This study aims to propose several recommendations for adopting blockchain to government procurement and for solving corruption. The results of the study can be summarized as: readiness of blockchain technology can prevent corruption and the level of readiness of blockchain adoption for government procurement and for solving corruption is low. The study proposes developing a blockchain system and a smart contract system in the selection of committees to enhance transparency via online grading both in the selection procedure and procurement projects, to undergo online e-government procurement training, and to develop an online grading system for the committees.

This study uses “Metaverse Seoul” as a case and focuses on Seoul residents. It explores the key factors influencing user immersion in South Korea's metaverse-based government service platforms. The study is based on immersion theory and expectation-confirmation theory. It constructs a theoretical model of “virtual-physical interaction” to show the feedback loop between virtual participation, emotional response, and real-world policy. The findings show that social presence has the greatest impact on user immersion in South Korea's metaverse government services. This is followed by perceived procedural justice, civic task gamification, and AI-driven civic interaction. Structural equation modeling analysis also shows that civic task gamification, accessibility and inclusivity, and virtual-physical policy linkage influence user immersion indirectly through expectation confirmation.

This study examines how public expectations and emotional perception affect user satisfaction with Seoul Talk, a government chatbots launched by the Seoul Government as part of its digital public service strategy. Based on survey data from residents of Seoul who have interacted with the chatbots, the study employs grouped and quantile regression analyses to investigate differences across various social groups. The results show that higher public expectations lead to greater satisfaction, particularly when users perceive positive emotional responses from the chatbots. However, the influence of emotional perception varies among different occupational and gender groups, with some users showing less positive or even negative reactions. Furthermore, the positive effect of expectations and emotional perception on satisfaction is most evident among users with lower initial satisfaction levels. The findings suggest that tailoring chatbots interactions to better reflect citizens' expectations and emotional needs can enhance public satisfaction and trust in digital government services.

As digital government mental health services expand, understanding how they foster sustained user engagement is increasingly important. This study investigates the effects of key service features—perceived intelligent adaptability, data security trust, perceived empowerment for self-help, and crisis-time instant reliance—on college students' digital attachment behavior, using data from the “Zheli Xinqing” public mental health platform. Emotional sense of belonging and emotional self-regulation efficacy are tested as mediators. Based on a survey of 298 college students in Hangzhou, hierarchical regression and bootstrap analyses reveal that all four service features significantly predict digital attachment behavior. Emotional sense of belonging mediates several of these relationships, while emotional self-regulation efficacy does not. These findings highlight the central role of emotional belonging in enhancing attachment to digital government mental health services, offering insights for more effective public platform design.

This study examines how government structural assurances (GSA) can enhance the effectivenes and acceptance of metaverse e-government services (METAEGS) among citizens. It extends innovation diffusion theory (IDT) by integrating GSA to investigate intentions to adopt METAEGS in China. Structural equation modeling was applied to 457 valid responses. The results show that attitudes toward METAEGS are positively influenced by key IDT constructs, including relative advantage, compatibility, lower complexity, trialability, and observability. Positive attitudes, in turn, significantly influence intentions to adopt these services. GSA positively moderates the relationship between attitude and intention to adopt METAEGS. GSA also significantly moderates the effects of relative advantage, observability, and compatibility on attitudes toward METAEGS. However, GSA does not moderate the effects of lower complexity and trialability on attitudes toward METAEGS. The academic and practical implications of these findings are discussed.

Governments are often not ready to implement AI; however, limited research has examined specific factors influencing their readiness, and models often remain at a high level of abstraction. This study aims to identify the factors influencing governments' readiness to implement predictive AI as perceived by the involved stakeholders. Deep insight was gained from a case study in a Dutch government that has been implementing predictive AI for two years. The AI readiness was analyzed based on factors of data, technology, people, organization, environment, and value creation. This study reveals that AI developers and AI users perceive diverse perspectives on the influencing factors to the organization's readiness. The AI developers highlight the importance of a dedicated team, sufficient computing power, and strong support from top management, while the AI users spotlight the importance of perceived value and trust in the AI system. This research also found that factors at the organizational and individual levels shape the organization's AI readiness.

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.

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.