Sharing location in online social networks
IEEE Network: The Magazine of Global Internetworking
The Effect of Online Privacy Information on Purchasing Behavior: An Experimental Study
Information Systems Research
PET'04 Proceedings of the 4th international conference on Privacy Enhancing Technologies
Explaining the user experience of recommender systems
User Modeling and User-Adapted Interaction
Making Decisions about Privacy: Information Disclosure in Context-Aware Recommender Systems
ACM Transactions on Interactive Intelligent Systems (TiiS)
Dimensionality of information disclosure behavior
International Journal of Human-Computer Studies
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Personalization relies on personal data about each individual user. Users are quite often reluctant though to disclose information about themselves and to be "tracked" by a system. We investigated whether different types of rationales (justifications) for disclosure that have been suggested in the privacy literature would increase users' willingness to divulge demographic and contextual information about themselves, and would raise their satisfaction with the system. We also looked at the effect of the order of requests, owing to findings from the literature. Our experiment with a mockup of a mobile app recommender shows that there is no single strategy that is optimal for everyone. Heuristics can be defined though that select for each user the most effective justification to raise disclosure or satisfaction, taking the user's gender, disclosure tendency, and the type of solicited personal information into account. We discuss the implications of these findings for research aimed at personalizing privacy strategies to each individual user.