Government Information Quarterly最新文献

英文中文

State versus Technology: What drives trust in and usage of internet voting, institutional or technological trust? 国家与技术：是什么推动了对互联网投票的信任和使用，是机构的信任还是技术的信任？

IF 1 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2025-12-01 Epub Date: 2025-09-05 DOI: 10.1016/j.giq.2025.102068

Bogdan Romanov , David Duenas Cid , Peeter Leets

This study examines the combined influence of technological and institutional trust on citizens’ perceptions of and engagement with Internet voting, addressing gaps in the literature on digital governance and trust. While prior research often treats these trust dimensions separately, this article explores their interplay within the context of Estonia, which has utilized Internet voting for two decades. By constructing composite indices for technological and institutional trust through factor analysis, the study offers a novel methodological approach to operationalizing trust in digital governance (within the article, digital governance and e-governance are used interchangeably) research in general and Internet voting in particular, based on post-electoral survey data.

Applying linear and logistic regression analyses, the study explicitly examines how these trust dimensions affect citizens’ trust in Internet voting systems and their actual use of such technology. The findings reveal that institutional trust is significantly more influential than technological trust, consistently emerging as the primary driver for both trusting Internet voting and engaging in its usage. Technological trust, in contrast, demonstrates only marginal predictive strength, highlighting the greater importance citizens place on institutional legitimacy, transparency, and accountability. These results emphasize the compensatory nature of institutional trust, suggesting that robust institutional frameworks allow citizens to confidently engage with complex technological systems despite limited technical understanding. Consequently, this research enhances theoretical insights into trust dynamics within digital governance, particularly in contexts where political sensitivity and institutional credibility significantly impact technology adoption.

本研究考察了技术和制度信任对公民对互联网投票的看法和参与的综合影响，解决了数字治理和信任方面的文献空白。虽然先前的研究通常将这些信任维度分开对待，但本文在爱沙尼亚的背景下探讨了它们的相互作用，爱沙尼亚已经利用互联网投票二十年了。通过因子分析构建技术和制度信任的综合指数，本研究提供了一种基于选举后调查数据的数字治理（在本文中，数字治理和电子治理可互换使用）研究中的信任运作的新方法，特别是互联网投票研究。运用线性和逻辑回归分析，该研究明确考察了这些信任维度如何影响公民对互联网投票系统的信任以及他们对此类技术的实际使用。研究结果显示，制度信任比技术信任更有影响力，一直是信任互联网投票和参与互联网使用的主要驱动因素。相比之下，技术信任仅显示出微弱的预测力，凸显了公民对制度合法性、透明度和问责制的更大重视。这些结果强调了制度信任的补偿性，表明健全的制度框架使公民能够自信地参与复杂的技术系统，尽管技术理解有限。因此，本研究增强了对数字治理中信任动态的理论见解，特别是在政治敏感性和机构可信度显著影响技术采用的背景下。

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引用次数: 0

From disclosure to discrepancy: How open government data alters ESG rating divergence 从披露到差异：政府数据公开如何改变ESG评级差异

IF 1 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2025-12-01 Epub Date: 2025-10-18 DOI: 10.1016/j.giq.2025.102085

Jianhao Hu , Honghui Zou , Qian Wang

Given the impact of environmental, social, and governance (ESG) rating divergence on sustainable practices, its antecedents have garnered increasing attention. In the context of growing demands for transparency from investors and policymakers, the effect of open government data (OGD) policies on ESG rating divergence remains underexplored. To address this gap, this study examines the dynamic relationship between OGD policies and ESG rating divergence. Using panel data from Chinese listed firms and employing a difference-in-differences approach, the analysis reveals that OGD policies significantly exacerbate ESG rating divergence in the short term, with pronounced effects observed among firms subject to mandatory disclosure requirements and those with state ownership. However, over time, OGD policies reduce the ESG rating divergence. By offering a dynamic analysis, this research contributes to the literature on OGD policies and ESG assessment by underscoring the role of city-level policies in driving institutional change, thereby enhancing our understanding of ESG variability and public policy impacts.

