Able but unwilling? Mapping citizens’ political GenAI use through the lens of Expectancy-Value Theory

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Generative AI (GenAI) holds promise and risk for democracy, yet little is known about howcitizens evaluate and adopt it for political purposes. Drawing on Expectancy-Value Theoryand Uses & Gratifications Theory, this study offers an initial account of political GenAIadoption, mapping how citizens' cost-value perceptions and expectancy beliefs cluster intodistinct profiles. We investigate how these ‘expectancy-value profiles’ shape political GenAIuse. Using cross-sectional survey data from 947 Dutch respondents, we conduct a latentprofile analysis, identifying four profiles: optimists, cautious pragmatists, sceptics, andindifferents. Profile differences stem largely from differing cost-value perceptions rather thanexpectancy beliefs, suggesting the question is less about whether people feel able to useGenAI, and more about whether they feel it is worth using. Across all profiles, costperceptions are high and political use is low, though optimists report significantly more usethan others. Individuals’ political dispositions predict usage but not profile membership andamong technological dispositions, trust in GenAI and belief in the machine heuristic predictbeing an optimist. Findings raise concerns about uncalibrated trust among adopters.