Government Information Quarterly最新文献_第9页

Analyzing digital government partnerships: An institutional logics perspective 分析数字政府伙伴关系：制度逻辑的视角

IF 7.8 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2024-12-06 DOI: 10.1016/j.giq.2024.101987

Yiwei Gong, Yan Yang

Digital government is transforming public service provision through collaboration between governments and companies. However, establishing digital government partnerships is complex and challenging, with governments often lacking a clear view of the influencing factors in various configurations and their underlying logics. Based on the theory of institutional logics, this study discusses the state, market, and corporation logic in digital government partnerships, and identifies six influencing factors. Employing a multiple qualitative comparative analysis method, the analysis of 31 provincial regions in Chinese mainland over five years derived 19 solutions that lead to a high digital government performance. These findings reveal the causal relationships between the configurational strategies for digital government partnerships and the different outcomes in terms of digital government performance. A series of propositions are derived to explain the logic multiplicity behind the configurations. This study theorizes the configurational relationships of the influencing factors and their underlying logics to enhance the understanding of the intricate diversity and dynamics exhibited within digital government partnerships.

数字政府正在通过政府和公司之间的合作改变公共服务的提供。然而，建立数字政府伙伴关系既复杂又具有挑战性，政府往往对各种配置中的影响因素及其潜在逻辑缺乏清晰的认识。本文基于制度逻辑理论，探讨了数字政府伙伴关系中的国家、市场和公司逻辑，并确定了六个影响因素。采用多元定性比较分析方法，对中国大陆31个省区5年的数据进行分析，得出19个提高数字化政府绩效的解决方案。这些发现揭示了数字政府合作伙伴关系的配置策略与数字政府绩效的不同结果之间的因果关系。导出了一系列命题来解释构型背后的逻辑多重性。本研究将影响因素的配置关系及其潜在逻辑理论化，以增强对数字政府伙伴关系中所表现出的复杂多样性和动态的理解。

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引用次数: 0

Examining public managers' competencies of artificial intelligence implementation in local government: A quantitative study 地方政府公共管理者实施人工智能能力的定量研究

IF 7.8 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2024-12-01 DOI: 10.1016/j.giq.2024.101986

Rodrigo Sandoval-Almazan , Adrian Osiel Millan-Vargas , Rigoberto Garcia-Contreras

The implementation of artificial intelligence in the public sector is a fast-evolving tendency in recent years. Despite much research on AI in government- ethics, algorithms, chatbots, AI systems-implement- there is very little data and understanding of the public manager's perception, adaptation, challenges, and resistance on this topic. What are the skills and knowledge needed to implement AI in the government? This research aims to investigate public managers' competencies to face AI challenges in the public sector. A survey was conducted among 38 key public managers from the government of the State of Mexico in the central region to assess their perceptions of AI. Using the competences for civil servants' framework from Balbo di Vinadio et al. (2022), we analyze three competences: (1) Digital Management and Execution (2) Digital Planning and Design (3) Data use and governance and their levels of. The findings point out that there is a lack of skills, and the competence of digital management and execution is the one that explains better this perception of AI in the local government.

近年来，人工智能在公共部门的应用是一个快速发展的趋势。尽管有很多关于政府中人工智能的研究——伦理、算法、聊天机器人、人工智能系统的实施——但很少有关于公共管理者对这个话题的感知、适应、挑战和抵制的数据和理解。在政府推行人工智能所需的技能和知识是什么？本研究旨在调查公共部门管理者应对人工智能挑战的能力。对中部地区墨西哥政府的38名主要公共管理人员进行了一项调查，以评估他们对人工智能的看法。使用Balbo di Vinadio等人（2022）的公务员能力框架，我们分析了三种能力：(1)数字管理和执行(2)数字规划和设计(3)数据使用和治理及其水平。调查结果指出，缺乏技能，数字管理和执行的能力可以更好地解释地方政府对人工智能的这种看法。

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引用次数: 0

Open Government Data (OGD) as a catalyst for smart city development: Empirical evidence from Chinese cities 开放政府数据（OGD）作为智慧城市发展的催化剂：来自中国城市的经验证据

IF 7.8 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2024-12-01 DOI: 10.1016/j.giq.2024.101983

