This research intends to draw implications for digital governance establishment by analyzing how the president’s leadership has worked in the digital government innovation promoted in South Korea over the past 30 years. This research examines the process of digital government development in South Korea from the 1990s onwards, chronologically and in the order of presidential administrations. The analysis proceeds from the following three perspectives: the political characteristics of digital government, the presidents’ leadership on government innovation through digital government process, and the composition of a strong cross and joint governmental promotion system. The implementation of digital government is not simply about a computerized government. Driving digital government means the overall transformation of government. From this point of view, to successfully implement digital governance, we must approach it from a highly political perspective. The implementation of digital government in South Korea has been continuously pursued as a national agenda. Since South Korea has a well-established high-speed information and communication infrastructure, e-Government and digital government innovation have been promoted as national agendas regardless of regime change. However, in this process, the president’s leadership determined the success or failure of digital government innovation. Therefore, the most important success factor for digital government innovation is securing policy sustainability regardless of administration change.

The government plans to absorb conflicts caused by policy problems, have a goal orientation, and solve problems through policy implementation. This process is meaningful in that it can reduce the goal ambiguity of the organization and achieve results. In other words, goal setting and optimal alternative selection based on a comprehensive analysis of the situation can achieve government performance. The global achievements in e-government in South Korea and Denmark also follow this initiative. However, the shift from e-government to digital government requires a change in approach to planning. In other words, rather than planning a rational-comprehensive approach to find the optimal solution, it requires a social and political approach or an incrementalism approach with the participation of various stakeholders. This point of view is similar to the study of a regional innovation system that aims to innovate through a network of multiple participants. It is also consistent with the perspective of open innovation, which creates knowledge through internal and external exchanges and breaks down organizational boundaries.

In recent years, Artificial intelligence products and services have been offered potential users as pilots. The acceptance intention towards artificial intelligence is greatly influenced by the experience with current AI products and services, expectations for AI, and past experiences with ICT technology. This study aims to explore the factors that impact AI acceptance intention and understand the process of its formation. The analysis results of this study reveal that AI experience and past ICT experience affect AI acceptance intention in two ways. Through the direct path, higher AI experience and ICT experience are associated with a greater intention to accept AI. Additionally, there is an indirect path where AI experience and ICT experience contribute to increased expectations for AI, and these expectations, in turn, elevate acceptance intention. Based on the findings, several recommendations are suggested for companies and public organizations planning to implement artificial intelligence in the future. It is crucial to manage the user experience of ICT services and pilot AI products and services to deliver positive experiences. It is essential to provide potential AI users with specific information about the features and benefits of AI products and services. This will enable them to develop realistic expectations regarding AI technology.

The purpose of this study is to develop a model to measure the level of informatization of local governments in South Korea. As digital technology advances, the policy environment is changing. However, advances in digital technology do not have the same effect on all local governments. Instead, the possibility of new gaps in different regions has raised a new problem depending on the level of digital technology acceptance and capabilities. However, objective methods of measuring local informatization levels are still insufficient. The purpose of this study is to fill these gaps and develop a diagnostic model that can identify different levels of local informatization. This study aims to evaluate the performance of informatization from a holistic perspective that encompasses providers, users, and services. First, the provider sector includes Information and Communications Technology (ICT)-related organizations, people, budgets, local informatization networks, and institutions from the perspective of informatization input or infrastructure. Second, the service sector consists of essential services and provisions provided by local governments for citizens and internal users, and the efforts to utilize and apply new technologies related to the Fourth Industrial Revolution from the perspective of informatization projects. Third, the user sector considers not only functional performance, such as the utilization rate related to local informatization, but also reaction, satisfaction, performance, and participation of citizens and civil servants as users. The index developed in this study has the following two purposes. First, it was applied to the evaluation of 245 local governments—17 regional and 228 basic local governments—in South Korea to determine the level of informatization of each local government. Second, the analysis of each sub-indicator can then be used to diagnose the problems of each local government's informatization policy and develop improvement measures. The development of the indicators in this study is as follows. First, the Delphi method was conducted with 18 participants, including experts, to ensure the validity of the indicators. Through this, the sub-indicators and indicator setting of four areas, including the supplier and infrastructure sectors, service and delivery system sectors, performance, and consumer sectors, and the overall indicators were verified. Second, the Analytic Hierarchy Process was conducted to verify the validity of the developed index model and synthesize individual indicators. This determined the importance of the substantial categories and sub-indicators of each significant category that were identified. The model was divided into three major categories and 11 middle categories, and the weights of the indicators were separately organized by regional and basic local governments.

Decarbonization and climate resilience are accelerating the digitalization of energy systems, expanding the use of AI-enabled and automated decision-making (ADM) in utility governance. Smart meters, dynamic tariffs, demand response, fraud detection, and automated eligibility screening for energy assistance or retrofit subsidies increasingly shift discretion from frontline caseworkers and customer-service staff to modelers, vendors, and code—an emerging form of algorithmic energy bureaucracy. Yet citizen acceptance of algorithmic decisions remains volatile, particularly when climate-motivated interventions impose immediate burdens (e.g., remote disconnection, peak-time restrictions, or load curtailment during heatwaves). Vignette experiments are well-suited to identify causal determinants of acceptance. Still, many designs either oversimplify energy contexts—erasing distributive and dignity concerns central to the “just transition”—or overcomplicate scenarios, undermining internal validity.Building on the conceptual tension between thin, standardized algorithmic rules and thick, context-dependent governance, and on procedural justice theory, this article proposes a parsimonious vignette architecture that preserves the normative thickness of energy governance while enabling clean causal inference. We argue that minimal, theoretically grounded manipulations should isolate: (1) decision locus (human vs algorithmic vs hybrid), (2) context sensitivity and exception handling (e.g., medical device reliance, extreme weather vulnerability), (3) transparency as accessibility (disclosure) versus explainability (comprehensible rationale), (4) opportunities for voice and appeal, and (5) climate-and-equity framing (emissions reduction and grid stability benefits versus bill impacts and hardship risk).An illustrative high-stakes scenario—smart-meter–triggered remote electricity disconnection or automated peak curtailment targeting households flagged as “high-risk” for arrears—demonstrates how simplification can retain climate-policy relevance without conflating “algorithmic” with “opaque,” “inflexible,” or “unaccountable.” The framework yields testable hypotheses about when climate-benefit narratives fail to compensate for losses in contextual legitimacy, and how explainable justifications and meaningful recourse can strengthen contextual legitimacy in the eyes of citizens.