The privacy calculus suggests that online self-disclosure is based on a cost–benefit trade-off. However, although companies progressively collect information to offer tailored services, the effect of both personalization and context-dependency on self-disclosure has remained understudied. Building on the privacy calculus, we hypothesized that benefits, privacy costs, and trust would predict online self-disclosure. Moreover, we analyzed the impact of personalization, investigating whether effects would differ for health, news, and commercial websites. Results from an online experiment using a representative Dutch sample (N = 1,131) supported the privacy calculus, revealing that it was stable across contexts. Personalization decreased trust slightly and benefits marginally. Interestingly, these effects were context-dependent: While personalization affected outcomes in news and commerce contexts, no effects emerged in the health context.

中文翻译：

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了解个性化作为隐私演算的影响：分析健康，新闻和商业环境中的自我披露^†

隐私计算表明，在线自我披露是基于成本与收益之间的权衡。但是，尽管公司逐渐收集信息以提供量身定制的服务，但个性化和上下文相关性对自我披露的影响仍未得到充分研究。基于隐私演算，我们假设收益，隐私成本和信任度可以预测在线自我披露。此外，我们分析了个性化的影响，调查了对健康，新闻和商业网站的影响是否会有所不同。使用代表性荷兰样本（N = 1,131）进行的在线实验结果支持了隐私演算，表明该演算在各种情况下都是稳定的。个性化会稍微降低信任度，并会带来一些好处。有趣的是，这些影响取决于上下文：