App Users Unwittingly in the Spotlight: A Model of Privacy Protection in Mobile Apps

Wottrich, Verena M.; Reijmersdal, Eva A. van; Smit, Edith G.

doi:10.1111/joca.12218

Cited by 33 publications

(41 citation statements)

References 47 publications

(118 reference statements)

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“…We made sure to account for the generally low knowledge level (Smit et al, 2014) by not simply measuring, but by manipulating it. Also, we hoped to maximize the effect by narrowing down the manipulation to a specific context, i.e., Google (as past studies have concluded that insignificant effects could be explained by too general measures, e.g., Wottrich et al, 2018). However, the effect was small and opposite to our expectation.…”

Section: Discussionmentioning

confidence: 82%

“…First, technical knowledge has been identified as one of the main predictors of health information privacy concern (Ermakova Fabian, Kelkel, Wolff, & Zarnekow, 2015). Second, more concerned individuals have been found to refrain from using certain apps (Wottrich, Van Reijmersdal, & Smit, 2018), or reject cookies that enable personalization (Milne & Culnan, 2004). Thus, we argue that privacy concern needs to be added as a factor within the threat appraisal that is impacted by technical knowledge and motivates users to opt-out from personalization.…”

Section: Explaining Empowering Impact Of Knowledge Through Pmtmentioning

confidence: 92%

“…While this construct is rather irrelevant to health protection, it has been shown crucial in the data processing context. In fact, more concerned individuals have been found to refrain from using certain apps (Wottrich et al, 2018) or reject cookies (Milne & Culnan, 2004).…”

Section: Knowledge and Perceived Severity Of And Susceptibility To Damentioning

confidence: 99%

“…A later version of the PMT included the value of the threat as a construct that lowers motivation to stop it (Maddux & Rogers, 1983). In the mobile app context, Wottrich et al (2018) concluded that when users like an app, they are less motivated to prevent it from collecting their data. Similarly, personalization has numerous benefits for consumers, such as informativeness, credibility, and entertainment of advertising (Kim & Han, 2014), which may make them less motivated to opt-out.…”

Section: Knowledge and Attitude Towards The Risky Behavior As A Part mentioning

confidence: 99%

“…Opt-out motivation. Opt-out motivation was measured with one item (1 = very unlikely and 7 = very likely) inspired by Wottrich et al (2018): 'Over the next two weeks, I intend to protect my privacy by using the settings available on adsetting.google.com' (M = 5.04, SD = 1.92).…”

Section: Participants and Data Collectionmentioning

confidence: 99%

See 4 more Smart Citations

Protective behavior against personalized ads: Motivation to turn personalization off

Strycharz¹,

Noort²,

Smit³

et al. 2019

Cyberpsychology

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Data collection and processing for personalized advertising has become a common practice in the industry. For this reason, regulators have been aiming to empower consumers to exercise more control over their data. Companies that collect and process data for personalized advertising are required to be transparent and among others, provide consumers with technical knowledge about the personalization process. At the same time, they have started offering settings to withdraw consent for processing data for personalization purposes by opting out from personalized advertising. However, such opt-out functions remain mostly unused. Thus, this study investigates first, if technical knowledge about personalization empowers consumers to use such opt-out functions and second, what mechanisms can explain the empowering impact of knowledge. Drawing on Rogers’ (1975) protection motivation theory (PMT) and applying an innovative combination of traditional (online experiment, N = 425, Mage = 48) and computational (online behavior tracking, N = 80, Mage = 48) research methods, the study shows that technical knowledge has no empowering effect on consumers by indirectly lowering opt-out motivation and behavior. The results also demonstrate that perceived severity and response efficacy increase motivation to opt-out, while positive attitude towards personalization and perceived self-efficacy lower it. Being one of the first studies to apply PMT to personalization context and computational methods to measure opt-out, it offers several important societal and theoretical implications regarding consumer empowerment and personalized advertising online.

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Section: Discussionmentioning

confidence: 82%

Section: Explaining Empowering Impact Of Knowledge Through Pmtmentioning

confidence: 92%

Section: Knowledge and Perceived Severity Of And Susceptibility To Damentioning

confidence: 99%

Section: Knowledge and Attitude Towards The Risky Behavior As A Part mentioning

confidence: 99%

Section: Participants and Data Collectionmentioning

confidence: 99%

See 3 more Smart Citations

Protective behavior against personalized ads: Motivation to turn personalization off

Strycharz¹,

Noort²,

Smit³

et al. 2019

Cyberpsychology

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Consumer Differences in Chilling Effects

Strycharz¹,

Segijn²

2023

European Advertising Academy

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Digitale Gesundheitstechnologien – das Recht als Hemmschuh oder Wegbereiter?

Freye,

Buchner

2024

Bundesgesundheitsbl

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ZusammenfassungDer potenzielle Nutzen digitaler Gesundheitstechnologien hängt im Bereich der populationsbezogenen Gesundheitsforschung maßgeblich davon ab, ob und in welchem Umfang sich diese Technologien auf eine Verarbeitung personenbezogener Gesundheitsdaten stützen lassen. Allerdings herrscht erhebliche Unsicherheit bei der Anwendung und Auslegung der einschlägigen rechtlichen Regelungen zur Verarbeitung von Forschungsdaten mittels digitaler Gesundheitstechnologien. Die Praxis der Forschungsdatenverarbeitung ist immer noch maßgeblich vom Primat der Einwilligung als Legitimationsgrundlage für eine Datenverarbeitung geprägt, obwohl das Informationsmodell des deutschen und europäischen Gesetzgebers mit seinen ambitionierten Anforderungen an die freiwillige und informierte Einwilligung realitätsfern ist. Auch die Konzepte des Broad Consent, Dynamic Consent und Meta Consent, die Alternativen zur klassischen Einwilligungslösung darstellen, können nicht sämtliche Defizite des Einwilligungsmodells beheben.Um die informationelle Selbstbestimmung der betroffenen Personen zu gewährleisten und gleichzeitig die Interessen der Forschung im Public-Health-Bereich im Blick zu behalten, muss der Forschungsdatenschutz weiterentwickelt werden. Lösungen müssen dabei nicht nur am Einwilligungsverhalten selbst ansetzen, sondern auch eine Legitimation der Datenverarbeitung ganz ohne Einwilligung in den Blick nehmen oder auf eine unwiederbringliche Aufhebung des Personenbezugs der Daten abzielen. Dieser Diskussionsartikel beleuchtet die ambivalente Rolle des Rechts im Hinblick auf digitale Gesundheitstechnologien und zeigt, dass der oftmals als Hindernis verstandene Gesundheitsdatenschutz – bei entsprechender Weiterentwicklung – durchaus den Weg für digitale Gesundheitstechnologien bereiten kann.

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App Users Unwittingly in the Spotlight: A Model of Privacy Protection in Mobile Apps

Cited by 33 publications

References 47 publications

Protective behavior against personalized ads: Motivation to turn personalization off

Protective behavior against personalized ads: Motivation to turn personalization off

Consumer Differences in Chilling Effects

Digitale Gesundheitstechnologien – das Recht als Hemmschuh oder Wegbereiter?

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