鉴于环境、社会和治理（ESG）评级差异对可持续实践的影响，其前身已引起越来越多的关注。在投资者和政策制定者对透明度要求不断提高的背景下，政府公开数据（OGD）政策对ESG评级差异的影响仍未得到充分探讨。为了解决这一差距，本研究考察了OGD政策与ESG评级差异之间的动态关系。利用来自中国上市公司的面板数据，采用差异中的差异方法，分析发现，OGD政策在短期内显著加剧了ESG评级的差异，在强制披露要求的公司和国有公司之间的影响显著。然而，随着时间的推移，OGD政策减少了ESG评级的差异。通过提供动态分析，本研究通过强调城市层面政策在推动制度变革中的作用，从而加强我们对ESG变异性和公共政策影响的理解，为OGD政策和ESG评估的文献做出了贡献。

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引用次数: 0

Digital transformation leadership: A public value-centered measurement scale 数字化转型领导力：一个以公共价值为中心的衡量尺度

IF 1 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2025-12-01 Epub Date: 2025-11-24 DOI: 10.1016/j.giq.2025.102091

Henrico van Roekel , Martiene Branderhorst , Lars Tummers , Albert Meijer

We introduce a psychometrically validated scale to measure Digital Transformation Leadership in public organizations. As governments adopt new digital technologies to improve processes and services, understanding how managers effectively lead these transformations is essential. Although research on digital transformation and public sector leadership is growing, no measurement instrument grounded in a public value perspective currently exists. Following a preregistered analysis plan, we developed the scale through a rigorous multi-stage process of item development, scale construction, and evaluation. Our findings show that Digital Transformation Leadership comprises two dimensions. First, a Strategic dimension: how managers develop a vision, foster collaboration, and consider public preferences. Second, an Operational dimension: how managers guide employees, internal processes, and outputs. Additional analyses support the scale's validity, showing positive correlations with perceived leadership effectiveness, public leadership, work engagement, and job satisfaction. We further discuss how items may be used in shorter subscales or as templates. The scale offers a foundation for future research on the role of Digital Transformation Leadership in public organizations.

我们引入了一个心理计量学验证的量表来衡量公共组织的数字化转型领导力。随着政府采用新的数字技术来改善流程和服务，了解管理者如何有效地领导这些变革至关重要。尽管关于数字化转型和公共部门领导力的研究正在增长，但目前还没有基于公共价值视角的衡量工具。根据预先注册的分析计划，我们通过项目开发、量表构建和评估等严格的多阶段过程来开发量表。我们的研究结果表明，数字化转型领导包括两个维度。首先，战略维度：管理者如何制定愿景，促进合作，并考虑公众偏好。第二，运营维度：管理者如何指导员工、内部流程和产出。进一步的分析支持了该量表的有效性，显示出与感知领导效能、公共领导、工作投入和工作满意度呈正相关。我们进一步讨论如何在较短的子量表中使用项目或作为模板。该量表为未来研究数字化转型领导在公共组织中的作用提供了基础。

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引用次数: 0

Artificial Intelligence in deliberation: The AI penalty and the emergence of a new deliberative divide 审议中的人工智能：人工智能的惩罚和新的审议鸿沟的出现

IF 1 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2025-12-01 Epub Date: 2025-09-24 DOI: 10.1016/j.giq.2025.102079

