The advent of the internet has significantly enhanced accessibility to information, facilitating the engagement of diverse communities with online resources. Despite the abundance of information available, navigating the structures of large organizations and effectively digesting essential personalized information remains a challenge. Consequently, individuals may be deterred from extracting valuable insights from already available resources. This paper addresses this issue by integrating a university's official website into an AI chatbot powered by large language models (LLMs). We demonstrate use cases to provide information tailored to general information-seeking and personalized information needs for college major selection. We present a novel approach for individuals to gain insights into large organizations via interactive conversation. Based on our system demonstration, we further delve into the role of generative AI in synthesizing vast organizational datasets into user-friendly formats accessible to the public and its implications for E-government and open government research.
This paper examines the use and theoretical support underlying the implementation of emerging technologies for citizen participation in cities. Based on a systematic literature review in the main database of digital government (Digital Government Research Library -DGRL-), we analyze the evidence drawn by prior research concerning the situation, theoretical support, and use of these disruptive technologies in citizen participation. We seek to contribute to prior research on arising a critical debate about the initial implementation of these technologies for promoting collaborative models of governance and providing future research directions to advance in this topic.
The transformation and development of the digital government serve as a crucial pathway for the modernization of national governance capability and level. Building a digital government is a key lever for the sustained development of Digital China in the era of big data. However, research indicates that the existing government smart service platforms, as one of the means for the modernization of digital government governance, still suffer from issues such as poor layout design and incomplete functional content, leading to a significant decrease in the willingness of the public users to use them. In light of this, the author primarily conducts an analysis of the theoretical framework and the current status of the construction and development of existing government smart service platforms through qualitative analysis, quantitative analysis, interdisciplinary research, etc. Based on this analysis, the study also includes research practices, problem elucidation, and cause analysis, along with platform construction practices, with the hope of exploring the development direction for the future upgrading and transformation of relevant smart service platforms.
Since its deployment in 2021, the Berlin Declaration monitoring mechanism (BDM) has proven to be an effective tool to monitor the digital transformation of the 27 Member States of the European Union (EU). Particularly, by putting forward seven Policy Areas that tackle different aspects of the digital sphere, the BDM has supported Member States in implementing the objectives set out in the Declaration, while fostering transparency and enabling countries to learn from one another. Overall, the results of the monitoring mechanism between 2021 and 2022 vary depending on the Policy Area under consideration. Therefore, after providing an overview of the BDM methodology, this paper aims to investigate two Policy Areas that have advanced at different paces over the same period of time, namely Policy Area 3 – Foster digital empowerment and digital literacy and Policy Area 4 – Strengthen trust through security in the digital sphere, to better understand how the Berlin Declaration and its related monitoring mechanism have helped Member States to foster the digital transformation of their governments and public services. To do so, good practices from different European countries, as well as initiatives advanced by the European Commission, will be examined. In its conclusion, this paper will point out some limitations related to the BDM itself and set forth possible future developments beyond the mere application of the Declaration, taking into account the priorities of the Belgian Presidency of the Council of the European Union, which will run from January to June 2024.
While the cutting-edge AI algorithms like ChatGPT are being adopted to government bureaucracy, there is a lack of discussion about which AI systems should be adopted and under what administrative conditions to ensure accountability. This study addresses the issue of AI system adoption in public sector from the perspective of accountability, which derived from institutionalism and democratic governance. Specifically, we borrow two terms from Romzek & Ingraham (2000) [36], "source of control" and "discretion of agency", to present a framework that categorizes "who can hold governments accountable" and "what kind of AI systems should be utilized?" for four types of accountability. we also point out the issue of the states of knowledge for each type of accountability and examines how AI systems can be adopted to address it. AI systems are discussed in terms of Symbolic and Connectionist AI systems, based on two approaches on AI. By doing so, we present a framework to help ensure that AI systems being deployed in the public sector are accountable.
