Governments struggle to exercise control over their digitalization efforts, often with many risks and uncertainties. Literature on IT control provides a fragmented understanding of government-specific digitalization areas. This paper systematically identifies and analyzes different areas of IT control through a systematic literature review, resulting in a novel classification. The literature review reveals that the current classifications of IT controls are fragmented, lack coherence, are incomplete, and depend on the research field and the language in which the data was collected. The novel classification presented in the paper focuses not only on technology, but also on the whole domain of digitalization, including the arrangements of organizations and projects, data management, agreements with other organizations, and the achievement of political ambitions and goals. This paper gives insights into the areas of control and argues that more research is needed to understand differences among government IT control approaches.

The EGOV-CeDEM-ePart 2025 conference, for short EGOV 2025, was organized by the IFIP WG 8.5 Information Systems in Public Administration. The conference is dedicated to digital or electronic government, open government, local government (smart cities), smart governance, artificial intelligence (AI), e-democracy, policy informatics, and electronic participation. With a long tradition along its various branches, the EGOV-CeDEM-ePart conference has gained its reputation of being the key and leading conference worldwide in the research domains of digital/electronic, open, and smart government, as well as electronic participation. [...]

Decentralized Autonomous Organizations (DAOs) are one of the most advanced blockchain-based applications, posing many opportunities for new governance models and organizational forms. Over the past decade, the number of DAOs and their assets under management have grown exponentially. DAOs offer many opportunities and advantages for organizations, including internal organizational democratization, external actor participation, transparency, advanced auditability, autonomy and automation of business processes and decisions, higher efficiency, lower cost, increased trust, and new organizational and governance forms. DAOs are being used for a wide range of business purposes, and their various governance elements and choices that can be made provide significant flexibility in their deployment. Although there are many opportunities, DAOs can face multiple (governance) challenges as well. As there are many differences between DAOs as a result of the various choices, one cannot approach a DAO as one organizational form. The choices in governance setup, business purpose, and even the chosen infrastructure can influence the long-term viability and should be made carefully.

Governments are increasingly deploying open data portals and platforms as a technological innovation to empower citizens by providing access to data. Yet, engagement with these portals remains low, suggesting that current approaches may not adequately map the issues surrounding the adoption of these tools. Research on open data has been conducted to overcome the technical and institutional barriers to adopting open data portals and platforms. However, there is a void in the literature about research on the citizens’ motivations that support or inhibit their adoption. This study addresses this gap by drawing on the Self-Concordance Model, a motivational theory that explores the alignment of an individual’s goals and values, to explain citizens’ motivations better. Through an integrative literature review, we conceptualized citizens’ motivational factors, linked them with corresponding barriers, and organized them into a taxonomy that reflects their role across different stages of the adoption process. Our analysis reveals that identified and intrinsic motivations play distinct roles in both pre-adoption and post-adoption phases, suggesting that tailored design strategies targeting these motivations could effectively initiate and sustain citizen engagement. This study advances open data research by connecting motivation and use of motivation theory to map the citizens’ behavioral dynamics underlying their adoption. Our proposed taxonomy provides a foundation for future research into motivation-driven strategies in designing open data portals and platforms’ interventions to increase citizens’ engagement.

Trust is a crucial factor in multi-actor data-sharing initiatives, particularly in sensitive domains like healthcare, where patient privacy, regulatory requirements, and organizational collaboration intersect. However, achieving trust-by-design, creating trust through intentional design choices, is challenging. To address this challenge, this paper investigates how trust frameworks in healthcare data-sharing are designed and how they evolve over time. Central to this inquiry is the conceptualization of “trust anchors”– designable components that provide a foundation for creating trust. Drawing on Technological Innovative Systems theory, this research qualitatively examines two healthcare trust frameworks, each at different lifecycle stages. The case studies reveal how trust anchors contribute to both the development and active management of trust frameworks. The contribution includes a lifecycle approach for trust frameworks and a matrix for categorizing trust anchors, providing guidance for organizations aiming to implement and maintain multi-actor data-sharing frameworks. We find that enforceable trust anchors are more important in the mature phase of a trust frameworks, while in the growing phase, less designable and enforceable trust factors assume a greater role.

