Publication type: Conference paper
Type of review: Peer review (abstract)
Title: Does context in privacy communication really matter? : a survey on consumer concerns and preferences
Authors: Ebert, Nico
Ackermann, Kurt Alexander
Heinrich, Peter
et. al: No
DOI: 10.1145/3313831.3376575
Proceedings: CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
Issue: 448
Conference details: Conference on Human Factors in Computing Systems (CHI), Honolulu, USA, 25-30 April 2020
Issue Date: 2020
Publisher / Ed. Institution: Association for Computing Machinery
Publisher / Ed. Institution: New York
ISBN: 978-1-4503-6708-0
Language: English
Subjects: Privacy; User preference; Policy; Privacy concern
Subject (DDC): 005: Computer programming, programs and data
Abstract: Privacy policies as a means of communicating with customers still prove ineffective. Researchers have recently suggested that a specific usage context should be considered to make privacy notices more relevant to users. To explore this approach further, we conducted an explorative online survey of privacy concerns and privacy information preferences with 642 participants for two different contexts (loyalty cards and fitness tracking). Our data shows some support for the suggestion that context may be a significant moderator of concerns and preferences. However, the corresponding effects are rather small and limited to specific concerns and information categories. In line with other research, the data supports the known hierarchy of concerns regarding unauthorized secondary use and improper data access, which seem to exceed concerns about erroneous data processing or excessive data collection in both contexts. Furthermore, participants considered information on personal rights and processing purposes more relevant than information on contact persons.
URI: https://digitalcollection.zhaw.ch/handle/11475/20111
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Management and Law
Organisational Unit: Institute of Business Information Technology (IWI)
Appears in collections:Publikationen School of Management and Law

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Ebert, N., Ackermann, K. A., & Heinrich, P. (2020). Does context in privacy communication really matter? : a survey on consumer concerns and preferences [Conference paper]. CHI ’20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 448. https://doi.org/10.1145/3313831.3376575
Ebert, N., Ackermann, K.A. and Heinrich, P. (2020) ‘Does context in privacy communication really matter? : a survey on consumer concerns and preferences’, in CHI ’20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. New York: Association for Computing Machinery. Available at: https://doi.org/10.1145/3313831.3376575.
N. Ebert, K. A. Ackermann, and P. Heinrich, “Does context in privacy communication really matter? : a survey on consumer concerns and preferences,” in CHI ’20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 2020, no. 448. doi: 10.1145/3313831.3376575.
EBERT, Nico, Kurt Alexander ACKERMANN und Peter HEINRICH, 2020. Does context in privacy communication really matter? : a survey on consumer concerns and preferences. In: CHI ’20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Conference paper. New York: Association for Computing Machinery. 2020. ISBN 978-1-4503-6708-0
Ebert, Nico, Kurt Alexander Ackermann, and Peter Heinrich. 2020. “Does Context in Privacy Communication Really Matter? : A Survey on Consumer Concerns and Preferences.” Conference paper. In CHI ’20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. New York: Association for Computing Machinery. https://doi.org/10.1145/3313831.3376575.
Ebert, Nico, et al. “Does Context in Privacy Communication Really Matter? : A Survey on Consumer Concerns and Preferences.” CHI ’20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, no. 448, Association for Computing Machinery, 2020, https://doi.org/10.1145/3313831.3376575.


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