2020
DOI: 10.1007/s12525-020-00404-9
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All of me? Users’ preferences for privacy-preserving data markets and the importance of anonymity

Abstract: Privacy-preserving data markets are one approach to restore users' online privacy and informational self-determination and to build reliable data markets for companies and research. We empirically analyze internet users' preferences for privacy in data sharing, combining qualitative and quantitative empirical methods. Study I aimed at uncovering users' mental models of privacy and preferences for data sharing. Study II quantified and confirmed motives, barriers, and conditions for privacy in data markets. Fina… Show more

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Cited by 50 publications
(34 citation statements)
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References 52 publications
(62 reference statements)
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“…Organizational decision-makers must balance the different ethically permissible options, especially by visibly addressing their workforce's concern or fear regarding autonomy and privacy loss while simultaneously offering prevention or mitigation strategies. Anonymizing data, even partially, until employees feel less threat emotions could be used (Schomakers et al, 2020).…”
Section: Discussionmentioning
confidence: 99%
“…Organizational decision-makers must balance the different ethically permissible options, especially by visibly addressing their workforce's concern or fear regarding autonomy and privacy loss while simultaneously offering prevention or mitigation strategies. Anonymizing data, even partially, until employees feel less threat emotions could be used (Schomakers et al, 2020).…”
Section: Discussionmentioning
confidence: 99%
“…This category explores data properties as a unit of analysis, such as data characteristics as economic goods (Demchenko et al, 2018) and approaches to identify data quality problems (Zhang et al, 2019). Meanwhile, literature on the users' preferences discusses data providers' willingness to share data considering aspects such as anonymity (Schomakers et al, 2020) and data ownership (Kamleitner & Mitchell, 2018).…”
Section: Results: Stof Model Categorizationmentioning
confidence: 99%
“…This category explores data properties as a unit of analysis, such as data characteristics as economic goods (Demchenko et al, 2018) and approaches to identify data quality problems (Zhang et al, 2019). Meanwhile, literature on the users' preferences discusses data providers' willingness to share data 34 TH BLED ECONFERENCE DIGITAL SUPPORT FROM CRISIS TO PROGRESSIVE CHANGE considering aspects such as anonymity (Schomakers et al, 2020) and data ownership (Kamleitner & Mitchell, 2018).…”
Section: Research Approachmentioning
confidence: 99%