Ruoyun Wang, Corey Kewei Xu, Xun Wu

While existing smart city models recognize the importance of data, they often overlook the specific role of Open Government Data (OGD) for urban development. This study addresses this gap by adapting the Smart City Model to explicitly include OGD as a critical component. Drawing on panel data from the 2022–2024 Chinese Cities Digitalization Evolution Index, we employ Structural Equation Modeling (SEM) to empirically examine the direct and indirect effects of OGD, digital infrastructure, and digital economy on smart city development. Our analysis identifies four key pathways, revealing that while digital infrastructure positively influences smart city development directly, the indirect pathways incorporating OGD demonstrate stronger effects. OGD plays a pivotal role by significantly enhancing the digital economy and digital infrastructure, as well as directly contributing to smart city development. This research contributes to the smart city literature by moving beyond discussions of individual components to empirically test the relationships between these elements. By positioning OGD as a catalyst, we provide a nuanced understanding of the mechanisms through which data-driven initiatives empower smart city development. Our findings offer valuable insights into the multifaceted ways OGD serves as a driving force for urban innovation, challenging the traditional view of government data as a passive resource. This study highlights the importance of OGD as a strategic asset for policymakers seeking to harness the potential of data-driven urban governance. We conclude with policy recommendations for leveraging OGD to support sustainable and efficient smart city development.

虽然现有的智慧城市模型认识到数据的重要性，但它们往往忽视了开放政府数据（OGD）在城市发展中的具体作用。本研究通过调整智慧城市模型，明确将OGD作为关键组成部分纳入其中，从而解决了这一差距。利用2022-2024年中国城市数字化演进指数的面板数据，我们运用结构方程模型（SEM）实证检验了OGD、数字基础设施和数字经济对智慧城市发展的直接和间接影响。我们的分析确定了四个关键途径，揭示了数字基础设施对智慧城市发展的直接积极影响，而包含OGD的间接途径表现出更强的影响。OGD在显著提升数字经济和数字基础设施以及直接促进智慧城市发展方面发挥着关键作用。本研究通过超越对单个组成部分的讨论，以经验检验这些要素之间的关系，为智慧城市文献做出了贡献。通过将OGD定位为催化剂，我们提供了对数据驱动计划增强智慧城市发展的机制的细致理解。我们的研究结果为OGD作为城市创新驱动力的多方面方式提供了有价值的见解，挑战了将政府数据视为被动资源的传统观点。本研究强调了OGD作为决策者寻求利用数据驱动型城市治理潜力的战略资产的重要性。最后，我们提出了利用OGD支持可持续、高效的智慧城市发展的政策建议。

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引用次数: 0

Public value positions and design preferences toward AI-based chatbots in e-government. Evidence from a conjoint experiment with citizens and municipal front desk officers 电子政务中基于人工智能的聊天机器人的公众价值立场和设计偏好。证据来自市民和市政前台官员的联合实验

IF 7.8 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2024-12-01 DOI: 10.1016/j.giq.2024.101985

Sebastian Hemesath , Markus Tepe

Developing a chatbot to handle citizen requests in a municipal office requires multiple design choices. We use public value theory to test how value positions shape these design choices. In a conjoint experiment, we asked German citizens (n = 1690) and front desk officers in municipalities (n = 267) to evaluate hypothetical chatbot designs that differ in their fulfillment of goals derived from different value positions: (1) maintaining security, privacy, and accountability, (2) improving administrative performance, and (3) improving user-friendliness and empathy. Experimental results show that citizens prefer chatbots programmed by domestic firms, value chatbots taking routine decisions excluding discretion, and strongly prefer human intervention when conversations fail. While altering the salience of public sector values through priming does not affect citizens' design choices consistently, we find systematic differences between citizens and front desk officers. However, these differences are qualitative rather than fundamental. We conclude that citizens and front desk officers share public values that provide a sufficient basis for chatbot designs that overcome a potential legitimacy gap of AI in citizens-state service encounters.

开发一个在市政办公室处理市民请求的聊天机器人需要多种设计选择。我们使用公共价值理论来测试价值定位如何影响这些设计选择。在一项联合实验中，我们要求德国公民（n = 1690）和市政部门的前台官员（n = 267）评估基于不同价值立场的聊天机器人设计，这些设计在实现目标方面存在差异：(1)维护安全、隐私和问责制，(2)提高行政绩效，(3)提高用户友好性和同理心。实验结果表明，公民更喜欢由国内公司编程的聊天机器人，重视聊天机器人进行日常决策，而不是自由裁量权，并且在对话失败时强烈倾向于人工干预。虽然通过启动改变公共部门价值观的显著性不会始终影响公民的设计选择，但我们发现公民和前台人员之间存在系统性差异。然而，这些差异是质的而不是根本的。我们得出的结论是，公民和前台人员拥有共同的公共价值观，这为聊天机器人的设计提供了充分的基础，从而克服了人工智能在公民与国家服务接触中潜在的合法性差距。

