Government Information Quarterly最新文献

In this paper, we propose a conceptual framework composed of a number of e-government, implementation and evaluation-oriented variables, with which we jointly analyze chatbots presented in the research literature and chatbots deployed as public services in Spain at national, regional and local levels. As a result of our holistic analysis, we identify and discuss current trends and challenges in the development and evaluation of chatbots in the public administration sector, such as focusing the use of the conversational agents on the search for government information, documents and services –leaving citizen consultation and collaboration aside–, and conducting preliminary evaluations of prototypes in limited studies, lacking experiments on deployed systems, with metrics beyond effectiveness and usability –e.g., metrics related to the generation of public values. Addressing some of the identified challenges, we build and evaluate two novel chatbots that present advances in the access to open government data and citizen participation content. Moreover, we come up with additional, potential research lines that may be considered in the future for a new generation of e-government chatbots.

This research analyzes the possible underlying determinants of citizens' intent to use electronic services provided by e-government (e-gov). A total of 519 responses and a theoretical model based on Unified Model of Electronic Government Adoption (UMEGA) including the role of e-gov in the context of the COVID-19 pandemic, were used. A partial least squares structural equation model and the fs-QCA technique were used to analyze the data collected. The results showed how two factors studied, Attitude and Facilitating Conditions directly affected the decision to use e-gov services. Attitude means that governments must find effective ways to entice positive attitudes toward using e-services, and Facilitating Conditions implies that the resources and facilities provided to users improve their intention to use. However, the second most robust relationship was associated with the influence of Performance Expectancy on Attitude, suggesting that the trust placed in e-gov by the citizens, based on previous experiences, is a crucial determinant of their attitude toward it and their behavior when using it.

This study examines the impact of ethical AI information on citizens' trust in and policy support for governmental AI projects. Unlike previous work on direct users of AI, this study focuses on the general public. Two online survey experiments presented participants with information on six types of ethical AI measures: legal compliance, ethics-by-design measures, data-gathering limitations, human-in-the-loop, non-discrimination, and technical robustness. Results reveal that general ethical AI information has little to no effect on trust, perceived trustworthiness or policy support among citizens. Prior attitudes and experiences, including privacy concerns, trust in government, and trust in AI, instead form good predictors. These findings suggest that short-term communication efforts on ethical AI practices have minimal impact. The findings suggest that a more long-term, comprehensive approach is necessary to building trust in governmental AI projects, addressing citizens' underlying concerns and experiences. As governments' use of AI becomes more ubiquitous, understanding citizen responses is crucial for fostering trust, perceived trustworthiness and policy support for AI-based policies and initiatives.

We empirically investigate the effect of electronic case-processing on court efficacy. We draw on monthly court-level panel data on adjudication and enforcement in Brazilian labor justice, a major pillar of the Brazilian justice system where electronic case-processing is a recent phenomenon and court inefficacy has been a pervasive concern. Using dynamic panel methods and multiple estimation approaches to address endogeneity, we show, first and foremost, that in both adjudication and enforcement a shift to electronic case-processing unequivocally increases judicial productivity and court clearance rate while reducing case disposition times. In adjudication, electronification exhibits diminishing marginal returns: additional electronification does not yield further efficacy gains once the share of electronically-processed court caseload is between 50% and 75%. We do not find similarly stark evidence of plateauing of the effect of electronification in enforcement, a key court activity domain where attaining fully electronic case-processing would thus be especially advantageous. Overall, our findings suggest that electronic case-processing provides one viable path to unclogging courts and enhancing administration of justice.

Use of semi- and fully automated, administrative decision-making in public administration is increasing. Despite this increase, few studies have explicitly analysed its relation to good administration. Good administration is regulations and norms aimed at securing the correctness of administrative decisions as well as the legitimacy of these and is often associated with underlying values such as transparency, equality of treatment and accountability. Based on a thematic analysis of qualitative interviews with 43 key public administration stakeholders in a wide array of policy areas in Denmark, insiders of government machinery are shown to perceive relations between automated decision-making and good administration as manifold. Automated, administrative decision-making is articulated as providing both opportunities for supporting good administration and undermining good administration. Six values of good administration particularly related to automated, administrative decision-making are identified: Carefulness; Respecting-individual-rights; Professionalism; Trustworthiness; Responsiveness and Empowerment. Put simply, risks to good administration can be expected to occur if administrative bodies apply automated, administrative decision-making, while opportunities must be actively nurtured through managerial attention. Despite popular conceptions of the threat of “robotic government”, the conclusions of this study indicate a need for a more pragmatic view of relations of automated, administrative decision-making and good administration balanced between outright techno-optimism and techno-pessimism.

