Understanding perceived privacy: A privacy boundary management model

Younghoon Chang, Siew Fan Wong, Hwansoo Lee

Research output: Contribution to conferencePaperpeer-review

3 Citations (Scopus)


Consumer data is asset to organizations. Analysis of consumers' transactional data helps organizations to understand customer behaviors and preferences. Before organizations could capitalize on these data, they ought to have effective plans to address consumers' privacy concerns because violation of consumer privacy brings long-term reputational damage to organizations. This paper proposes and tests a Privacy Boundary Management Model that explains how consumers formulate and manage their privacy boundary. Survey data was collected from 98 users of online banking websites who have used the system for a minimum of six months. The PLS results showed that the model accounts for high variance in perceived privacy. Three elements of the FIPs (notice, access, and enforcement) have significant impact on perceived effectiveness of privacy policy. Perceived effectiveness in turns significantly influences privacy control and privacy risks. Privacy control affects perceived privacy and trust while privacy risk influences privacy concern and perceived privacy. Privacy concern has a negative relationship with perceived privacy and trust has a positive relationship with perceived privacy. The findings have novel implications for organizations and policy makers.

Original languageEnglish
Publication statusPublished - 2015
Externally publishedYes
Event19th Pacific Asia Conference on Information Systems, PACIS 2015 - Singapore, Singapore
Duration: 5 Jul 20159 Jul 2015


Conference19th Pacific Asia Conference on Information Systems, PACIS 2015


  • Fair information practices Privacy boundary management
  • Perceived effectiveness
  • Perceived privacy
  • Privacy policy

ASJC Scopus subject areas

  • Information Systems


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