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引用次数: 0

Regulating generative AI: The limits of technology-neutral regulatory frameworks. Insights from Italy's intervention on ChatGPT 监管人工智能的生成：技术中立监管框架的局限性。意大利干预 ChatGPT 的启示

IF 7.8 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2024-11-23 DOI: 10.1016/j.giq.2024.101982

Antonio Cordella , Francesco Gualdi

Existing literature has predominantly concentrated on the legal, ethical, governance, political, and socioeconomic aspects of AI regulation, often relegating the technological dimension to the periphery, reflecting the design, use, and development of AI regulatory frameworks that are technology-neutral. The emergence and widespread use of generative AI models present new challenges for public regulators aiming at implementing effective regulatory interventions. Generative AI operates on distinctive technological properties that require a comprehensive understanding prior to the deployment of pertinent regulation. This paper focuses on the recent case of the suspension of ChatGPT in Italy to explore the impact the specific technological fabric of generative AI has on the effectiveness of technology-neutral regulation. By drawing on the findings of an exploratory case study, this paper contributes to the understanding of the tensions between the specific technological features of generative AI and the effectiveness of a technology-neutral regulatory framework. The paper offers relevant implications to practice arguing that until this tension is effectively addressed, public regulatory interventions are likely to underachieve their intended objectives.

现有文献主要集中在人工智能监管的法律、伦理、治理、政治和社会经济方面，往往将技术层面置于边缘，反映了技术中立的人工智能监管框架的设计、使用和发展。生成式人工智能模型的出现和广泛使用，对旨在实施有效监管干预的公共监管机构提出了新的挑战。生成式人工智能具有独特的技术特性，需要在部署相关监管措施之前对其进行全面了解。本文以最近意大利暂停 ChatGPT 的案例为重点，探讨了生成式人工智能的特定技术结构对技术中立监管的有效性的影响。通过利用探索性案例研究的结果，本文有助于理解生成式人工智能的特定技术特征与技术中立监管框架的有效性之间的紧张关系。本文认为，在这种矛盾得到有效解决之前，公共监管干预很可能无法实现其预期目标，从而为实践提供了相关启示。

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引用次数: 0

A more secure framework for open government data sharing based on federated learning 基于联合学习的更安全的开放式政府数据共享框架

IF 7.8 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2024-11-11 DOI: 10.1016/j.giq.2024.101981

Xingsen Zhang

Open government data, abbreviated as OGD, attracts significant public interest with substantial social value recently, which enables the government to make more accurate and efficient decisions based on real and comprehensive data. It also helps break down information silos, improve service quality and management efficiency, and enhance public trust in government activities. This is crucial for advancing public management modernization, fostering technological innovation, and strengthening governance capabilities. The focus of this study is how to solve the problem of more secure sharing of OGD. And we developed a more secure framework for open government data sharing based on federated learning. Inspired by the government data authorization operation model, this framework includes four categories of participants: OGD providers, OGD collectors, OGD operators, and OGD users. We further analyzed modeling techniques for horizontal federated learning, vertical federated learning, and federated transfer learning. By applying this framework to typical scenarios in China, its actual effectiveness has been illustrated in preventing information leakage, protecting data privacy, and improving model security, providing more reliable and efficient solutions for government governance and public services. Future research can continuously explore the application of privacy-computing-related technologies in secure sharing of OG to further enhance data security and the potential of OGD.

开放式政府数据（简称 OGD）近来备受公众关注，具有巨大的社会价值，使政府能够基于真实、全面的数据做出更加准确、高效的决策。它还有助于打破信息孤岛，提高服务质量和管理效率，增强公众对政府活动的信任。这对于推进公共管理现代化、促进技术创新和加强治理能力至关重要。本研究的重点是如何解决更安全地共享 OGD 的问题。我们开发了一个基于联合学习的更安全的政府数据开放共享框架。受政府数据授权运行模式的启发，该框架包括四类参与者：开放政府数据提供者、开放政府数据收集者、开放政府数据操作者和开放政府数据使用者。我们进一步分析了横向联合学习、纵向联合学习和联合转移学习的建模技术。通过将该框架应用于中国的典型场景，说明其在防止信息泄露、保护数据隐私、提高模型安全性等方面的实际效果，为政府治理和公共服务提供更可靠、更高效的解决方案。未来的研究可以继续探索隐私计算相关技术在OG安全共享中的应用，以进一步提高数据的安全性和OGD的潜力。

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引用次数: 0

Does trust in government moderate the perception towards deepfakes? Comparative perspectives from Asia on the risks of AI and misinformation for democracy 对政府的信任会缓和对深度假新闻的看法吗？从亚洲的比较视角看人工智能和错误信息对民主的风险

