多归宿
利用
互联网隐私
定向广告
信息隐私
计算机科学
优势(遗传学)
消费者隐私
业务
竞赛(生物学)
计算机安全
共享经济
隐私软件
商业模式
最终用户
骨料(复合)
市场份额
展示广告
数据共享
服务提供商
大数据
设计隐私
隐私政策
作者
Xin Zhang,Xu Hong,Wei Thoo Yue,Yugang Yu
标识
DOI:10.1177/10591478261483184
摘要
User data is central to the operations of many online platforms, enabling personalized services for users and targeted advertising for advertisers. However, as users become increasingly aware of privacy issues associated with platform usage, they may alter their usage levels and engagement patterns, such as switching between singlehoming (using one platform exclusively) and multihoming (using multiple platforms). These shifts in user behavior are not only reshaping platform dynamics but also creating ripple effects for advertisers. In this study, we develop a game-theoretic model to explore how privacy concerns influence user strategies, advertiser decisions, and platform performance, and to assess the effectiveness of common privacy practices implemented by platforms. Our analysis generates several interesting insights. First, we demonstrate that as privacy concerns increase, users are motivated to shift from singlehoming to multihoming, especially when their privacy concerns are relatively low. This shift occurs because multihoming allows users to limit their data exposure on any single platform, thereby reducing the potential for platforms to exploit their data in depth. Second, we find that heightened privacy concerns can unexpectedly benefit platforms by increasing user traffic and aggregate usage, which makes platforms more appealing to advertisers and increases advertiser demand. Third, our analysis reveals that increased privacy concerns can reduce the market dominance of the large platform with strong data capabilities, narrowing the gap in market share between competing platforms. Furthermore, we discuss important managerial and policy implications regarding partial data use and privacy protection, offering insights into how platforms can balance user privacy and platform performance. Finally, we extend our model in several directions to demonstrate the robustness of our key findings and obtain additional insights.
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