Government Information Quarterly最新文献_第8页

Exploiting GPT for synthetic data generation: An empirical study 利用GPT合成数据生成：一个实证研究

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

Government Information Quarterly

Pub Date : 2024-12-19 DOI: 10.1016/j.giq.2024.101988

Tony Busker , Sunil Choenni , Mortaza S. Bargh

There are many good reasons to use synthetic data instead of real data for research purposes. These reasons may range from the business sensitiveness of real data to increased cost of collecting real data in accordance with GDPR requirements. In this paper, we elaborate upon the potentials of the Large Language Model GPT as a tool to generate synthetic data for analytical purposes when there is no real-data available or accessible. Primarily, we show that by varying the scope of probes adequately, we can generate data of different granularities. To show this, we generated stereotypical data with three levels of granularity by posing more than 18,500 probes to GPT. In total, we generated stereotypical data for eight different views, which can be categorized in three view types corresponding to the three levels of granularity. Secondarily, we show that by varying the scope of probes one can create meaningful information. To show this, we performed a so-called similarity analysis on the generated stereotypical data. We used data visualizations, e.g. heatmaps, to show the views and categories within the views that are similar and those that are at odd with each other. We elaborate upon the application areas of the insight gained about such similarities and differences. Furthermore, we discuss several other types of analysis that can be performed on the generated stereotypical data.

出于研究目的，有很多很好的理由使用合成数据而不是真实数据。这些原因可能包括实际数据的业务敏感性，以及根据GDPR要求收集实际数据的成本增加。在本文中，我们详细阐述了大型语言模型GPT作为一种工具的潜力，当没有可用或可访问的实际数据时，它可以生成用于分析目的的合成数据。首先，我们表明，通过适当地改变探针的范围，我们可以生成不同粒度的数据。为了证明这一点，我们通过向GPT放置超过18,500个探针，生成了具有三个粒度级别的典型数据。总的来说，我们为8个不同的视图生成了原型数据，这些数据可以分为三种视图类型，对应于三个粒度级别。其次，我们表明，通过改变探针的范围，可以创建有意义的信息。为了证明这一点，我们对生成的刻板印象数据进行了所谓的相似性分析。我们使用数据可视化，例如热图，来显示视图和视图中的相似和不一致的视图和类别。我们详细阐述了关于这些相似点和不同点的见解的应用领域。此外，我们还讨论了可以对生成的原型数据执行的几种其他类型的分析。

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引用次数: 0

Leave it to the parents: How hacktivism-as-tuning reconfigures public sector digital transformation 把它留给父母：黑客行动主义如何重新配置公共部门的数字化转型

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

Government Information Quarterly

Pub Date : 2024-12-17 DOI: 10.1016/j.giq.2024.101996

Claire Ingram Bogusz , Johan Magnusson , Mattias Rost

Extant research on public sector digital transformation has emphasised the process of deliberate digital technology use by public organizations in pursuit of efficiency and innovation. Studies of the unintended or contrarian uses associated with digital technologies have been scarce. This study explores a case in which parents of schoolchildren in the City of Stockholm react to the perceived poor usability of a learning management system through citizen “hacktivism”. The parents developed a challenger app on top of an existing platform, to which the city reacted by trying to obstruct development work, both technically and through litigation. We interpret this as a case of digital transformation reconfiguration through boundary object tuning, legal tuning and digital transformation tuning. These lead to, respectively, reconfiguration of 1) the site of transparency and engagement, 2) the boundaries of responsibility and ownership and 3) the locus of control over public services. We contribute to the public sector digital transformation literature by offering tuning as a way to understand (re)configuration of the non-linear and dialectical and materially embedded process of digital transformation. We also empirically explore the phenomenon of citizen hacktivism, offering insights into associated processes and effects.

现有的关于公共部门数字化转型的研究强调了公共组织在追求效率和创新的过程中有意识地使用数字技术。与数字技术相关的意外或反向使用的研究很少。本研究探讨了一个案例，其中斯德哥尔摩市小学生的父母通过公民“黑客行动主义”对学习管理系统的可用性差做出反应。这对父母在现有平台上开发了一款挑战者应用程序，市政府的反应是试图通过技术和诉讼来阻碍开发工作。我们将此解释为通过边界对象调谐，法律调谐和数字转换调谐进行数字转换重构的案例。这些分别导致了以下方面的重新配置：1)透明和参与的场所；2)责任和所有权的界限；3)公共服务的控制点。我们通过提供调整作为一种理解（重新）配置非线性、辩证和物质嵌入的数字化转型过程的方式，为公共部门数字化转型文献做出贡献。我们也实证地探讨了公民黑客行动主义的现象，提供了相关过程和影响的见解。

