five

In Pursuit of Informed Voters: Three Experimental Studies on Enhanced Voting Advice Applications

收藏
DataCite Commons2026-01-30 更新2026-04-25 收录
下载链接:
https://dataverse.nl/citation?persistentId=doi:10.34894/R2YWEL
下载链接
链接失效反馈
官方服务:
资源简介:
Voters frequently struggle to understand political attitude statements in Voting Advice Applications (VAAs) and often invest limited effort in resolving these difficulties. Conversational Agent VAAs (CAVAAs) aim to reduce the cognitive effort involved in searching for relevant information by integrating chatbots that can provide contextual support. This paper presents findings from three studies comparing CAVAAs to standard VAAs without additional information (Studies 1 and 2) and to VAAs with static clickable explanations (Study 3, VAA+). Study 1 (N = 93) was a laboratory experiment conducted during the 2023 Dutch Parliamentary elections with university students. Participants were assigned to either a standard VAA, or a CAVAA condition. The chatbot in the CAVAA was used in approximately 45% of cases, with users showing a preference for opinion-based and status quo information. Compared to the standard VAA, CAVAAs reduced non-directional responses and increased evaluations of the tool being usable, Moreover, as well as users’users perceived knowledge was higher, whereas no differences were found for factual knowledge and turn-out intention. Study 2 (N = 144) largely replicated these results in a field setting with a more diverse sample and showed that the effects held hold across different levels of political sophistication. Study 3 (N = 159), conducted during the 2024 European elections, compared a CAVAA to a VAA+ with static clickable explanations. While VAA+ users requested information more frequently than CAVAA users, both tools received similar evaluations, and this finding was again consistent across user groups based on their level of political sophistication. In the manuscript we discuss the implications of these findings for theory and practice.
提供机构:
DataverseNL
创建时间:
2025-08-08
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作