The decision-making by citizens: Evaluating the effects of rule-driven and learning-driven automated responders on citizen-initiated contact

Abstract

While many studies have investigated the impact of artificial intelligence (AI) deployment in the public sector on government-citizen interactions, findings remain controversial due to the technical complexity and contextual diversity. This study distinguishes between rule-driven and learning-driven AI and explores their impact as automated respondents on citizen-initiated contact, an important scenario for public participation with initiative. Based on a conjoint experiment with 763 participations (4578 observations), this study suggests that AI deployments enormously reduce the likelihood of citizen-initiated contact compared to human response, with learning-driven AI having a higher negative effect than rule-driven AI. In addition, the causal effects of respondent image, contact channel, contact purpose, and matter attributes on citizen-initiated contact, as well as their moderating effects, are explored. These findings make theoretical implications and calls for public participation in the roaring AI deployment in the public sector.