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引用次数: 0

What determinants influence citizens' engagement with mobile government social media during emergencies? A net valence model 在紧急情况下，哪些决定因素影响公民对移动政府社交媒体的参与？净价模型

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

Government Information Quarterly

Pub Date : 2024-12-12 DOI: 10.1016/j.giq.2024.101995

Houcai Wang , Zhenya Robin Tang , Li Xiong , Xiaoyu Wang , Lei Zhu

Citizens proactively engage in public deliberation during emergencies, which is pivotal for the success of emergency management. Drawing on the net valence model, the current manuscript investigates the antecedents for citizens' engagement in mobile government social media during emergencies. Using an online payment survey service provider, data were acquired from 740 subscribers to mobile government social media in mainland China. The research findings show that source credibility and perceived transparency, but not mobility, increased perceived benefits, which further increased citizens' engagement during emergencies. The findings also demonstrate that privacy risk and perceived Internet censorship increased perceived risk; however, perceived risk did not affect citizens' engagement during emergencies. These findings can inform future research on public participation with mobile government social media in emergencies and provide insights for emergency management practitioners.

在紧急情况下，公民积极参与公共审议，这是应急管理成功的关键。利用净价模型，目前的手稿调查了公民在紧急情况下参与移动政府社交媒体的先决条件。通过在线支付调查服务提供商，从中国大陆移动政府社交媒体的740名用户中获取数据。研究结果表明，信息源可信度和感知透明度（而非流动性）增加了感知利益，这进一步提高了突发事件期间公民的参与度。研究结果还表明，隐私风险和感知到的互联网审查增加了感知风险；然而，在紧急情况下，感知风险并不影响公民的参与。这些发现可以为未来关于突发事件中公众参与移动政府社交媒体的研究提供信息，并为应急管理从业者提供见解。

引用次数: 0

Data-driven intelligence in crisis: The case of Ukrainian refugee management 危机中的数据驱动情报：乌克兰难民管理案例

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

Government Information Quarterly

Pub Date : 2024-12-11 DOI: 10.1016/j.giq.2024.101978

Kilian Sprenkamp , Mateusz Dolata , Gerhard Schwabe , Liudmila Zavolokina

The ongoing conflict in Ukraine has triggered a humanitarian crisis, leading to a substantial increase in refugees. This situation presents a significant challenge for European countries, emphasizing the urgent need for effective refugee management strategies. Hence, effective decision-making is needed for the public sector to create a better livelihood for refugees. In this study, we propose using the concept of intelligence defined by Herbert Simon for effective refugee management. Following the Design Science Research Methodology, we utilize 58 semi-structured stakeholder interviews within Switzerland to identify problems and define design goals that facilitate intelligence in refugee management. Based on the design goals, we developed R2G – “Refugees to Government”, an application that utilizes community data and state-of-the-art NLP, including a chatbot interface, to offer an interactive dashboard for identifying refugee needs. The chatbot allows policymakers to interact with refugee data through dynamic, conversational queries, enabling real-time identification of refugee needs and providing data-driven intelligence. Our assessment of R2G, facilitated through 28 semi-structured interviews, resulted in four design principles for data-driven intelligence in refugee management: community-driven insight, spatial-temporal knowledge, multilingual data synthesis and visualization, and interactive data querying through chatbots. Additionally, we provide policy recommendations emphasizing the ethical use of community data, the integration of advanced NLP techniques in government processes, and the need for shifting governmental roles towards data analytics.

乌克兰持续不断的冲突引发了一场人道主义危机，导致难民人数大幅增加。这种情况对欧洲国家构成重大挑战，强调迫切需要制订有效的难民管理战略。因此，公共部门需要有效的决策，为难民创造更好的生计。在这项研究中，我们建议使用赫伯特·西蒙定义的情报概念进行有效的难民管理。遵循设计科学研究方法论，我们利用瑞士境内的58个半结构化利益相关者访谈来识别问题并定义设计目标，从而促进难民管理的智能化。基于设计目标，我们开发了R2G——“难民到政府”，这是一个利用社区数据和最先进的自然语言处理（NLP）的应用程序，包括聊天机器人界面，为识别难民需求提供了一个交互式仪表板。聊天机器人允许政策制定者通过动态对话查询与难民数据进行交互，从而实时识别难民需求并提供数据驱动的情报。通过28个半结构化访谈，我们对R2G进行了评估，得出了难民管理中数据驱动智能的四个设计原则：社区驱动的洞察力、时空知识、多语言数据合成和可视化，以及通过聊天机器人进行交互式数据查询。此外，我们还提供了政策建议，强调社区数据的道德使用，将先进的NLP技术整合到政府流程中，以及将政府角色转向数据分析的必要性。

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引用次数: 0

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