Digital campaign competence The role of citizens in data-driven election campaigns
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| Award date | 20-09-2024 |
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| Number of pages | 217 |
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| Abstract |
Data-driven campaigning strategies provide political parties with new opportunities to inform and persuade voters, often using data unknowingly provided by citizens. Concerns for democracy’s health arise from worries about citizens’ data privacy and unintended consequences such as unequal access to political information online. This cumulative dissertation investigates citizens’ digital campaign competence and specifically addresses (a) the extent to which citizens know about data-driven campaigns, how they evaluate them, how they navigate through them, (b) whether their competence impacts their campaign (dis-) engagement, and (c) how citizens can be best supported to increase their competence. Using panel surveys from elections in the Netherlands and Germany, alongside an experimental study in Austria, this dissertation uncovers several key findings. Citizens exhibit a basic understanding of data-driven tactics online and their implications but lack the technical expertise to mitigate their negative impact effectively, and tend to underestimate their susceptibility. Citizens at large do not evaluate data-driven tactics positively. Skeptical individuals who understand these strategies tend to avoid them. Non-interactive educational interventions highlighting the risks of data-driven campaigning are the most effective means to enhance citizen competence. These findings highlight that citizen competence extends beyond mere knowledge, emphasizing its multifaceted nature. Citizens' evaluations of data-driven campaign strategies must be considered because their understanding alone does not explain their campaign (dis-) engagement. Solution-oriented research is needed to empower citizens, yet informing them while avoiding perceptions of pervasive targeting is challenging. These insights deepen our understanding of citizens' digital campaign competence and the complexities involved.
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| Document type | PhD thesis |
| Language | English |
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