隐私政策
互联网隐私
信息隐私
消费者隐私
设计隐私
业务
通知
个人可识别信息
隐私软件
FTC公平信息实践
控制(管理)
信息隐私法
计算机安全
计算机科学
法学
人工智能
政治学
作者
Younghoon Chang,Siew Fan Wong,Christian Fernando Libaque-Sáenz,Hwansoo Lee
标识
DOI:10.1016/j.giq.2018.04.002
摘要
With today's big data and analytics capability, access to consumer data provides competitive advantage. Analysis of consumers' transactional data helps organizations to understand customer behaviors and preferences. However, prior to capitalizing on the data, organizations ought to have effective plans for addressing consumers' privacy concerns because violation of consumer privacy brings long-term reputational damage. This paper proposes and tests a Privacy Boundary Management Model, explaining how consumers formulate and manage their privacy boundary. It also analyzes the effect of the five dimensions of privacy policy (Fair Information Practices) on privacy boundary formation to assess how customers link these dimensions to the effectiveness of privacy policy. Survey data was collected from 363 customers who have used online banking websites for a minimum of six months. Partial Least Square results showed that the validated research model accounts for high variance in perceived privacy. Four elements of the Fair Information Practice Principles (access, notice, security, and enforcement) have significant impact on perceived effectiveness of privacy policy. Perceived effectiveness in turn significantly influences perceived privacy control and perceived privacy risk. Perceived privacy control significantly influences trust and perceived privacy. Perceived privacy concern and trust also significantly influence perceived privacy.
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