Exposing Civic Normativity: Applying the Persona-Based Walkthrough Method to the Dutch Happiness Meter

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This study analyzes the Dutch Happiness Meter (HM) – a digital tool employed by the government to quantify citizens’ happiness – through the lens of critical data studies. We introduce the “persona-based walkthrough method” to explore the HM’s algorithmic underpinnings and its socio technical construction of happiness. By navigating diverse personas through the HM’s interface, we answer the following questions: RQ1: How does the Dutch Happiness Meter (HM) embed socio-cultural norms and biases within its algorithmic design, and how do these translate to the quantification and representation of citizen happiness across diverse demographic groups? RQ2: How does the persona-based walkthrough method reveal the limitations and exclusions of black-boxed e-government applications such as the Happiness Meter, and how can this method contribute to algorithmic accountability and transparency in digital governance? and RQ3: What are the implications of datafying subjective well-being through tools like the Happiness Meter on public perceptions of happiness, and how does algorithmic governance influence the epistemologies of well-being in the context of policy-making and societal inclusion? The analysis untangles cultural and computational synergies, examining their influence on civic normativity and quantified well-being. Our contribution shows how such data-driven systems construct a normative understanding of happiness which impacts governmental strategies and public accountability. The findings reveal critical insights into the underlying assumptions and biases in the HM, particularly how socio-technical systems shape user experience and influence perceptions of well-being. By employing personas, especially “anti-personas”, the study exposes civic normativity as mechanisms of exclusions and inequality. This study aims to contribute to discussions on digital governance’s role in shaping societal perceptions of well-being, highlighting the need for algorithmic accountability, transparency and inclusivity in algorithmic e-governmental infrastructures.