IF 7.8 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2024-10-19 DOI: 10.1016/j.giq.2024.101980

Taberez Ahmed Neyazi , Arif Hussain Nadaf , Khai Ee Tan , Ralph Schroeder

There have recently been growing global concerns about misinformation, and more specifically about how deepfake technologies have been used to run disinformation campaigns. These concerns, in turn, have influenced people's perceptions of deepfakes, often associating them with threats to democracy and fostering less positive views. But does high trust in government mitigate these influences, thereby strengthening positive perceptions of deepfakes? In a cross-national survey conducted in Malaysia, Singapore, and India, we found no evidence of a negative association either between concern about the spread of misinformation online or perceived risks of AI to democracy, with positive attitudes towards deepfakes. However, when accounting for the moderating factor of trust in government, respondents in Singapore who have high trust levels exhibited more positive attitudes towards deepfakes, despite their concerns about misinformation. Similarly, higher trust in government correlated with more favorable perceptions of deepfakes even among those who view AI as a risk to democracy; this effect is evident across all three countries. In the conclusion, we spell out the implications of these findings for politics in Asia and beyond.

最近，全球对虚假信息的关注与日俱增，更具体地说，是对深度伪造技术如何被用于虚假信息宣传的关注与日俱增。这些担忧反过来又影响了人们对深度伪造技术的看法，往往将其与对民主的威胁联系在一起，助长了不太积极的观点。但是，对政府的高度信任是否会减轻这些影响，从而加强人们对深度假新闻的正面看法呢？在马来西亚、新加坡和印度进行的一项跨国调查中，我们没有发现任何证据表明，对网上错误信息传播的担忧或人工智能对民主的风险感知与对深度假新闻的积极态度之间存在负相关。然而，当考虑到对政府信任这一调节因素时，尽管新加坡的受访者对错误信息表示担忧，但信任度高的受访者对深度假新闻表现出更积极的态度。同样，即使在那些认为人工智能对民主构成风险的受访者中，对政府的较高信任度也与对深度假新闻的较好看法相关；这种效应在所有三个国家都很明显。在结论部分，我们阐述了这些发现对亚洲及其他地区政治的影响。

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引用次数: 0

Open government data and self-efficacy: The empirical evidence of micro foundation via survey experiments 开放政府数据与自我效能：通过调查实验的微观基础实证

IF 7.8 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2024-10-18 DOI: 10.1016/j.giq.2024.101975

Kuang-Ting Tai , Pallavi Awasthi , Ivan P. Lee

Research on the potential impacts of government openness and open government data is not new. However, empirical evidence regarding the micro-level impact, which can validate macro-level theories, has been particularly limited. Grounded in social cognitive theory, this study contributes to the literature by empirically examining how the dissemination of government information in an open data format can influence individuals' perceptions of self-efficacy, a key predictor of public participation. Based on two rounds of online survey experiments conducted in the U.S., the findings reveal that exposure to open government data is associated with decreased perceived self-efficacy, resulting in lower confidence in participating in public affairs. This result, while contrary to optimistic assumptions, aligns with some other empirical studies and highlights the need to reconsider the format for disseminating government information. The policy implications suggest further calibration of open data applications to target professional and skilled individuals. This study underscores the importance of experiment replication and theory development as key components of future research agendas.

关于政府开放和开放政府数据的潜在影响的研究并不新鲜。然而，能够验证宏观理论的微观层面影响的实证证据却特别有限。本研究以社会认知理论为基础，通过实证研究以开放数据格式传播政府信息如何影响个人的自我效能感（公众参与的关键预测因素），为相关文献做出了贡献。基于在美国进行的两轮在线调查实验，研究结果显示，接触开放式政府数据与感知自我效能下降有关，从而导致参与公共事务的信心下降。这一结果虽然与乐观的假设相反，但与其他一些实证研究相一致，并强调了重新考虑政府信息传播形式的必要性。政策影响建议进一步校准开放数据应用，以专业技能人才为目标。本研究强调了实验复制和理论发展作为未来研究议程关键组成部分的重要性。

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引用次数: 0

Transforming towards inclusion-by-design: Information system design principles shaping data-driven financial inclusiveness 通过设计实现包容性转型：信息系统设计原则塑造数据驱动的金融包容性

IF 7.8 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2024-10-17 DOI: 10.1016/j.giq.2024.101979

