论文标题
大量采用联系人跟踪应用程序 - 从用户的偏好中学习以改善应用程序设计
Towards Mass Adoption of Contact Tracing Apps -- Learning from Users' Preferences to Improve App Design
论文作者
论文摘要
接触示踪应用程序已成为控制和减慢Covid-19的传播并减轻锁定措施的主要方法之一。尽管这些应用程序可以在阻止传输链和挽救生命的情况下非常有效，但它们的采用仍处于预期的临界质量之下。有关联系跟踪应用程序的公开辩论强调了一般隐私保留，并且是在专家级别进行的，但缺乏与实际设计相关的用户观点。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偏好的联系跟踪应用程序。我们的主要贡献是对个人和群体偏好的经验见解，以及针对规定设计的见解。虽然我们的结果证实了大多数欧洲联系跟踪应用程序的隐私设计，但它们也提供了对可接受功能的更细微的理解。基于市场模拟和变异分析，我们得出结论，添加目标与一致的功能将在促进批量采用方面发挥重要作用。
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.
