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Why Would I Use Location-Protective Settings on My Smartphone? Motivating Protective Behaviors and the Existence of the Privacy Knowledge–Belief Gap

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  • Robert E. Crossler

    (Department of Management, Information Systems, and Entrepreneurship, Carson College of Business, Washington State University, Pullman, Washington 99163)

  • France Bélanger

    (ACIS Department, Pamplin College of Business, Virginia Tech, Blacksburg, Virginia 24061)

Abstract

The omnipresence of smartphones means that more and more personal information is accessed, transferred, or stored on these devices. Smartphone users struggle to control the release of their information when smartphones are always connected, close at hand, and the privacy settings for individual apps are difficult to access. To have meaningful privacy in this context, individuals must be knowledgeable about their devices and truly motivated to make use of the device’s privacy settings. We draw from extant privacy literature, the self-efficacy theory, and the information–motivation–behavioral skills model to understand usage of privacy settings on smartphones through data from 334 iPhone users. Our findings indicate that personal motivation is one of the strongest determinants of utilizing privacy-protective settings, and social motivation is not significant. Furthermore, privacy knowledge and self-efficacy constructs (i.e., knowledge specific to the device’s privacy settings) determine one’s use of privacy-protective settings, but knowledge and self-efficacy about smartphone technology do not. An interaction effect also exists between privacy knowledge and privacy self-efficacy such that people with high levels of privacy knowledge utilize less restrictive privacy settings when their confidence in protecting themselves is low, but as their self-efficacy increases, they are more likely to use more privacy-protective settings. We label this the privacy knowledge–belief gap.

Suggested Citation

  • Robert E. Crossler & France Bélanger, 2019. "Why Would I Use Location-Protective Settings on My Smartphone? Motivating Protective Behaviors and the Existence of the Privacy Knowledge–Belief Gap," Information Systems Research, INFORMS, vol. 30(3), pages 995-1006, September.
  • Handle: RePEc:inm:orisre:v:30:y:2019:i:3:p:995-1006
    DOI: 10.1287/isre.2019.0846
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    References listed on IDEAS

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    Cited by:

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    2. Cloarec, Julien & Cadieu, Charlotte & Alrabie, Nour, 2024. "Tracking technologies in eHealth: Revisiting the personalization-privacy paradox through the transparency-control framework," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    3. Cloarec, Julien, 2022. "Privacy controls as an information source to reduce data poisoning in artificial intelligence-powered personalization," Journal of Business Research, Elsevier, vol. 152(C), pages 144-153.
    4. Jia Gao & Ying Rong & Xin Tian & Yuliang Yao, 2024. "Improving Convenience or Saving Face? An Empirical Analysis of the Use of Facial Recognition Payment Technology in Retail," Information Systems Research, INFORMS, vol. 35(1), pages 16-27, March.
    5. Crystal Reeck & Xue Guo & Angelika Dimoka & Paul A. Pavlou, 2024. "Uncovering the Neural Processes of Privacy: A Neurally Informed Behavioral Intervention to Protect Information Privacy," Information Systems Research, INFORMS, vol. 35(2), pages 727-746, June.
    6. Frank Ebbers & Jan Zibuschka & Christian Zimmermann & Oliver Hinz, 2021. "User preferences for privacy features in digital assistants," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 411-426, June.
    7. Chen, Claire Y.T. & Sun, Edward W. & Miao, Wanyu & Lin, Yi-Bing, 2024. "Reconciling business analytics with graphically initialized subspace clustering for optimal nonlinear pricing," European Journal of Operational Research, Elsevier, vol. 312(3), pages 1086-1107.

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