Powered by OpenAIRE graph
Found an issue? Give us feedback
addClaim

Choice Architecture, Framing, and Cascaded Privacy Choices

Authors: Idris Adjerid; Alessandro Acquisti; George Loewenstein;

Choice Architecture, Framing, and Cascaded Privacy Choices

Abstract

or consumers, managing privacy online requires navigating a complex process of interrelated choices. This process may be conceived of as "cascaded," in that a combination of upstream choices (e.g., of privacy settings on a social network site) and downstream choices (e.g., of what to reveal on the site) together determine ultimate privacy outcomes. In a series of experiments, we examine the potential impact of choice architecture in cascaded privacy choice settings. We investigate how changes in choice frames implemented by service providers can influence consumers' upstream disclosure settings, often in ways that they are unaware of and that may be destructive to them. Whether the effects of choice frames upstream are ultimately detrimental to individuals' privacy, however, depends on whether they are offset by more or less protective downstream choices. Thus, we also examine whether such upstream effects of choice architecture are "mitigated" through changes in downstream self-disclosure. We find, first, that various manipulations of decision frames, common in privacy contexts, significantly impact participants' upstream choice of disclosure settings. Second, we do not find evidence that the impact of choice architecture upstream is mitigated downstream: participants' self-disclosure rates do not adjust or change in response to choice architecture-induced changes in upstream choices. These findings call into question both policy makers' and industry advocates' reliance on choice-based privacy protection mechanisms, contribute to an emerging behavioral perspective on privacy decision making, and highlight the importance of accounting for the cascaded nature of privacy decision making in both policy and managerial settings.

Related Organizations
Keywords

Operations research, Decision making

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    20
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
20
Top 10%
Top 10%
Top 10%
Upload OA version
Are you the author of this publication? Upload your Open Access version to Zenodo!
It’s fast and easy, just two clicks!