Andreas Jungherr , Adrian Rauchfleisch

Advances in Artificial Intelligence (AI) promise help for democratic deliberation, such as processing information, moderating discussion, and fact-checking. But public views of AI's role remain underexplored. Given widespread skepticism, integrating AI into deliberative formats may lower trust and willingness to participate. We report a preregistered within-subjects survey experiment with a representative German sample (n = 1850) testing how information about AI-facilitated deliberation affects willingness to participate and expected quality. Respondents were randomly assigned to descriptions of identical deliberative tasks facilitated by either AI or humans, enabling causal identification of information effects. Results show a clear AI penalty: participants were less willing to engage in AI-facilitated deliberation and anticipated lower deliberative quality than for human-facilitated formats. The penalty shrank among respondents who perceived greater societal benefits of AI or tended to anthropomorphize it, but grew with higher assessments of AI risk. These findings indicate that AI-facilitated deliberation currently faces substantial public skepticism and may create a new “deliberative divide.” Unlike traditional participation gaps linked to education or demographics, this divide reflects attitudes toward AI. Efforts to realize AI's affordances should directly address these perceptions to offset the penalty and avoid discouraging participation or exacerbating participatory inequalities.

人工智能（AI）的进步有望帮助民主审议，如处理信息、主持讨论和事实核查。但公众对人工智能角色的看法仍未得到充分探讨。鉴于普遍存在的怀疑，将人工智能整合到审议形式中可能会降低信任和参与意愿。我们报告了一项预先注册的受试者内调查实验，其中有代表性的德国样本（n = 1850）测试了关于人工智能促进的审议的信息如何影响参与意愿和预期质量。受访者被随机分配到由人工智能或人类促成的相同审议任务的描述中，从而能够对信息效果进行因果识别。结果显示了一个明显的人工智能惩罚：参与者不太愿意参与人工智能促进的审议，并且预期审议质量低于人工促进的格式。在那些认为人工智能有更大的社会效益或倾向于将其拟人化的受访者中，惩罚减少了，但随着对人工智能风险的更高评估而增加。这些发现表明，人工智能促进的审议目前面临着公众的大量怀疑，并可能造成新的“审议鸿沟”。与传统的与教育或人口统计学相关的参与率差距不同，这种差距反映了人们对人工智能的态度。实现人工智能的能力的努力应该直接解决这些看法，以抵消惩罚，避免阻碍参与或加剧参与不平等。

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引用次数: 0

Complexity, understandability, and compatibility: A comparative study of AI advisory systems for National Security 复杂性、可理解性和兼容性：国家安全人工智能咨询系统的比较研究

IF 1 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2025-12-01 Epub Date: 2025-11-06 DOI: 10.1016/j.giq.2025.102088

Brecht Weerheijm, Sarah Giest, Bram Klievink

Artificial Intelligence (AI) advisory systems are being implemented in the public sector for more efficient and effective decision-making. Yet, there is a lack of in-depth qualitative and comparative research focusing on how decision-makers in a real-world setting use different types of AI advisory systems. By asking “How do different AI advisory systems affect use by national security decision-makers?”, this study reveals through a qualitative case study and using scenario-based interviews that decision-makers are more likely to use relatively simple AI systems over complex ‘black box’ systems. Additionally, factors such as accountability concerns and compatibility with existing decision-making processes influence their willingness to use AI advisory systems. Ultimately, a more technically advanced AI system is not necessarily perceived as more competent, as decision-makers view processes like data analysis as integral to nuanced and effective decision-making. This suggests that the fit between the perceived competences and compatibility of the AI system and the decision-making task at hand is highly important for the successful implementation of AI advisory systems.

人工智能（AI）咨询系统正在公共部门实施，以提高决策效率和效果。然而，缺乏深入的定性和比较研究，重点关注决策者在现实世界中如何使用不同类型的人工智能咨询系统。通过提问“不同的人工智能咨询系统如何影响国家安全决策者的使用？”，本研究通过定性案例研究和基于场景的访谈揭示，决策者更有可能使用相对简单的人工智能系统，而不是复杂的“黑匣子”系统。此外，问责问题和与现有决策过程的兼容性等因素影响了他们使用人工智能咨询系统的意愿。最终，技术更先进的人工智能系统并不一定会被认为更有能力，因为决策者认为数据分析等过程是微妙而有效决策的组成部分。这表明，人工智能系统的感知能力和兼容性与手头的决策任务之间的契合对于人工智能咨询系统的成功实施非常重要。

