Opinions or Algorithms: An Investigation of Trust in People Versus Automation in App Store Security

  • David Schuster
  • Mary L. Still
  • Jeremiah D. Still
  • Ji Jung Lim
  • Cary S. Feria
  • Christian P. Rohrer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9190)

Abstract

Mobile application (app) stores are a critical source of information about risk in an uncertain environment. App stores ought to assess and communicate the risk associated with an installation so that users are discouraged from installing risky or harmful apps in app stores. However, only a limited number of studies offer designers information about how to communicate risk effectively. We focused on the user’s trust associated with security information stemming from crowd-sourced evaluations compared to those generated from an automated system. Both of these sources of security information are pervasively used to indicate possible risk associated with an app. We investigated whether biases exist for a particular source of information given similar amount of security information being available. We found that participants preferred to install apps rated by automation to those rated by humans despite equivalence in stated risk. Further, we found evidence of a gender difference in trust in automation.

Keywords

Mobile device security App stores Trust in automation Interpersonal trust 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • David Schuster
    • 1
  • Mary L. Still
    • 1
  • Jeremiah D. Still
    • 1
  • Ji Jung Lim
    • 2
  • Cary S. Feria
    • 1
  • Christian P. Rohrer
    • 2
  1. 1.San José State UniversitySan JoseUSA
  2. 2.Intel SecuritySanta ClaraUSA

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