Government Information Quarterly最新文献

While widely recognized as a potent instrument for improving public service delivery, enhancing e-government performance remains challenging. This study employs necessary condition analysis (NCA) to identify “bottleneck” causes (NCs) for e-government performance. Drawing from the Resource-Based View and Institutional Theory, we categorize nine potential NCs within a supply-and-demand framework. High levels of GDP per capita, digital infrastructures, managerial innovativeness, and urbanization are prerequisites for attaining high levels of e-government performance across China's 31 provinces. To support as many provinces as possible, national policymakers should prioritize limited resources to tackle two “relatively important” barriers: digital infrastructures and managerial innovativeness. Individual provinces can focus on addressing specific critical constraints that hinder the desired e-government performance levels.

This study is grounded in the understanding that the potential of digital technologies contributes to digital transformation and public value creation, primarily by enhancing information sharing and cooperation. Empirical analysis of policy reports on digital transformation has enabled key topics to be identified, revealing their linkages to core initiatives associated with public values. Through this examination, we underscore the significance of strategic support within digital transformation policies, wherein (emerging) digital technology serves not only as an enabling infrastructure for public administration but also as a catalyst for progress. Leveraging these insights, we propose recommendations for policy groups and identify priority initiatives that can be useful for planning complex digital policies aimed at achieving digital transformation.

The rapid advancement of information and communication technologies is driving a swift transition towards digital transformation (DT) in public services globally. This paper investigates Brazil's approach to DT through its Startup Gov.br program, which employs startup principles to drive digital innovation within federal government agencies. Drawing upon interviews with key stakeholders and analysis of publicly available documents, the study reveals the program's characteristics and its integration into the current IT governance structure of the federal government. The results indicate that the program effectively addresses capacity constraints within government agencies, fostering strategic projects and long-term cultural change. By examining this novel approach, the paper contributes to the literature on startups in the public sector and offers insights into how governments can leverage innovative strategies for effective digital transformation.

This paper introduces systems theory and system safety concepts to ongoing academic debates about the safety of Machine Learning (ML) systems in the public sector. In particular, we analyze the risk factors of ML systems and their respective institutional context, which impact the ability to control such systems. We use interview data to abductively show what risk factors of such systems are present in public professionals' perceptions and what factors are expected based on systems theory but are missing. Based on the hypothesis that ML systems are best addressed with a systems theory lens, we argue that the missing factors deserve greater attention in ongoing efforts to address ML systems safety. These factors include the explication of safety goals and constraints, the inclusion of systemic factors in system design, the development of safety control structures, and the tendency of ML systems to migrate towards higher risk. Our observations support the hypothesis that ML systems can be best regarded through a systems theory lens. Therefore, we conclude that system safety concepts can be useful aids for policymakers who aim to improve ML system safety.