我们的助推，我们自身：利用人格定制移动用户参与度
Our Nudges, Our Selves: Tailoring Mobile User Engagement Using Personality
AI总结：
针对当前移动应用行为助推“一刀切”的问题，研究通过内嵌六种参与技术的AR应用HarrySpotter开展2周实验，发现用户对参与技术的偏好可预测大五人格特质，为个性化用户参与设计提供依据。
摘要：
为提升移动用户参与度，当前应用采用了多种行为助推手段，但这些参与度提升技术均采用“一刀切”的应用方式。然而，不同个体对同一技术的感知可能存在差异。为验证这一点，我们开发了HarrySpotter——一款基于位置的AR应用，内嵌了六种参与度提升技术。我们开展了为期2周的研究，招募了29名用户参与，这些用户还完成了大五人格测试。用户对特定参与度技术的偏好不仅能描述人格特质，还能对其进行预测。调整后$R^2$值范围从尽责型用户（受竞争激励）的0.16，到神经质型用户（以自我为中心、关注自身成就）的0.32，外向型用户（受物品和地点探索的双重激励）的该值甚至可达0.61。这些发现表明，未来需要对这些技术进行个性化设计。
英文摘要：
To increase mobile user engagement, current apps employ a variety of behavioral nudges, but these engagement techniques are applied in a one-size-fits-all approach. Yet the very same techniques may be perceived differently by different individuals. To test this, we developed HarrySpotter, a location-based AR app that embedded six engagement techniques. We deployed it in a 2-week study involving 29 users who also took the Big-Five personality test. Preferences for specific engagement techniques are not only descriptive but also predictive of personality traits. The Adj. $R^2$ ranges from 0.16 for conscientious users (encouraged by competition) to 0.32 for neurotic users (self-centered and focused on their own achievements), and even up to 0.61 for extroverts (motivated by both exploration of objects and places). These findings suggest that these techniques need to be personalized in the future.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
