自我导向的在线信息搜索可以影响政策支持：一项结合数字行为数据的随机鼓励设计
Self-directed online information search can affect policy support: a randomized encouragement design with digital behavioral data
AI总结：
通过一项三波田野实验（n=791），结合数字痕迹数据，研究发现自我导向的在线信息搜索在货币激励条件下对某些政策议题的支持产生显著变化，揭示了动机激活、议题可塑性和搜索环境质量是影响政策支持变化的关键条件。
摘要：
随着公民在数字环境中越来越多地接触政治信息，理解这种参与是否影响其政策观点已成为核心关注点。借鉴说服的双重过程理论，我们认为在高选择性的在线环境中，动机激活是政策支持变化的促成条件。我们在一个三波田野实验中对此进行了检验，参与者为德国人（n=791），涉及三个政策议题（基本儿童抚养费、可再生能源转型、大麻合法化），参与者被随机分配到对照组和两个鼓励条件组：口头鼓励或与知识测试挂钩的金钱激励。通过数字痕迹数据在20小时窗口内被动跟踪浏览行为。我们发现，自我导向的在线信息搜索对儿童抚养费和大麻合法化的政策支持产生了变化，但对能源转型没有影响，其中金钱激励比口头提示产生了显著效果。我们讨论了动机显著性、议题可塑性和搜索环境质量作为政治信息参与能够产生可检测的政策支持变化的联合条件。
英文摘要：
As citizens increasingly encounter political information in digital environments, understanding whether this engagement shapes their policy views has become a central concern. Drawing on dual-process theories of persuasion, we argue that motivational activation is an enabling condition for policy support change in high-choice online environments. We test this in a three-wave field experiment with German participants (n = 791) across three policy topics (basic child support, renewable energy transition, cannabis legalization), in which participants were randomly assigned to a control group, and two encouragement conditions: a verbal encouragement, or a monetary incentive tied to a knowledge test. Browsing behavior was passively tracked via digital trace data over a 20-hour window. We find that self-directed online information search produced changes in policy support for child support and cannabis legalization but not for the energy transition, with monetary incentives producing significant effects rather than verbal prompts. We discuss motivational salience, issue malleability, and search-environment quality as joint conditions under which political information engagement can produce detectable changes in policy support.
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