The complex interplay between wildlife conservation and community support in areas prone to human-wildlife conflicts, particularly livestock depredation, poses challenges to effective conservation efforts. This study explores the potential of digital governance tools to address these challenges, focusing on the acceptance and feasibility of such technologies in tiger reserve areas. The research objectives include assessing community willingness to adopt digital platforms for reporting wildlife incidents, exploring perceived benefits and concerns, and proposing strategies to overcome barriers for effective integration. Digital governance tools offer innovative solutions by enabling real-time data collection, conflict monitoring, and community engagement. However, challenges arise due to socio-economic factors, limited technology access, and low digital literacy in local populations. Capacity-building programs are identified as crucial for empowering communities, reducing reliance on forest officials, and fostering sustainable cohabitation. The research, conducted in the Panna Tiger Reserve in India, employs a two-phase approach. The first phase involves a survey of 150 households to gauge community knowledge, attitudes, and accessibility to digital platforms. The second phase includes a workshop where participants perform tasks related to compensation processes using mobile applications. Results indicate a positive willingness to adopt digital platforms, particularly among the younger population. Demographic analysis reveals the educational and economic profile of the sample population, emphasizing the need for tailored capacity-building programs. Workshop data analysis demonstrates initial challenges but highlights the potential for improved performance and acceptance through education. The study identifies concerns in technological accessibility, digital literacy, language barriers, data privacy, socio-economic factors, and cultural appropriateness. Recommendations include enhancing digital infrastructure, fostering digital literacy through community programs, ensuring language and cultural inclusivity in platform design, addressing privacy concerns, and promoting community engagement. In conclusion, the successful integration of digital governance in wildlife conservation requires a multifaceted approach, including infrastructure improvement, education, cultural sensitivity, and economic accessibility. The study provides valuable insights into the challenges and opportunities of implementing digital solutions, emphasizing the importance of community involvement for sustainable conservation practices.
The purpose of this research is to review the impact on open government data in evaluation at world level and research in academia. First, six most renowned open government data evaluations are reviewed with their impact section. Then, a systematic literature review on the impact of open government data is conducted with test of AI-assisted software using Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Protocol, to find the current status of realized open government data impact research. Only four out of six evaluations of open government data have covered impact explicitly, and their criteria counting toward true impact is blur at best. AI-assisted software saves around 30 similar to 40% of reviewing time in this research. Political, social, and economic impacts are all included research topics, however, no environmental impact research. Political impact outperformed other impact, both in quality and quantity. Political capital, not social capital shows more social impact on open government data. Economic impact research of open government data is scarce, and more about the open government data helps urban innovation in China. This research contributes to the open government data research in three ways. First, it reviews impact section major open government data evaluations. Second, it tests the AI-assisted software on the impact of open government data. Third, it sheds light on new research direction for impact of open government data on case study, qualitative research and mixed method.
This study investigates China's reputation on Twitter in the beginning of the COVID-19 period. Drawing from a four-dimensional framework of country reputation, this study examined the dimensions and sentiments of the public discourse. Twitterverse largely focused on the dimension of China's country competence at the beginning of the COVID-19 pandemic and displayed highly negative sentiment.
This study comparatively analyzes the differences between managers and practitioners in public officials' attitudes toward Data-Driven Government. While previous research has extensively covered the technological and institutional aspects of data-driven government, there has been a notable gap in understanding how and whether data-driven government can be facilitated at an organizational behavior level. This study attempts to fill that gap by emphasizing the perceptual mechanism toward participation in a data-driven administration process. It examines the determinants of public officials' attitudes toward a data-driven government, positing that organizational behavior factors should be significantly considered in future research. The study also provides meaningful results from comparing differences of attitudes toward data-driven government between managers and practitioners in the Korean government. Additionally, it offers policy recommendations and theoretical implications by integrating UTAUT (Unified Theory of Acceptance and Use of Technology) and additional variables based on Job Characteristics Theory.
The Linked Open Vocabularies (LOV) registry, designed with the Linked Data principles at core, provides an environment suitable for research which targets domain-specific, but also potentially reusable, information representation. The main purpose of this study is to follow the recommendations pertaining to the utilisation of LOV as a basis for experimentation in order to examine how information within the Cultural Heritage (CH) domain can be improved in terms of reusability and interoperability. The present lack of cross-domain knowledge transfer forms the motivation behind this study, with the aim of facilitating the transition from conventional, domain-specific knowledge representation to reusable and semantically interoperable information. The methodology of this study involves the manual semantic mapping of elements from 12 vocabularies in the LOV registry, reinforced by a small-scale experiment using contemporary large language models (LLMs), particularly GPT, for a preliminary assessment of the mapping process. The findings revealed several key aspects to consider regarding the alignment of semantically adjacent vocabulary elements in the CH domain and beyond, emphasising the potential unveiled by linking domain-focused schemata to standardised, established ones while preserving the conceptual hierarchies inherent to each individual knowledge domain. The contribution of this research pertains to the vision of linking data across different domains by initiating the alignment among representation schemata in CH, with the ultimate aim to expand beyond the boundaries of the in-word knowledge domain, while employing combinatory methodological approaches of technological means and human expertise to facilitate this process.
Online petitions are an important means for citizens to express their concerns and to interact with government entities. Due to the increase in the number of petitions, manually attributing them to the competent unit in public administration creates a bottleneck, leading to response delays, and potentially even errors. To address this problem, a multi-label classifier using fine-tuned BERT model is suggested. The proposed model, trained on a dataset from the Taiwanese Join Platform, performs reasonably well in predicting the governmental departments in charge of petitions, even when trained on an imbalanced and rather small dataset. The obtained model manages to effectively process petitions and predicts responsible departments, achieving F1 score of 0.61 averaged over 12 categories. The proposed approach would potentially improve government responsiveness, optimize resource allocation, and facilitate online petition processing. Future work would focus on improving the model’s generalization capabilities.