The use of drones offers police forces potential gains in efficiency and safety. However, their use may also harm public perception of the police if drones are refused. Therefore, police forces should consider the perception of bystanders and broader society to maximize drones' potential. This article examines the concerns expressed by members of the public during a field trial involving 52 test participants. Analysis of the group interviews suggests that their worries go beyond airspace safety and privacy, broadly dis-cussed in existing literature and regulations. The interpretation of the results indicates that the perceived justice of drone use is a significant factor in acceptance. Leveraging the concept of organizational justice and data collected, we propose a catalogue of guidelines for just operation of drones to supplement the existing policy. We present the organizational justice perspective as a framework to integrate the concerns of the public and bystanders into legal work. Finally, we discuss the relevance of justice for the legitimacy of the police's actions and provide implications for research and practice.

Urban Living Labs (ULLs) have been implemented in many cities, but their organizational and legal structure has not often been analyzed. ULLs aim to provide a space for different parties to research, develop, and test solutions to urban problems whilst engaging with local communities. Their experimental approach to urban innovation and to public-private collaboration makes flexibility, openness, and informality important. However, ULLs are also confronted with existing legal frameworks, responsibilities, and liabilities. Whilst they aim at shared decision-making and horizontality, they must navigate public and private interests, and interact with local government as well. To understand these dynamics, this article examines the legal and organizational structures of ULLs, the factors and trade-offs that influence it, and the role municipal government plays in in these structures. This article analyzes the different forms and trajectories of ULLs in practice, through semi-structured interviews held in four labs in Amsterdam. Through qualitative research, we found that 1) ULLs are partnerships that exist on a spectrum of formalization, from informal to highly formal; 2) the degree of formalization is influenced by financial, legal, and organizational factors that change over time; 3) each degree of formalization is associated to trade-offs, even if these trade-offs are not explicitly formulated by the people involved; 4) tensions arise from the municipality's double role as public authority and as partner. We conclude that ULLs could gain from clearly identifying the legal frameworks that condition their structure, actions, and future.

Street-level bureaucracies are digitalized with significant implications for street-level decision-making. Whereas research has focused on how street-level bureaucrats are influenced by this development, less research has focused on how the clients, who experience actual policy outcomes, perceive the increasing digitalization. This study explores clients' perceptions of automation in street-level decision-making. To do so, we have presented clients with three hypothetical case scenarios of street-level work with automated decision-making. Our findings suggest that clients are reluctant to automation in complex decision-making at the street level. However, those who trust technology the most are more in favor of automation. Individual differences were found in terms of gender and age. This study contributes, first, to the understanding of how clients perceive automation in street-level decision-making, and second, to practice by offering recommendations.

All policy implementation environments that transfer authority to field agents incur principal-agency problems. Given the difficulty of choosing the right agent (solving the “adverse selection” problem), leaders of agencies look for ways to reduce “moral hazard” when agents take actions against the public interest. Increasingly, leaders try to reduce moral hazard by monitoring employees using data collection technologies. Cameras and recorders are examples of sensors – devices for collecting and transmitting multiple modalities of physical phenomena. We focus on cameras and recorders as mixtures of modalities of monitoring using policing data in the United States. Policing agencies use different mechanisms such as training and policies to reduce moral hazard by officers. We show that those mechanisms are associated with the use of different sensor modalities – but not all mechanisms are associated with all modalities. We also show that statistical models should account for the use of mixtures of different technologies.

Various countries throughout the world have enacted transparency reforms, especially reforms oriented toward increasing access to government information (right to information, RTI). While researchers have published thorough examinations of the impact of such reforms, no study to date has surveyed how the successful exercise of this RTI affects institutional trust. Using a field experiment in Chile, Peru, and Uruguay to elicit information requests, we identify the effect of the successful exercise of RTI on individuals' trust in and perceptions of the transparency of institutions in these countries. Our findings indicate a need to differentiate between how the successful exercise of RTI affects individuals' trust in a specific institution— and especially their perception of its transparency—and how it affects trust in and perceived transparency of governmental institutions in general.