Privacy cynicism: A new approach to the privacy paradox

Hoffmann, Christian; Lutz, Christoph; Ranzini, Giulia

doi:10.5817/cp2016-4-7

Cited by 122 publications

(113 citation statements)

References 48 publications

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“…58% of Europeans see no alternative to the provision of personal information to obtain products or services (ibid.). Active privacy protection which could soothe concerns is perceived as too complex, not feasible, and users are resigned (Hoffmann et al 2016). On the other hand, protection is neglected because of a feeling of "nothing to hide" (Prettyman et al 2015) or "no-one is interested in my data" (Schomakers et al 2018).…”

Section: Online Privacy and Data Sharingmentioning

confidence: 99%

All of me? Users’ preferences for privacy-preserving data markets and the importance of anonymity

2020

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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. Finally, in a conjoint study, trade-offs between decisive attributes that shape the decision to share data are analyzed. Additionally, differences between user groups with high and with low privacy concerns are observed. The results show that the anonymization level has the greatest impact on the willingness to share data, followed by the type of data. Users with higher privacy concerns are less willing to share data in data markets and want more privacy protection. The results contribute to an understanding of how privacy-preserving data markets could be designed to suit users' preferences.

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Section: Online Privacy and Data Sharingmentioning

confidence: 99%

All of me? Users’ preferences for privacy-preserving data markets and the importance of anonymity

2020

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“…Finally, there could also be no long-term relation at all: People might have already become used to sharing information online, which stifles further cognitive or emotional processing. This rationale is central to so-called privacy cynicism (e.g., Hoffmann, Lutz, & Ranzini, 2016).…”

Section: Longitudinal Analysis Of the Privacy Paradox 11mentioning

confidence: 99%

A Longitudinal Analysis of the Privacy Paradox

Dienlin¹,

Masur²,

Trepte³

2019

Preprint

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The privacy paradox states that people’s concerns about online privacy are unrelated to their online sharing of personal information. On the basis of a representative sample of the German population, which includes 1403 respondents who were interviewed at three waves separated by 6 months, we investigate the privacy paradox from a longitudinal perspective. Using a cross-lagged panel model with random intercepts, we differentiate between-person relations from within-person effects. Results revealed that people who were more concerned about their online privacy than others also shared slightly less personal information and had substantially more negative attitudes toward information sharing (between-person level). People who were more concerned than usual also shared slightly less information than usual (within-person level). We found no long-term effects of privacy concerns on information sharing or attitudes 6 months later. The results provide further evidence against the privacy paradox, but more research is needed to better understand the variables’ potential causal relations.

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“…Previous research has provided different answers as to why privacy disclosure behavior shows paradoxical tendencies [1], [5]- [8], and even whether it does at all [2], [4]. For example, [1] consider the Privacy Paradox to emerge from a gap between disclosure intentions and disclosure behavior.…”

Section: State Of Researchmentioning

confidence: 99%

Can Probabilistic Cognitive Modeling Explain Adoption Behavior of Smartphone Apps Gathering Private Data?

Schürmann¹

2019

Preprint

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Predominant perspectives in privacy behavior research describe human decision making as boundedly rational, cognitively biased, and driven by heuristics. This description stands in contrast to Bayesian decision theory, which de-scribes decision making as mathematically rational utility maximization. Recently, research has proposed rational process modeling as a way of considering justified criticisms of Bayesian models with regards to psychological plausibil-ity. In rational process modeling, locally irrational behavior emerges from an approximation of Bayesian inference focused on the attribution of cognitive resources. In two online surveys, we have applied a rational process model to predict the choice behavior of 103 and 113 participants. They saw 3 (study 1) and 2 (study 2) app store offerings of fictional smartphone apps related to travel or messaging and decided whether to use them. Stated preferences for features of these apps were gathered and sampled from until triggering a probabilistic stopping rule. Then, they were integrated via Bayes’ rule and used to predict service adop-tion. Depending on the number of samples, the model would show behavior ranging from extreme probability matching to near-deterministic utility maximization. We compared the model prediction of observed participant behavior to three alternative models by using Bayes Fac-tors: a deterministic Bayesian model, the take-the-best heuristic, and a probabilistic variant of said heuristic. We found that the data give the most support to the proposed rational process model, although all models tended to over-estimate service adoption. We connect our results to previ-ous work in cognitive science and online privacy research and outline approaches derived from the model to encour-age privacy-preserving user behavior through interventions.

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Privacy cynicism: A new approach to the privacy paradox

Abstract: Abstract

Cited by 122 publications

References 48 publications

All of me? Users’ preferences for privacy-preserving data markets and the importance of anonymity

All of me? Users’ preferences for privacy-preserving data markets and the importance of anonymity

A Longitudinal Analysis of the Privacy Paradox

Can Probabilistic Cognitive Modeling Explain Adoption Behavior of Smartphone Apps Gathering Private Data?

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