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“Ask App Not to Track”: The Effect of Opt-In Tracking Authorization on Mobile Privacy

https://doi.org/10.1007/978-3-030-93747-8_11
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25/25 checkable references clean · checked 2026-07-25

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

14 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 25 checked references that resolve
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Nudging Privacy: The Behavioral Economics of Personal Information
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Gone in 15 Seconds: The Limits of Privacy Transparency and Control
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What Is Privacy Worth?
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Sleights of privacy
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Your Location has been Shared 5,398 Times!
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Studying the effectiveness of android application permissions requests
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Third Party Tracking in the Mobile Ecosystem
resolves10.1515/popets-2016-0038
Tales from the Dark Side: Privacy Dark Strategies and Privacy Dark Patterns
resolves10.1007/978-3-642-40477-1_5
Nudging People Away from Privacy-Invasive Mobile Apps through Visual Framing
resolves10.1016/j.cose.2018.06.005
Influence of privacy priming and security framing on mobile app selection
resolves10.1145/1772690.1772719
Malicious interface design
resolves10.1145/3313831.3376600
UI Dark Patterns and Where to Find Them
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I've got 99 problems, but vibration ain't one
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Android permissions
resolves10.1145/3173574.3174108
The Dark (Patterns) Side of UX Design
resolves10.1023/A:1015044207315
Defaults, Framing and Privacy: Why Opting In-Opting Out1
resolves10.2307/1914185
Prospect Theory: An Analysis of Decision under Risk
resolves10.1007/978-3-642-34638-5_6
A Conundrum of Permissions: Installing Applications on an Android Smartphone
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Expectation and purpose
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Dark Patterns at Scale
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Android vs iOS Security: A Comparative Study
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Dark Patterns: Past, Present, and Future
resolves10.14722/ndss.2018.23353
Apps, Trackers, Privacy, and Regulators: A Global Study of the Mobile Tracking Ecosystem
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Loss Aversion in Riskless Choice: A Reference-Dependent Model
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The Feasibility of Dynamically Granted Permissions: Aligning Mobile Privacy with User Preferences
The 14 references without a DOI — listed, not checked
no DOI — not checkedAlashoor, T., Fox, G., Jeff Smith, H.: The priming effect of prominent is privacy concerns scales on disclosure outcomes: an empirical examination. In: Pre-ICIS Workshop on Information Security and Privacy (2017)
no DOI — not checkedBalebako, R., et al.: Nudging users towards privacy on mobile devices (2011)
no DOI — not checkedBonné, B., Peddinti, S.T., Bilogrevic, I., Taft, N.: Exploring decision making with android’s runtime permission dialogs using in-context surveys. In: Thirteenth Symposium on Usable Privacy and Security, pp. 195–210 (2017)
no DOI — not checkedBrignull. H.: Dark patterns (2019)
no DOI — not checkedNorwegian Consumer Council. Deceived by design, how tech companies use dark patterns to discourage us from exercising our rights to privacy. Norwegian Consumer Council Report (2018)
no DOI — not checkedGluck, J., et al.: How short is too short? implications of length and framing on the effectiveness of privacy notices. In: Twelfth Symposium on Usable Privacy and Security, pp. 321–340 (2016)
no DOI — not checkedGrossklags, J., Acquisti, A.: When 25 cents is too much: an experiment on willingness-to-sell and willingness-to-protect personal information. In: WEIS (2007)
no DOI — not checkedApple Inc., Human interface guidelines (2021)
no DOI — not checkedKurtz, A., Weinlein, A., Settgast, C., Freiling, F.: DiOS: dynamic privacy analysis of iOS applications (2014)
no DOI — not checkedLiccardi, I., Pato, J., Weitzner, D.J.: Improving mobile app selection through transparency and better permission analysis. J. Priv. Confidentiality 5(2), 1–55 (2014)
no DOI — not checkedLiu, B., et al.: Follow my recommendations: a personalized privacy assistant for mobile app permissions. In: Twelfth Symposium on Usable Privacy and Security, pp. 27–41 (2016)
no DOI — not checkedSchreiner, M., Hess, T.: On the willingness to pay for privacy as a freemium model: first empirical evidence (2013)
no DOI — not checkedSensor Tower. Top charts: iphone - us - all categories, June 2021
no DOI — not checkedVallina-Rodriguez, N., et al.: Tracking the trackers: towards understanding the mobile advertising and tracking ecosystem. arXiv preprint arXiv:1609.07190 (2016)
What this badge says. CiteStamped means the CHECKABLE references of this work were clean at the dated check: each resolved to a known work in a public registry, and none carried a retraction notice at that time. It says nothing about the quality, findings, or importance of the work itself, and nothing about references deposited without a DOI.

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