The phenomenon of Digital Transformation, driven by Digital Information and Communication Technologies, has played a pivotal role in enhancing efficiency and collaborative effectiveness across various societal domains, including education. The Brazilian National Textbook Program (PNLD), established in the late 1930s, exemplifies adaptability over time, embracing technological advances for the modernization of educational content provision. This article presents a case study on digital transformation in PNLD’s pedagogical evaluation, involving active engagement with reviewers. The focus is on analyzing recent public calls to identify trends and improvement opportunities, contributing to a nuanced understanding of evaluation processes. The paper outlines strategic steps for digital transformation, aiming to optimize efficiency and effectiveness in contemporary education. It concludes by emphasizing the significance of Augmented Intelligence as a tool to enhance the pedagogical evaluation process, emphasizing the need for ethical implementation. Overall, the ongoing work signifies a user-centric approach, crucial for the success of the PNLD’s digital transformation in pedagogical evaluation.
Generative Artificial Intelligence (AI) systems bring innovative ways of information provision and knowledge delivery. In the public sector, generative AI has the potential to decrease bureaucratic discretion in the decision-making process. Increasing reliance on this technology brings challenges of unfair treatment, colonized responses from the system, and data governance. Because of historical interaction, tribal communities are the most underrepresented in policy planning and implementation. Indigenous communities suffer from the neglect of tribal sovereignty by the U.S. federal government and limited accessibility and literacy in the digital world. Generative AI systems exacerbate these challenges with insufficient tribal input. However, the negative impact can be alleviated with digital equity and knowledge cocreation. Digital equity emphasizes the importance of tribal knowledge representation, and knowledge cocreation focuses on the collaboration between Indigenous communities and relevant actors in data governance for generative AI systems. This study proposes two research questions to discuss tribal knowledge cocreation in generative AI systems: (1) what are the biases in the system responses from the tribal perspective? (2) what are the potential resolutions for these problems? The findings from in-depth interviews with tribal members in the U.S. indicate that the insufficient articulation of tribal culture, the lack of crucial tribal historical events, and the inappropriate appellation of tribal nations are the primary drawbacks in the system responses. From the Indigenous perspective, tribal oral traditions, native publications and documents, and collaboration with tribal governments can address the problems of generative AI responses. This study contributes to the theory development of digital equity and knowledge cocreation in tribal generative AI system responses. Policy recommendations and future research agendas are included in this research.
This study examines policy interventions and regulation of generative artificial intelligence (AI), focusing on key differences in generative AI policy in the EU, the US, and China. Using a comparative research methodology, the study analyzed the most recent policy documents from these regions through text-mining techniques to assess their key differences in terms of word frequency and specific content. This work highlights the different strategies, goals, and approaches to regulating generative AI across the regions. It found that the EU adopts a more comprehensive and stringent regulation of generative AI, emphasizing regional harmonization and foresight; the U.S. regulation is characterized by pragmatism, closely aligned with industry innovation, and a focus on risk avoidance; while China focuses more on macro-level regulation aimed at promoting innovation and encouraging ecological construction. Participants may be interested in this study because it not only uses up-to-date materials but also employs text-mining methods to present the findings in a clearer way than previous studies. It provides insights into how regulatory policies for generative AI affect the development and practice of digital government. The study sheds light on different countries’ strategies for technology governance, which is crucial for understanding and designing effective digital government policies. In addition, due to the potential of generative AI technologies to deliver public services and drive government transparency, these insights help participants better assess the opportunities and challenges of technological innovation in the digital government space.
This study explores the concept of sharing economy, a domain predominantly investigated in the private sector, and extends its applicability to the realm of digital government. Despite its potential alignment with digital government principles such as co-creation and citizen participation, literature on sharing economy within digital government remains limited. Moreover, this study also highlights the untapped potential of the sharing economy model during crises presenting an avenue for enhanced government responsiveness. Recognizing Facebook's prevalence in digital government initiatives through the years, this study investigates its suitability as a platform for fostering sharing economy in government contexts. Specifically, this paper aims to shed light on how governments and the public sector can take advantage of social media platforms like Facebook in implementing the sharing economy model effectively. Through a two-pronged approach, a comprehensive review of related literature and a focused case study from one city government in the Philippines were conducted. This country was selected for its high sharing attitudes, social media usage, and e-participation providing valuable insights especially for governments sharing similar socio-technological contexts such as those from the Global South and developing economies. Considerations for future policies and future research pathways were also presented.
