Lastly, our control variables for both analyses comprise age, the dummy variables higher and lower education reference category: medium-level education , and gender dummy female which we obtained from the first wave of the panel survey.
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Importantly, we also control for political interest in all models, which becomes particularly relevant for cognitive personalization. Our modelling approaches require transformation of those independent variables that do not vary within voters during a respective wave. After that, we rescaled all independent variables dividing each by two standard deviations; wave and candidate dummies in the conditional logit models have been mean centred Gelman, This allows us to compare effects within models see also Giebler and Wagner, : The descriptive statistics of all raw variables can be found in the Online appendix.
We begin by answering our first research question and examine the extent of cognitive personalization among Dutch voters during the EP election campaigns. We report our results in Table 1 which contains the cross-classified multilevel logistic models. The likelihood-ratio tests are significant for all models. Hence, the chosen models are preferred over ordinary logistic regression. Note : Independent variables are standardized. We expected that the stronger the preference for a certain party, the more likely that voters recognize the lead candidate of that party H1.
Our results lend support to these assumptions. These results are in line with the extant literature that identifies a causal relationship between party preference and candidate recognition e. Gschwend and Zittel, ; Wolak, Turning to H2 , which stipulated that exposure to news about the lead candidates increases the likelihood of candidate recognition, our results lend support to the importance of information.
Model 1 shows that the effect of exposure to news about a respective candidate is positive holding everything else constant.
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Put differently, information is crucial for candidate recognition. Similarly, the effects of exposure to news about the respective candidates are also stronger in waves 2 and 3 compared to the post-election wave.
This suggests an over-time crystallization effect as voters might have a better idea about candidates and issues the closer the election day and are hence less dependent on information from the media Figure 1d. Fitted values for the probability to vote for respective party a and b and weighted news exposure to respective candidate c and d , by candidate and wave, respectively. We turn to Model 2 in Table 1 to test H3 , which hypothesized a moderating effect of exposure to candidate news on the effect of party preferences. It shows that the effect is not significant. For comparison, when substituting our weighted news exposure measure with either the unweighted or the comprehensive party exposure measures, the results do not yield significant interaction effects either see the Online appendix.
The results thus do not lend support to H3. The effects of the control variables show that older voters and women are more likely to recognize lead candidates although the latter effect is small compared to the rest.
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Higher education has positive effects compared to medium-level education, but there are no differences between low and medium levels of education. Lastly, political interest has a positive effect as the literature would expect e. Additional tests show further that the positive effect of exposure to news about the respective candidates on candidate recognition decreases with higher levels of political interest and is lower for those who are highly educated see the Online appendix.
This suggests that exposure to candidate news is less important for those who are more politically aware. Next, we are interested in the extent to which behavioural personalization applies. Table 2 reports the results from the conditional logit models. We expected that the more favourable a voter is towards the lead candidate of a party, the more likely that she will vote for that party H4a.
Holmberg and Oscarsson, Note : Robust standard errors; independent variables are standardized or mean centred dummies ; N respondents only includes those who participated in all waves; respondents observations dropped because of all positive or all negative outcomes.
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Furthermore, we are interested whether candidate evaluations moderate the positive effect of party preference on vote choice H4b. The interaction effect in Model 2 does not comply with conventional levels of statistical significance. We thus do not find support for H4b.
Likewise, the interaction effect between candidate evaluation and exposure to candidate news H4c is also not statistically significant Model 3. This constrains variation between voters. Table 2 shows that the direct effect of exposure to candidate news does not meet conventional levels of statistical significance. The same holds for comprehensive party exposure, but the unweighted news exposure has a positive effect although not in interaction with candidate evaluations, see the Online appendix.
The personalization of EU elections could potentially make the EU more accessible to its citizens see Gattermann, The aim of this article was to shed light onto the phenomenon by examining the role that individual lead candidates play for voting behaviour in EP elections. We have shown that exposure to candidate news plays a more decisive role than party preference for explaining why voters are more likely to recognize lead candidates in EP elections than not.
Our aim was to show that information from the news media is highly important during EP election campaigns. This information may eventually help voters to hold their representatives accountable in EU elections see Hobolt and Tilley, The wider literature on candidate recognition argues that there are additional explanatory factors related to candidates themselves, including their campaign activities; to voters, such as campaign interest and campaign exposure; or to the local election context e. We argued that the campaign context in Dutch EP elections is less important given the particularities of the electoral system.
