While there has been growth in the literature exploring the governance of artificial intelligence (AI) and recognition of the critical importance of guiding public values, the literature lacks a systematic study focusing on public values as well as the governance challenges and solutions to advance these values. This article conducts a systematic literature review of the relationships between the public sector AI and public values to identify the impacts on public values and the governance challenges and solutions. It further explores the perspectives of U.S. government employees on AI governance and public values via a national survey. The results suggest the need for a broad inclusion of diverse public values, the salience of transparency regarding several governance challenges, and the importance of stakeholder participation and collaboration as governance solutions. This article also explores and reports the nuances in these results and their practical implications.

E-government has been touted by many as a technological answer to improve citizen participation, government accountability, and transparency by facilitating a greater level of communication and flow of public information between citizens and the government. This article examines how political environment, government structure, and the nature of individual e-government applications influence the likelihood of adoption. Using data obtained from multiple sources, logistic regressions are conducted on a sample of six e-government applications that possess varying degrees of communicative and organizational impacts on the government to observe how different factors influence their adoption. Findings include a general disinclination for adopting e-government applications with high communicative impact; however, such disinclination dissipated when there was a high level of political competition in the area and perceived demand for online communication; active traditional channels of political communication, such as political parties and accessibility to local council members, reduced the likelihood of adoption; the preferences of the elected mayors coincided with the perceptions of nonelected officials who favor e-government applications that would reduce the workload while disfavoring applications that would increase it.

This study aims to address strategies, models, and the motivation behind smart cities by analyzing two smart city project cases in medium-sized cities, i.e., Gimpo and Namyangju in South Korea. The case of Smartopia Gimpo represents a top-down, infrastructure-focused smart city innovation that invested in building state-of-the-art big data infrastructure for crime prevention, traffic alleviation, environmental preservation, and disaster management. On the other hand, Namyangju 4.0 represents a strategy focused on internal process innovation through extensive employee training and education regarding smart city concepts and emphasizing data-driven (rather than infrastructure-driven) policy decision making. This study explores two smart city strategies and how they resulted in distinctively different outcomes. We found that instilling a culture of innovation through the training of government managers and frontline workers is a critical component in achieving a holistic and sustainable smart city transformation that can survive leadership changes.

Artificial Intelligence has emerged as a transformative force in public service delivery, promising improved efficiency and responsiveness to citizens’ needs, so it is essential to understand the functional factors that influence citizens’ adoption and intention to continue using such services. Drawing on the technology acceptance model, this study investigates the influence of six functional factors, namely, usefulness, ease of use, service reliability, service quality, responsiveness, and security, on the continued use of AI-enabled public services through the mediating effect of user satisfaction. Data were collected from an online survey of AI-enabled public services in Korea during the summer of 2021, and causal mediation analysis was conducted to examine these relationships. The results indicate that usefulness, service reliability, and security significantly influenced users’ intent to continue using AI-based services. Furthermore, causal mediation analysis confirmed that the five components of AI public services—usefulness, service reliability, service quality, responsiveness, and security—had significant effects on the continued use of AI-enabled service platforms, with user satisfaction playing a mediating role in the relationships. The main functional factors can lead to higher levels of satisfaction, and this ultimately drives the sustained adoption and continued use of AI-enabled public services by citizens.