Government Information Quarterly最新文献

Governments implement transparency policies aiming to enhance citizens' trust, but the outcomes of these efforts are complex and not fully understood. Existing literature often focuses on how citizens consume government information, but tends to overlook the impact of the act of disclosure itself. This study seeks to fill this gap by examining citizens' responses to the act of government disclosure, independent of the content disclosed. Through a factorial survey experiment, the research manipulates motivations, types, and levels of information disclosure across two distinct organizations to assess their impact on public trust. The findings reveal that the act of disclosure generally enhances trust perceptions, but this effect is significantly modulated by individuals' pre-existing attitudes toward transparency. Those indifferent about transparency do not show increased trust, while for those who value transparency, trust increases notably. The study also finds that the disclosure of performance information, particularly when combined with financial information, has the most substantial impact on trust. Moreover, public trust is higher when organizations disclose more information than legally required. These results contribute to a more nuanced understanding of how government transparency influences public trust and highlight the importance of considering citizen attitudes in transparency initiatives.

Using an embedded mixed method design, we compared a nationally representative sample of US adults and a sample of US-based emergency managers (EM) on their attitudes toward artificial intelligence (AI) and their intentions to rely on AI in a set of decision-making scenarios relevant to emergency management. Emergency managers reported significantly less positive attitudes toward AI and were less likely to rely on AI for decisions compared to the nationally representative sample. Our analysis of EMs' open-ended responses explaining their choices to use or not use AI-based solutions reflected specific concerns about implementation rather than wariness toward AI generally. These concerns included the complexity of the potential outcomes in the scenarios, the value they placed on human input and their own extensive experience, procedural concerns, collaborative decision-making, team-building, training, and the ethical implications of decisions, rather than a rejection of AI more generally. Managers' insights integrated with our quantitative findings led to a person-environment fit framework for AI implementation in the public sector. Our findings and framework have implications for how AI systems should be introduced and integrated in emergency managerial contexts and in public sector organizations more generally. Public managers' perceptions and intentions to use AI and organizational oversight processes are at least as important as technology design considerations when public sector organizations are considering the deployment of AI.

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 public sector is actively pursuing digital transformation to ensure continuous operations and relevance. While existing research has outlined essential prerequisites for successful digital transformation, there is recognition of willful ignorance concerning these prerequisites. Public servants may in other words deliberately avoid understanding the necessary conditions for digital transformation, often driven by strategic motives such as evading responsibility and/or accountability. The phenomenon of willful ignorance constitutes an important yet under-researched area within the study of digital government. To close this gap, we investigate the latent factors of willful ignorance in public sector digital transformation, utilizing three sets of national panel data focused on digital transformation prerequisites. Employing exploratory factor analysis on an initial sample, we construct a factor model, subsequently assessing its validity through confirmatory factor analysis on two additional samples. Our research identifies and validates latent factors associated with willful ignorance in the digital transformation of the public sector. Building on these findings, we propose a mid-range variance theory termed “digital transformation decoupling”. By integrating this theory with existing knowledge, we present a set of propositions to guide future research in the realm of public sector digital transformation.

The high expenditure on public procurement by governments makes it imperative to enhance transparency across the procurement cycle with technology-driven initiatives, such as e-procurement systems. This paper develops the Public Procurement Transparency Index and evaluates the impact of institutional reforms and membership of the World Trade Organisation Government Procurement Agreement on transparency. We use the Technology-Organisation-Environment and Balanced e-Participation Index frameworks to analyse transparency in procurement. The Public Procurement Transparency Index uses generalised least squares technique to develop the country-level transparency score. Findings indicate that e-government procurement systems promote transparency, especially in countries with robust institutional frameworks. Further, with fractional probit regression techniques we find institutional quality and infrastructure are key determinants of transparency in public procurement. The results also highlight the importance of information technology and institutional reforms as a means to enhance transparency and accountability in public procurement and offers valuable implications for policymakers.

Government chatbots are naturally imparted the identity of civil service agents, because they replace human civil servants in providing services on the frontline. However, little is known about whether government chatbots should present key characteristics consistent with their identity. Based on Computers Are Social Actors (CASA) theory and the stereotype content model (SCM), this study explored the effect of various identity characteristics on citizens' experiences in Chinese government chatbots. Valid data were obtained from 705 respondents and analysed using structural equation model, we found that manners, proactivity, conscientiousness, fairness, and professionalisation significantly influence the citizens' experiences, and that task-oriented language style has a negative effect. These effects are partly mediated by perceptions of warmth and competence. These findings contribute theoretically to the literature on chatbot identity and provide implications for incorporating chatbots into the design of e-government context.

User-centricity in e-government is a double-edged sword. While it helps governments design digital services tailored to the needs of citizens, it may also increase the burden on users and deepen the digital divide. From an institutional perspective, these fundamental conflicts are inevitable. To better understand the role and effect of user-centricity in e-government, this paper analyses academic literature on user-centricity and public values. The analysis leads to three main insights: First, there is a conflict in citizen representation that may result from the normative dominance of decision-makers. Second, we identify an accountability conflict that can prevent user-centric innovation from thriving in a highly institutionalized environment. Third, we identify a pluralism conflict that emerges from a clash between the reality of a diverse society and the assumed homogeneity of actors. The need to address these conflicts increases with rapid technological innovation, such as distributed ledger technologies, artificial intelligence, and trust infrastructures. These technologies put the user at the center stage and permeate aspects of social life beyond government. In response to these insights, we outline suggestions for further research and practice.

Open government data initiatives are important agents of public sector digital transformation and understanding how agencies design, implement, and evaluate strategies for these initiatives is paramount to their ongoing success. However, a challenging and little understood aspect of open government data initiatives is precisely how open data users engage with and use these vital resources. This study examines the extent of open dataset use among early adopters using a cross-sectional analysis of web analytic behavioral data. We quantify early users' extent of use of open datasets from Health Data NY, the first state-level open health data platform. Results of a PLS-SEM analysis provide new empirical evidence that prior, or initial levels of open dataset use positively influence the later extent of open dataset use. Further, prior open government data use influences not only the later variety of open data activities, but also their depth of sophistication. Implications for practice include open data strategies to facilitate deeper, more extensive use of open dataset resources that lead to increased value creation, and ultimately, more effective digital transformation. Theoretically, this study contributes to the body of research on the Unified Theory of Acceptance and Use of Technology (UTAUT) by empirically testing its Use Behavior construct in light of early open data users' extent of use of open datasets, thus providing a more refined understanding of open data use behavior.

Despite the increasing use of digital tools for citizen participation, their ecosystem and functionality remain underexplored. What digital tools exist, and how do they help citizens engage in policymaking? This article addresses this gap by examining the supply side of digital tools for citizen participation. We compiled a comprehensive dataset of 116 digital tools from three public repositories. Using the collective intelligence genome framework, adapted for the e-participation context, we systematically examined the dynamics and trends of these tools through cluster analyses. Our findings highlight the potential of digital participatory tools to facilitate the flow of information from citizens to governments using advanced technologies. However, a prominent deficiency was identified in disseminating accountability information to citizens regarding how policy decisions are made, realised, and assessed. These findings offer valuable insights and notable gaps in the digital tool ecosystem.