Online Privacy Heuristics that Predict Information Disclosure

S. Shyam Sundar, Jinyoung Kim, Mary Beth Rosson, Maria D. Molina

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Online users' attitudes toward privacy are context-dependent. Studies show that contextual cues are quite influential in motivating users to disclose personal information. Increasingly, these cues are embedded in the interface, but the mechanisms of their effects (e.g., unprofessional design contributing to more disclosure) are not fully understood. We posit that each cue triggers a specific "cognitive heuristic" that provides a rationale for decision-making. Using a national survey (N = 786) that elicited participants' disclosure intentions in common online scenarios, we identify 12 distinct heuristics relevant to privacy, and demonstrate that they are systematically associated with information disclosure. Data show that those with a higher accessibility to a given heuristic are more likely to disclose information. Design implications for protection of online privacy and security are discussed.

Original languageEnglish (US)
Title of host publicationCHI 2020 - Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450367080
DOIs
StatePublished - Apr 21 2020
Event2020 ACM CHI Conference on Human Factors in Computing Systems, CHI 2020 - Honolulu, United States
Duration: Apr 25 2020Apr 30 2020

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2020 ACM CHI Conference on Human Factors in Computing Systems, CHI 2020
CountryUnited States
CityHonolulu
Period4/25/204/30/20

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Software

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