This study focuses on the impact of technological changes, particularly the development of generative artificial intelligence, on government–citizen interactions in the context of government services. From a psychological perspective with an emphasis on technological governance theory and emotional contagion theory, it examines public perceptions of the simulated emotions of governmental chatbots and investigates the moderating role of age. Data were collected through a multi-stage stratified purposive sampling method, yielding 194 valid responses from an original distribution of 300 experimental questionnaires between 24 September and 13 October 2023. The findings reveal that public expectations significantly enhance the simulated emotional perception of chatbots, with this effect being stronger among older individuals. Age shows significant main and interaction effects, indicating that different age groups perceive the simulated emotional capabilities of chatbots differently. This study highlights the transformative impact of generative artificial intelligence on government–citizen interactions and the importance of integrating AI technology into government services. It calls for governments to pay attention to public perceptions of the simulated emotions of governmental chatbots to enhance public experience.

This study investigates the relationship between public experience and sense of gain after using AI-driven governmental chatbots for public services, with a focus on two user groups: policy-oriented users and practical knowledge users. Governmental chatbots, as a significant tool in digital public service delivery, provide convenient interactive services that have the potential to enhance the public's sense of gain, particularly within the public administration and public policy domains. The innovation of this study lies in analyzing how public experience influences the sense of gain through internal mechanisms, and comparing these effects across the two user groups. Using mediation analysis and regression models, the results indicate that while both policy-oriented and practical knowledge users benefit from enhanced public experience, the sense of gain for practical knowledge users increases more significantly, especially when considering internal political efficacy.

This study examine the roles of internal and collective political efficacy across different forms of political participation, with a focus on how generative AI, used as a digital engagement tool by governments, influences citizens' political interest, knowledge, efficacy, and participation. The findings reveal that internal political efficacy significantly promotes political participation, especially non-institutionalized participation, while collective political efficacy plays a more limited role. The government's use of generative AI improves access to policy information and political participation opportunities. However, its impact on political efficacy depends on whether online engagement leads to real policy responsiveness. Policy responsiveness plays a key role in enhancing efficacy and sustained participation. This study shows that political efficacy is influenced by both individual cognition and external factors, highlighting the need for digital government strategies to combine innovation with effective policy feedback to better engage citizens.

This study examines the determinants of institutional political participation among street-level bureaucrats in China within the context of digital government transformation, guided by an integrated theoretical framework combining Resource Mobilization Theory and Political Opportunity Structure Theory. These theories help explain how both the availability of resources and the broader institutional opportunities influence participation behaviors. Using CGSS 2021 data, which employs a stratified multi-stage probability sampling method to ensure nationwide representativeness, the research assesses how sociodemographic, socioeconomic, and digital factors impact voting behavior and broader political engagement among bureaucrats. The study highlights that digital participation and digital skills significantly increase the likelihood of institutional engagement, particularly among younger bureaucrats. In contrast, those with higher socioeconomic status are less active in traditional voting but more engaged through digital channels.

Chapter 8 examines the ethical foundations of trusted AI enabled digital government by extending the discussion beyond algorithmic fairness toward human centered AI governance. It argues that AI systems are not neutral technical tools but value embedded governance mechanisms that influence public decisions and social outcomes. The chapter explores the ethical boundaries of AI authority, the reconstruction of accountability, and the importance of meaningful explainability in maintaining public trust. Furthermore, it introduces emotional intelligence as a new dimension of AI governance, emphasizing that trustworthy AI requires not only technical reliability but also ethical legitimacy, human understanding, and respect for citizen experiences.

Chapter 2 explores the multidimensional nature of trust in AI enabled digital government. It argues that trust is no longer limited to citizens' confidence in government institutions but has expanded to include digital systems, AI mediated governance, governance processes, and citizen government relationships. The chapter distinguishes different forms of trust and explains how each contributes to the legitimacy and effectiveness of digital governance. Furthermore, it examines how AI reshapes governance power through the emergence of algorithmic power, redistributing influence among governments, data providers, technology developers, and algorithmic systems. This transformation creates new challenges regarding accountability, transparency, and public control. The chapter concludes that Trusted AI for Effective Digital Government requires not only reliable technologies but also accountable institutions, fair governance processes, and meaningful citizen trust.