However, we recommend that future research compares the effects of information acquired directly from the media vs information from campaign activities on the recognition of MEP candidates. Yet, we find that unweighted news exposure as well as comprehensive exposure to party news also serve as explanatory factors for the likelihood of providing candidate evaluations in the first place, suggesting that other news features might affect this relationship in addition to specific exposure to candidate news.
Moreover, we were unable to account for the entire information environment during the EP election campaigns. Voters might, for instance, be exposed to additional information about the candidates on social media, which might play a supplemental role for their vote choice e. Kruikemeier, Understanding how candidate evaluations come about is relevant because they have a direct and positive effect on vote choice in EU elections.
This finding resonates with some research on voting in national elections e. It suggests that individual politicians, in this case lead candidates, may serve as information short-cuts with which voters form their party political preferences in EU elections. However, we also expected that candidate evaluations moderate the effect of party preferences, and that personalized voting behaviour is contingent upon information from exposure to candidate news.
Our results do not support these assumptions. We proposed that possible reasons relate to the sample, because those who provide an evaluation in the first place are likely to be more politically aware. Moreover, our data exhibit little within-voter variation when it comes to exposure to individual candidates as these are generally hardly visible.
It remains to be seen whether some candidates receive more media coverage in certain outlets in future campaigns e. Labour candidates in left-leaning outlets and whether selective exposure of voters to this kind of content then has an effect on the evaluations. We argued that the Netherlands represent an excellent case given the attentiveness to individual candidates provided by the preferential voting system.
Furthermore, our findings might differ in other countries. That would also mean that in such countries more voters take into account their preferences for or against certain candidates when they go to the polls.
Although one would hence expect that behavioural personalization is less pronounced in countries where closed ballots in both national and EU elections prevail, such as in Germany see also Kaase, ; Karvonen, , research has shown that evaluations of lead MEP candidates also matter for party choice in German EP elections Giebler and Wagner, Comparative research is therefore required to examine the extent to which personalized voting behaviour in EU elections evolves across Europe, if at all. Lastly, comparisons to first-order elections are necessary in order to understand the scope of cognitive and behavioural personalization in EU elections see Giebler and Wagner, Future research should also investigate those factors on which voters base their evaluation of lead candidates in EU elections.
Voters are likely to take into account past political performance of the candidates — either inside the EP or in domestic politics. This would underline once more that information is crucial in order to evaluate the candidates in the first place. In the interim, individual lead candidates make a difference for only few voters, namely those who are politically aware. Increased concentration of power at the supranational level has led to a politicization of EU affairs, activating politicians, parties, journalists, and ultimately citizens see Hooghe and Marks, And since the politicization of EU integration is likely to increase in the future Kriesi, , the personalization of EU politics is likely to affect more voters.
Our aim was to provide a first account of personalized voting behaviour in EU elections; our results show that there is certainly a reason to further investigate the phenomenon. The usual disclaimer applies. In , For eight of them, alpha ranges from 0. For one politicians alpha is 0. However, in case of three politicians the score is below 0. To compare the samples, we a provide the respective descriptive statistics of party choice, and b we also estimate the main models with the candidate recognition dummy instead of candidate evaluations in the Online appendix.
The results are not significant. The BIC difference between the models including the effects of candidate and comprehensive party news exposure is marginal 0. National Center for Biotechnology Information , U. European Union Politics. Eur Union Polit.
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Published online May Author information Copyright and License information Disclaimer. Email: ln. This article has been cited by other articles in PMC. Associated Data Supplementary Materials Supplementary material. Abstract We study the personalization of voting behaviour in European Parliament elections. Keywords: Content analysis, European Parliament elections, media effects, personalization of politics, voter survey. Cognitive personalization in European Parliament elections EU elections do not function in the same way as national general elections: the former are considered second-order national elections which are characterized by lower turnout, and voter and party behaviour being determined by national political considerations e.
European Parliament elections and personalized voting behaviour Turning to the extent and conditions under which vote choice is influenced by behavioural personalization, extant scholarship is divided over the occurrence and strength of an effect of individual candidate evaluations on party choice. Findings We begin by answering our first research question and examine the extent of cognitive personalization among Dutch voters during the EP election campaigns.
Table 1. Cross-classified multilevel logistic regression, predicting candidate recognition.
Open in a separate window. Figure 1. Note : calculations based on Table A13 in the Online appendix. Table 2. Conditional logit models, predicting vote choice.