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Does Context in Privacy Communication Really Matter? — A Survey on Consumer Concerns and Preferences

Published: 23 April 2020 Publication History

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.

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The file contains the raw survey data both in Excel and SPSS format.

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    cover image ACM Conferences
    CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
    April 2020
    10688 pages
    ISBN:9781450367080
    DOI:10.1145/3313831
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    Published: 23 April 2020

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    1. policy
    2. privacy
    3. privacy concerns
    4. user preferences

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    • (2024)“Our Users' Privacy is Paramount to Us”: A Discourse Analysis of How Period and Fertility Tracking App Companies Address the Roe v Wade OverturnProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642384(1-21)Online publication date: 11-May-2024
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    • (2022)Transparency of privacy notices and contextualisation: effectively conveying information without wordsBehaviour & Information Technology10.1080/0144929X.2022.207723441:10(2120-2150)Online publication date: 23-May-2022
    • (2022)Transferring recommendations through privacy user models across domainsUser Modeling and User-Adapted Interaction10.1007/s11257-021-09307-632:1-2(25-90)Online publication date: 1-Apr-2022
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