Reni Sulastri, Marijn Janssen, Ibo van de Poel, Aaron Ding

Digitalization and datafication of financial systems result in more efficiency, but might also result in the exclusions of certain groups. Governments are looking for ways to increase inclusions and leave no one behind. For this, they must govern an organizational ecosystem of public and private parties. We derive value-based requirements through a systematic research methodology and iteratively refine design principles for achieving inclusivity goals. This refinement process is enriched by interviews with field experts, leading to the formulation of key Design principles: the essential role of inclusive metrics, leveraging alternative data sources, ensuring transparency in loan processes and the ability for decision contestation, providing tailored credit solutions, and maintaining long-term system sustainability. The government's role is to ensure a level playing field where all parties have equal access to the data. Following the principles ensures that exclusion and discrimination become visible and can be avoided. This study underscores the necessity for system-level transformations, inclusion-by-design, and advocacy for a new system design complemented by regulatory updates, new data integration, inclusive AI, and organizational collaborative shifts. These principles can also be used in different data-driven governance situations.

金融系统的数字化和数据化提高了效率，但也可能导致某些群体被排除在外。各国政府正在想方设法提高包容性，不让任何人掉队。为此，他们必须管理一个由公共和私营各方组成的组织生态系统。我们通过系统的研究方法得出基于价值的要求，并反复推敲实现包容性目标的设计原则。通过与实地专家的访谈，我们丰富了这一完善过程，最终制定了关键的设计原则：包容性衡量标准的重要作用、利用替代数据源、确保贷款流程的透明度和决策争议能力、提供量身定制的信贷解决方案，以及保持系统的长期可持续性。政府的职责是确保公平的竞争环境，使各方都能平等地获取数据。遵循这些原则可确保排斥和歧视现象变得明显并得以避免。这项研究强调，必须进行系统层面的变革，通过设计实现包容性，并倡导新的系统设计，辅之以监管更新、新的数据整合、包容性人工智能和组织合作转变。这些原则也可用于不同的数据驱动治理情况。

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引用次数: 0

Bridging the gap: Towards an expanded toolkit for AI-driven decision-making in the public sector 缩小差距：为公共部门的人工智能驱动决策开发扩展工具包

IF 7.8 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Government Information Quarterly

Pub Date : 2024-10-11 DOI: 10.1016/j.giq.2024.101976

Unai Fischer-Abaigar , Christoph Kern , Noam Barda , Frauke Kreuter

AI-driven decision-making systems are becoming instrumental in the public sector, with applications spanning areas like criminal justice, social welfare, financial fraud detection, and public health. While these systems offer great potential benefits to institutional decision-making processes, such as improved efficiency and reliability, these systems face the challenge of aligning machine learning (ML) models with the complex realities of public sector decision-making. In this paper, we examine five key challenges where misalignment can occur, including distribution shifts, label bias, the influence of past decision-making on the data side, as well as competing objectives and human-in-the-loop on the model output side. Our findings suggest that standard ML methods often rely on assumptions that do not fully account for these complexities, potentially leading to unreliable and harmful predictions. To address this, we propose a shift in modeling efforts from focusing solely on predictive accuracy to improving decision-making outcomes. We offer guidance for selecting appropriate modeling frameworks, including counterfactual prediction and policy learning, by considering how the model estimand connects to the decision-maker's utility. Additionally, we outline technical methods that address specific challenges within each modeling approach. Finally, we argue for the importance of external input from domain experts and stakeholders to ensure that model assumptions and design choices align with real-world policy objectives, taking a step towards harmonizing AI and public sector objectives.

人工智能驱动的决策系统正在成为公共部门的重要工具，其应用领域涵盖刑事司法、社会福利、金融欺诈检测和公共卫生等。虽然这些系统为机构决策过程提供了巨大的潜在好处，如提高效率和可靠性，但这些系统也面临着将机器学习（ML）模型与公共部门决策的复杂现实相协调的挑战。在本文中，我们研究了可能出现不匹配的五大挑战，包括分布偏移、标签偏差、数据方面过去决策的影响，以及模型输出方面的竞争目标和人为环路。我们的研究结果表明，标准的 ML 方法往往依赖于无法充分考虑这些复杂性的假设，从而可能导致不可靠和有害的预测。为了解决这个问题，我们建议将建模工作从单纯关注预测准确性转向改善决策结果。通过考虑模型估计值与决策者效用之间的联系，我们为选择适当的建模框架（包括反事实预测和政策学习）提供了指导。此外，我们还概述了应对每种建模方法中特定挑战的技术方法。最后，我们论证了来自领域专家和利益相关者的外部意见的重要性，以确保模型假设和设计选择符合现实世界的政策目标，从而朝着协调人工智能和公共部门目标的方向迈出一步。

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引用次数: 0