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引用次数: 0

Parallel learning loops in collaborative innovation: Insights from digital government 协同创新中的平行学习循环：来自数字政府的见解

IF 1 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2025-12-01 Epub Date: 2025-10-09 DOI: 10.1016/j.giq.2025.102080

Philipp Trein, Bastien Presset, Thenia Vagionaki

The implementation of digital innovations in the public sector—such as Electronic Health Records (EHRs)—requires decisionmakers to engage in learning processes. This article investigates how collective learning processes unfold in collaborative innovation, focusing on the development of Switzerland's national Electronic Health Record (EHR) system. Building on policy learning and collaborative governance literatures, we conceptualize learning as comprising two interdependent processes: policy-oriented learning (focused on technical effectiveness) and power-oriented learning (concerned with political feasibility). Drawing on 39 semi-structured interviews and extensive document analysis, we find that the EHR initiative followed a sequential learning pattern—technical solutions were developed before sufficient political support was secured—leading to a politically endorsed but technically flawed implementation. The study introduces the concept of parallel learning loops to explain how simultaneous engagement with technical and political dimensions can improve innovation outcomes. These findings advance theoretical understanding of collaborative learning in digital government and underscore the need for institutional designs that support concurrent technical and political deliberation in complex innovation processes.

在公共部门实施数字创新，如电子健康记录（EHRs），需要决策者参与学习过程。本文研究了集体学习过程如何在协作创新中展开，重点是瑞士国家电子健康记录（EHR）系统的发展。在政策学习和协作治理文献的基础上，我们将学习概念化为两个相互依存的过程：政策导向学习（关注技术有效性）和权力导向学习（关注政治可行性）。通过39次半结构化访谈和广泛的文件分析，我们发现电子病历倡议遵循了一个循序渐进的学习模式——在获得足够的政治支持之前就制定了技术解决方案——导致了政治上得到认可，但技术上存在缺陷的实施。该研究引入了平行学习循环的概念，以解释同时参与技术和政治维度如何改善创新成果。这些发现促进了对数字政府协作学习的理论理解，并强调了在复杂创新过程中支持技术和政治同步审议的制度设计的必要性。

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引用次数: 0

Bridging trust in AI and its adoption: The role of organizational support in AI chatbot implementation in korean government agencies 弥合对人工智能的信任及其采用：韩国政府机构在人工智能聊天机器人实施中的组织支持作用

IF 1 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2025-12-01 Epub Date: 2025-09-27 DOI: 10.1016/j.giq.2025.102081

Seongkyung Cho , Joon-Young Hur , Danee Kim

Amid rapid technological advancements, AI-chatbot integration into government workplaces represents a transformative shift to enhance communication, streamline administrative processes, and boost employee efficiency. Through a mixed-methods design combining survey data and employee interviews, this study analyzes how trust in AI chatbots influences employee utilization of chatbots in government organizations and examines organizational support's moderating role. By demonstrating the pivotal roles of trust and organizational support, this study emphasizes their combined effect on driving adoption and digital transformation within government agencies. Findings provide insights for government administrators and policymakers, guiding the development of trust-building strategies and organizational support mechanisms to promote effective chatbot adoption in public-sector workplaces. This research fills the empirical gap in understanding chatbot adoption from the perspective of government employees and illuminates opportunities and challenges as public-sector employees adapt to technological changes in their work environments.

