Abstract Whistleblowing policies are seldom effective in inducing civil servants to report misconduct. While current literature focusses more upon the identification of the chief factors that prevent witnesses from reporting, it overlooks potentially effective strategies to stimulate active behavior. In particular, it neglects the framing and impact of information provision. According to the prospect theory, information that frames the consequences of non‐reporting as negative is more effective in enhancing the intention to report misconduct, as opposed to information that frames the consequences of reporting as positive. This study tested these propositions through an online survey experiment targeted at the civil servants of a major European city. We exposed participants to four different frames of economic and psychological consequences of reporting wrongdoing, in order to analyze the impact of various frames upon participants' reporting intentions. The results of this study confirm the relevance of the prospect theory and clearly indicate how the presentation of information affects active behavior.
Targeted transparency has become an essential tool for regulation. Through information disclosure, regulatory agencies try to get regulated companies to improve their practices and comply with regulations. We investigate the effect of targeted transparency on citizen trust through a large-scale representative survey experiment (n = 5303). We used 12 transparency frames in three regulated domains in the Netherlands (consumer rights, healthcare safety, and nuclear plant safety).
Abstract Open government is an important innovation to foster trustworthy and inclusive governments. The authors develop and test an integrative theoretical framework drawing from theories on policy diffusion and innovation adoption. Based on this, they investigate how structural, cultural, and environmental variables explain three dimensions of open government: accessibility, transparency, and participation. The framework is tested by combining 2014 survey data and observational data from 500 local U.S. government websites. Organizational structure, including technological and organizational capacity, is a determinant shared by all dimensions of open government. Furthermore, accessibility is affected by a mixture of an innovative and participative culture and external pressures. A flexible and innovative culture positively relates to higher levels of transparency, whereas capacity is a strong predictor of adopting participatory features. The main conclusion is that there is no one‐size‐fits‐all solution to fostering the three dimensions of open government, as each dimension is subject to a unique combination of determinants .
La cantidad de datos difundidos en plataformas por las administraciones publicas se ha disparado en los ultimos anos en todo el mundo. Estas plataformas de datos gubernamentales abiertos pretenden mejorar la transparencia y la participacion. Aunque estas plataformas son prometedoras, aun no han alcanzado todo su potencial. Los investigadores han identificado barreras tecnicas y cualitativas para el uso de los datos abiertos. Aunque son interesantes, estas cuestiones no tienen en cuenta el hecho de que el significado de los datos abiertos tambien depende del contexto y de las personas implicadas. En este estudio analizamos el uso de los datos abiertos desde una perspectiva practica –como un constructo social que surge a lo largo del tiempo en interaccion con los Gobiernos y los usuarios en un contexto determinado– para comprender mejor el papel del contexto y de la representacion en el desarrollo de las plataformas de datos abiertos. Este estudio se basa en una innovadora investigacion basada en la accion en la que funcionarios y ciudadanos colaboran en iniciativas para encontrar soluciones a problemas publicos a traves de una plataforma de datos abiertos. Nuestro estudio propone analizar los trabajos realizados en el ambito de los datos abiertos desde un punto de vista interno. Nuestras observaciones indican que la falta de un marco cognitivo compartido para interpretar los datos abiertos, asi como la carencia de conjuntos de datos de calidad, pueden obstaculizar los procesos de aprendizaje colaborativo. Nuestro enfoque contextual subraya la necesidad de contar con practicas en materia de datos abiertos basadas en interacciones enriquecedoreas con los usuarios en lugar de en aplicaciones centradas en el gobierno.
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Can a government agency mitigate the negative effect of "bad new" on public trust? To answer this question, we carried out a baseline survey to measure public trust five days before a major press release involving bad news about an error committed by an independent regulatory agency in the Netherlands. Two days after the agency's press release, we carried out a survey experiment to test the effects on public trust of the press release itself as well as related newspaper articles. Results show that the press release had no negative effect on trustworthiness, which may be because the press release "steals thunder" (i.e. breaks the bad news before the news media discovered it) and focuses on a "rebuilding strategy" (i.e. offering apologies and focusing on future improvements). In contrast, the news articles mainly focused on what went wrong, which affected the competence dimension of trust but not the other dimensions (benevolence and integrity). We conclude that strategic communication by an agency can break negative news to people without necessarily breaking trust in that agency. And although effects of negative news coverage on trustworthiness were observed, the magnitude of these effects should not be overstated.
Abstract Artificial Intelligence is increasingly used to support and improve street‐level decision‐making, but empirical evidence on how street‐level bureaucrats' work is affected by AI technologies is scarce. We investigate how AI recommendations affect street‐level bureaucrats' decision‐making and if explainable AI increases trust in such recommendations. We experimentally tested a realistic mock predictive policing system in a sample of Dutch police officers using a 2 × 2 factorial design. We found that police officers trust and follow AI recommendations that are congruent with their intuitive professional judgment. We found no effect of explanations on trust in AI recommendations. We conclude that police officers do not blindly trust AI technologies, but follow AI recommendations that confirm what they already thought. This highlights the potential of street‐level discretion in correcting faulty AI recommendations on the one hand, but, on the other hand, poses serious limits to the hope that fair AI systems can correct human biases.