Artificial intelligence (AI) could drive both positive and negative impacts on society, prompting recent studies to advocate for a more inclusive approach to AI initiatives aimed at amplifying benefits and mitigating drawbacks. In local communities, public libraries are often deemed to have crucial potential in engaging diverse targeted audiences with educational and informational needs. Given this context, this paper aims to investigate the innovative programs, services, and strategies implemented by public libraries with the goal of raising awareness about AI and fostering inclusive civic engagement in AI initiatives in their communities. In order to achieve our goal, we searched libraries' websites and identified 105 AI-related events held by libraries around the US and Canada. We classified these practices under five categories aimed at raising awareness about AI and building competencies related to AI: lectures and podcasts, hands-on workshops, seminars and conversations, exhibitions, and makerspaces. We also acknowledged that most of these initiatives take place in collaboration. However, we also found that there is no particular focus on inclusive AI and/or marginalized communities and that public libraries could therefore expand their role but providing spaces of community participation particularly targeted at individuals with diverse socio-economic, racial, and cultural backgrounds.
In the public sector, digital social innovation (DSI) has emerged as an instance of open innovation in which citizens and other stakeholders use technology to gain new knowledge, develop tools, and form relations and collaborations to address complex societal problems. DSI is particularly relevant to local governments as the agglomeration of social capital, diverse resources, and technology in cities can be leveraged to collectively develop bespoke innovative solutions to pressing urban issues. Despite this potential and importance, the existing research on DSI is nascent, limited, and considered fragmented. To fill these gaps, this systematic literature review presents a synthesis of the interdisciplinary literature on the subject and also offers an evidence-based overview of current practices of DSI in urban contexts. Further, this article identifies relevant research gaps and proposes a research agenda. The findings advance the research on DSI and, in particular, in relation to cities.
With the advancement of artificial intelligence (AI), governments around the world have been exploring possibilities to adopt AI technologies in their operations to improve efficiency and citizen satisfaction. Few studies examine bureaucrats' preferences of using or working with AI in making decisions and delivering public services. Are they supportive of government use of AI? We answer this question by analyzing public officials' opinion data from Taiwan Government Bureaucrats Survey. Our results show that bureaucrats with higher levels of innovativeness favor the idea of adopting AI in their organizations whereas bureaucrats with higher levels of career commitment oppose such adoption.
Estonia has a global reputation of a "digital state" or "e-country". However, despite the success in digital governance, the country has faced challenges in the realm of Open Government Data (OGD) area, with significant advancements in its OGD ecosystem, as reflected in various open data rankings from 2020 and onwards, in the recent years being recognized among "trend-setters". This paper aims to explore the evolution and positioning of Estonia's OGD development, encompassing national and local levels, through an integrated analysis of various indices, primary data from the Estonian OGD portal, and a thorough literature review. The research shows that Estonia has made progress in the national level open data ecosystem, primarily due to improvements in the OGD portal usability and legislation amendments. However, the local level is not as developed, with local governments lagging behind in OGD provision. The literature review highlights the lack of previous research focusing on Estonian and European local open data, emphasizing the need for future studies to explore the barriers and enablers of municipal OGD. This study contributes to an understanding of Estonia's dynamic journey in the OGD landscape, shedding light on both achievements and areas warranting further attention for establishing a sustainable open data ecosystem.
The literature in the field of smart cities shows a continuous emphasis and interest in the topic of big data due to the extensive use of Information and Communication Technologies by public and private institutions within each city. There is undoubtedly value in big data: in data lie insights on the city, its stakeholders, citizens, products, and services. Challenges, though, lie in data's variety, volume, and velocity, but also in managing them, considering the complex interplay between stakeholders inside a city or a country. Another layer of complexity is added when we consider a smart city as a smart destination where the visitor - often an international tourist - becomes an additional stakeholder of a smart city bringing in additional data. Such challenges, though, are even stronger when tourists do not stop at geographical borders: smart destinations become cross-border destinations. While there is a physical border between them, but most importantly, a legal difference in how data should be collected, stored, managed, and re-used [56, 59], data flows do not stop at this border. This complexity has to be managed both by governmental and tourism agencies. However, the literature between eGovernment and tourism is often theoretical in nature, and while it highlights the potential benefits of smart destinations and data-management processes, it does not provide detailed guidelines on how to implement these concepts in practice [41], especially in the context of cross-border smart destinations. With regards to this, not only has the need for guidelines risen to help tourism destinations tackle smart data- and technology-related projects, but also to define how stakeholders can come together to determine data policies and governance in order to create private as well as public value [60]. This paper responds to such a need by presenting the results of a cross-border research project conducted in Switzerland and Italy, where the model of a smart destination's structure proposed by Ivars-Baidal et al. [35] has been applied, and its dimensions have been operationalized in a data-related management project. This allowed the authors to understand how to create public and private value managing data flows in a cross-border context, while also elaborating on the model reflecting on data's dual role as a starting point but also as a central component impacting other dimensions.