随着技术的快速发展，人工智能聊天机器人融入政府工作场所是加强沟通、简化行政流程、提高员工效率的革命性转变。本研究通过结合调查数据和员工访谈的混合方法设计，分析了政府机构对AI聊天机器人的信任如何影响员工对聊天机器人的使用，并考察了组织支持的调节作用。通过展示信任和组织支持的关键作用，本研究强调了它们在推动政府机构采用和数字化转型方面的综合作用。研究结果为政府管理者和政策制定者提供了见解，指导建立信任战略和组织支持机制的发展，以促进公共部门工作场所有效采用聊天机器人。本研究填补了从政府雇员角度理解聊天机器人采用的经验空白，并阐明了公共部门雇员在适应工作环境中的技术变革时所面临的机遇和挑战。

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引用次数: 0

How AI assistance enhances work adaptability in the public sector: A mixed-methods study from Southwest China 人工智能辅助如何增强公共部门的工作适应性：来自西南地区的混合方法研究

IF 1 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2025-12-01 Epub Date: 2025-10-29 DOI: 10.1016/j.giq.2025.102089

Wei Zhang, Yili Yu

As artificial intelligence (AI) becomes increasingly embedded in public organizations, a critical challenge is how employees adapt to technological change while maintaining effective job performance.The objective of this study is to examine how AI assistance influences public employees' work adaptability. Drawing on Sociotechnical Systems (STS) theory, we develop a moderated mediation model in which AI assistance enhances adaptability through employee creativity, while task complexity serves as a contextual moderator. To test this model, we conducted a 2-by-2 field experiment (AI-assisted vs. non-assisted; complex vs. simple tasks) in the traffic management division of a municipal public security bureau in southwestern China, involving 408 participants and complemented by semi-structured interviews with 20 employees.The results show that AI enhances employees' work adaptability indirectly by stimulating creativity. Moreover, the positive effects of AI are amplified under high task complexity, indicating that AI performs more effectively in cognitively demanding contexts. These findings advance the theoretical understanding of the “technology-task-human” triadic interaction, extend micro-level behavioral research on AI in public administration, and provide practical guidance for the conditional deployment of AI in public organizations.

随着人工智能（AI）越来越多地融入公共组织，一个关键的挑战是员工如何适应技术变革，同时保持有效的工作绩效。本研究的目的是研究人工智能辅助如何影响公共雇员的工作适应性。利用社会技术系统（STS）理论，我们开发了一个有调节的中介模型，其中人工智能辅助通过员工创造力增强适应性，而任务复杂性则作为语境调节因子。为了验证这一模型，我们在中国西南某市公安局的交通管理部门进行了2乘2的现场实验（人工智能辅助与非辅助；复杂任务与简单任务），涉及408名参与者，并辅以对20名员工的半结构化访谈。结果表明，人工智能通过激发创造力间接提高了员工的工作适应性。此外，人工智能的积极影响在高任务复杂性下被放大，这表明人工智能在认知要求较高的环境中表现得更有效。这些研究成果推进了对“技术-任务-人”三元互动的理论认识，拓展了人工智能在公共管理中的微观行为研究，并为人工智能在公共组织中的有条件部署提供了实践指导。

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引用次数: 0

Understanding public acceptance of data collection by intelligence services in the Netherlands: A factorial survey experiment 了解公众对荷兰情报部门收集数据的接受程度：一个析因调查实验

IF 1 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2025-12-01 Epub Date: 2025-10-29 DOI: 10.1016/j.giq.2025.102077

E.C. Oomens, R.S. van Wegberg, M.J.G. van Eeten, A.J. Klievink

Intelligence services must balance values such as national security and privacy when collecting data, with each scenario involving specific contextual trade-offs. While citizens benefit from effective intelligence operations, they also risk having their rights infringed upon. This makes citizen perspectives on acceptable data collection for intelligence and national security salient, as their legitimacy is also contingent upon public support. Yet, important aspects of citizen perspectives are understudied, such as the influence of contextual factors related to the use of intelligence collection methods. This study, inspired by Nissenbaum's contextual integrity framework, uses a factorial survey experiment with vignettes among a representative sample of 1423 Dutch citizens to examine the influence of threat type, duration, data subject, collection method, data type, and data retention on public acceptance of surveillance. Additionally, the study considers the impact of respondents' trust and privacy attitudes. The findings reveal significant influence of both contextual variables – particularly threat type, data subject, and data retention – and respondent predispositions – particularly trust in institutions, trust in intelligence services' competence, and privacy concerns for others. The findings imply that more in-depth contextual knowledge among the public may foster support for intelligence activities.

