Contact tracing apps have become one of the main approaches to control and slow down the spread of COVID-19 and ease up lockdown measures. While these apps can be very effective in stopping the transmission chain and saving lives, their adoption remains under the expected critical mass. The public debate about contact tracing apps emphasizes general privacy reservations and is conducted at an expert level, but lacks the user perspective related to actual designs. To address this gap, we explore user preferences for contact tracing apps using market research techniques, and specifically conjoint analysis. Our main contributions are empirical insights into individual and group preferences, as well as insights for prescriptive design. While our results confirm the privacy-preserving design of most European contact tracing apps, they also provide a more nuanced understanding of acceptable features. Based on market simulation and variation analysis, we conclude that adding goal-congruent features will play an important role in fostering mass adoption.

中文翻译：

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致力于大规模采用联系人跟踪应用程序-学习用户的偏好设置以改进应用程序设计

联系人跟踪应用程序已成为控制和减缓COVID-19的传播并简化锁定措施的主要方法之一。尽管这些应用程序在阻止传输链和挽救生命方面非常有效，但其采用率仍低于预期的临界水平。关于联系人跟踪应用程序的公开辩论强调了一般的隐私保留，并且是在专家级别上进行的，但是缺乏与实际设计相关的用户角度。为了解决这一差距，我们使用市场研究技术（特别是联合分析）来探索用户对联系人跟踪应用程序的偏好。我们的主要贡献是对个人和团体偏好的经验性见解，以及对处方设计的见解。我们的结果证实了大多数欧洲联系人跟踪应用程序的隐私保护设计，它们还提供了对可接受功能的更细微的了解。根据市场模拟和变化分析，我们得出结论，添加目标一致的功能将在促进大规模采用方面发挥重要作用。