Visualisations in Voting Advice Applications and Their Effects: A Vignette Experiment

In many different fields, decision making is supported by software systems that facilitate a particular choice process. This can be management decisions in public as well as private organizations, but also those in other areas. Arguably, one of the most widely used decision support systems are so-called Voting Advice Applications (VAA), which enable citizens to find out which political party or candidate provides the best match in an upcoming election. The basic idea behind such tools is not only to aid the decision making, but also to facilitate a learning process. Via the interaction with the system’s interface, citizens can learn about the policy positions that parties or candidates take on a wide range of policy issues and how these add up to an overall level of matching. In countries with a multi-party system, in practice sometimes almost half of the voters use one or more of these tools. This paper analyses the different visualisations that these decision support systems use and how they affect the learning process and decision making. More specifically, we compare the three most popular visualisation types (bar chart, two-dimensional space, and spider diagram) and assess to what extent they help citizens to learn which party provides the best match and influence their decision making. We do so by embedding a vignette experiment in a survey about political preferences. The analyses suggest that citizens do indeed learn from such tools and subsequently let this information influence their decision making. The paper ends by discussing the implications for the future design of these tools.