Peer privacy protection motivation and action on social networking sites: Privacy self-efficacy and information security as moderators

Hsin Hsin Chang, Kit Hong Wong, Ho Chin Lee

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


The protection motivation theory was adopted in this study to explore the effects of severity and susceptibility of the perceived threat of peer disclosure on privacy protection actions on social networking sites. Severity and susceptibility of a perceived threat were second-order formative constructs, measured by three reflective components: privacy disclosure, informal sanction, and online flaming. A set of relational factors were introduced as the antecedents, including interdependence, network convergence, depth, predictability, and commitment, which may facilitate social networking site user perceptions of the potential risk of information disclosure, in turn evoking protection motivation in their peers. In addition, in this study, two coping appraisal factors, privacy self-efficacy and information security awareness, are also considered as moderators in the research model. 740 valid questionnaires were collected from social networking site users and tested using partial least squares structural equation modeling techniques. The results showed that interdependence and network convergence have a positive effect on severity and susceptibility, respectively, and predictability and commitment have positive effects on both the severity of and susceptibility to perceived threats. The severity of a perceived threat and susceptibility to a perceived threat positively affect privacy protection behavior. Therefore, SNS companies should design several interactive mechanisms to help SNS users build online relationships with each other in order to enhance their consciousness of information security. Also, these companies should also educate beginners who lack privacy self-efficacy about how serious security threats will be and should also help experienced users determine how likely such threats will be to drive them to take privacy protection measures.

Original languageEnglish
Article number101176
JournalElectronic Commerce Research and Applications
Publication statusPublished - 2022 Jul 1

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Networks and Communications
  • Marketing
  • Management of Technology and Innovation


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