情报机构在收集数据时必须平衡国家安全和隐私等价值，每种情况都涉及特定的上下文权衡。虽然公民从有效的情报行动中受益，但他们的权利也有受到侵犯的风险。这使得公民对可接受的情报和国家安全数据收集的看法变得突出，因为它们的合法性也取决于公众的支持。然而，公民观点的重要方面尚未得到充分研究，例如与使用情报收集方法有关的背景因素的影响。本研究受Nissenbaum的上下文完整性框架的启发，在1423名荷兰公民的代表性样本中使用了一个因子调查实验，以检验威胁类型、持续时间、数据主体、收集方法、数据类型和数据保留对公众接受监视的影响。此外，研究还考虑了受访者的信任和隐私态度的影响。研究结果揭示了上下文变量（特别是威胁类型、数据主体和数据保留）和受访者倾向（特别是对机构的信任、对情报服务能力的信任以及对他人隐私的关注）的显著影响。研究结果表明，在公众中更深入的背景知识可能会促进对情报活动的支持。

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引用次数: 0

Human-centric AI governance: what the EU public values, what it really, really values 以人为中心的人工智能治理：欧盟公众的价值观是什么，它真正的价值观是什么

IF 1 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2025-12-01 Epub Date: 2025-10-24 DOI: 10.1016/j.giq.2025.102084

Valentin Wittmann, Timo Meynhardt

The EU and other institutions worldwide have committed to aligning AI with human values to ensure that the technology contributes to the common good. Yet, criticism persists that debates over which values should guide this alignment are dominated by private and public organizations that prioritize technological considerations. Societal perspectives that emphasize broader, non-normative values are often marginalized. This exclusion generates a democratic deficit and risks forgoing the advantages of aligning AI with citizens' public values - namely trust, acceptance and public value creation. To address this gap, we empirically examine EU citizens' regulatory and value preferences regarding AI and its regulation, drawing on two complementary studies and Public Values theory and tools: one mixed-methods study of the EU's Public Consultation and one study based on the quantitative assessment of a newly developed AI Public Value (PV) Landscape. Our findings show that EU citizens (i) prefer binding regulation of AI, (ii) consider both ethical and technological principles as well as broader, non-normative societal values, especially along the moral-ethical value dimension, important, and (iii) serve as a conciliatory force capable of balancing business interests against those of state and NGO stakeholders. These results underscore the importance of aligning AI with broader PVs, reinforcing ethical foundations, and enhancing public inclusion in AI governance to achieve truly human-centric and socially accepted AI.

欧盟和世界各地的其他机构致力于使人工智能与人类价值观保持一致，以确保该技术为共同利益做出贡献。然而，批评仍然存在，关于哪些价值观应该指导这种一致性的争论，主要是由优先考虑技术的私人和公共组织主导的。强调更广泛、非规范性价值观的社会观点往往被边缘化。这种排斥产生了民主赤字，并有可能放弃将人工智能与公民的公共价值观（即信任、接受和公共价值创造）结合起来的优势。为了解决这一差距，我们利用两项互补研究和公共价值理论和工具，实证研究了欧盟公民对人工智能及其监管的监管和价值偏好：一项是对欧盟公众咨询的混合方法研究，另一项是基于对新开发的人工智能公共价值（PV）景观的定量评估的研究。我们的研究结果表明，欧盟公民(i)倾向于对人工智能进行约束性监管，（ii）同时考虑伦理和技术原则以及更广泛的、非规范性的社会价值观，尤其是道德-伦理价值维度，这很重要，（iii）作为一种调和力量，能够平衡商业利益与国家和非政府组织利益相关者的利益。这些结果强调了将人工智能与更广泛的pv结合起来，加强道德基础，加强公众对人工智能治理的包容，以实现真正以人为中心和社会接受的人工智能的重要性。

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