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Improving issue representation with candidate‐level voting advice applications

MICHA GERMANN Orcid Logo, FERNANDO MENDEZ Orcid Logo, JONATHAN WHEATLEY Orcid Logo, CONSTANTINOS DJOUVAS Orcid Logo, ROULA NEZI Orcid Logo, Matthew Wall Orcid Logo

European Journal of Political Research

Swansea University Author: Matthew Wall Orcid Logo

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Abstract

Voting advice applications (VAAs) have proliferated in recent years. However, most VAAs only match their users with parties, at least in part because creating a VAA matching voters to individual candidates tends to be more labour‐intensive. This could be an important missed opportunity. Candidates m...

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Published in: European Journal of Political Research
ISSN: 0304-4130 1475-6765
Published: Wiley 2025
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa69341
Abstract: Voting advice applications (VAAs) have proliferated in recent years. However, most VAAs only match their users with parties, at least in part because creating a VAA matching voters to individual candidates tends to be more labour‐intensive. This could be an important missed opportunity. Candidates may deviate from the party line, but voters are often unaware of the policy platforms of individual candidates and therefore rarely hold them accountable for their issue positions in candidate‐based elections. VAAs providing information on issue congruence with individual candidates could help to rectify this. We evaluate the potential of candidate‐level VAAs by integrating a randomized experiment into a real‐world VAA whereby users were exposed either to candidate‐level VAA advice or to more standard party‐level VAA advice. Our results suggest that candidate‐level VAAs are worth the extra effort: they help voters distinguish candidates from parties and cast votes that are more in line with their policy preferences.
Item Description: Research Note
Keywords: voting advice applications, candidate preferences, democratic representation, issue voting, field experiment
College: Faculty of Humanities and Social Sciences
Funders: Economic and Social Research Council (Grant Number: ES/W000598/1), British Academy (Grant Number: SRG20\200273)