Purpose – Although there is much research about Open Government Data (OGD), comprehensive evaluations of OGD policy impacts remain sparse. This study addresses this gap by empirically examining the alignment between OGD policy objectives and real-world outcomes across 337 Chinese municipalities. Design/methodology/approach – A typology of OGD policy objectives is developed and used to evaluate OGD policies and their impact in different geographical regions. Findings – The analysis revealed a hierarchy of outcomes where “technical support” goals yield higher impacts than “innovative value”, whereas the latter is often the goal of OGD initiatives. Regional disparities also emerge, with Eastern cities outperforming traditional industrial areas. Originality/value – These findings underscore that policy design does not guarantee expected outcomes, especially under varying regional contexts. Policy-makers should better address local characteristics and develop targeted policy strategies and effective resource allocation.

Decentralized autonomous organizations (DAOs) are one of the most advanced blockchain-based applications. DAOs offer democratic participation in decision-making for their users, without needing traditional hierarchical reporting and management structures, through the autonomous execution of decisions through smart contracts. These characteristics have driven exponential growth in DAO projects over recent years. Their inherent democratic features make them well-suited for the public sector. Being transparent, DAOs align well with public values, such as direct democracy, privacy, and openness. In the public sector, DAOs provide opportunities to improve the internal democracy of existing government bodies, increase citizen participation, enhance ownership and management of public goods (commons), and support the development of decentralized political parties, some of which are already being experimented with. While DAOs present many opportunities for the public sector, a robust governance design is crucial to mitigate potential risks when using DAOs.

Open data intermediaries are critical for enhancing value generation from open data. However, empirical research on their business model archetypes remains limited. This gap constrains our understanding of the conditions and potential innovations required to perform the roles of open data intermediaries sustainably. To address this gap, we developed a taxonomy and empirically derived business model archetypes based on 190 open data intermediaries. We identified nine archetypes: collaborative open data platforms, premium self-service data delivery, personalized open data services, interactive apps with other complementary products, open data repositories funded by sponsorship, one-stop packages around an (augmented) open data platform/repository, single-purpose apps, interactive apps without complementary products, and open data advocacy. We also described each archetype’s value proposition, value creation, and value capture dimensions. Our findings support further research into the conditions that contribute to the success of open data intermediaries’ business models and the design of new, innovative ones. They also provide business model inspiration for existing and potential open data intermediaries, thereby encouraging greater exploitation of open data value.

Despite the many promises of open data, numerous challenges inhibit its full potential, such as its poor or inconsistent quality, a lack of complementary assets, and the limited skills of data providers and end-users. Open data intermediaries are instrumental in addressing some of these challenges. They provide specialized resources and capabilities to enhance the supply, flow, or use of open data, or strengthen the relationships among various open data ecosystem (ODE) actors. However, in-depth studies on open data intermediation business models are limited, with most having only offered birds-eye views of those business models. This deficiency has limited our knowledge to develop open data intermediation business models that contribute positively to the ODE. In the geospatial domain, Esri is an important open data intermediary, having been involved in such a role since the 1990s. This article unpacks Esri's open data intermediation business model and analyses its current strengths and weaknesses as well as its potential opportunities and threats to the ODE. Finally, this article recommends factors to consider in developing an open data intermediation business model that supports the sustainability of the ODE.

Companies and public agencies who are looking to improve their services can benefit from more data sharing. However, due to regulations and security concerns, data sharing between individuals, businesses and public agencies is complicated. There are many variables to consider in a multi-actor environment where actors with various roles and incentives look for legal and technical certainty. Public and private organizations increasingly acknowledge the need for multi-organizational agreements on data sharing standards. This results in the rise of trust frameworks to guide efforts towards trustworthy data sharing in an interorganizational setting. However, academic literature on trust frameworks is scarce, and we lack a systematic understanding of the factors that constitute trust in a multi-actor data sharing environment. The objective of this paper is to provide a systematic understanding of the antecedents of trust playing a role in trust frameworks. A two-stage approach is followed, starting with a systematic review of antecedents, followed by an empirical inquiry as verification. Our findings indicate a wide range of antecedents - including technological and organizational antecedents - can be considered.

The application of governmental resources for OGD and citizen engagement is often assumed to be interdependent, yet empirical investigation has been lacking. This study examines the reciprocal influence between the use of resources and OGD by quantifying both government resources and citizen engagement within the framework of resource-based theory. The significance of these metrics is determined using the entropy method. Data from 337 Chinese municipalities was gathered to explore the dependency between government resources and citizen engagement in OGD. The findings reveal that while there is a robust interaction between governmental resources and citizen engagement in OGD across 337 Chinese municipalities, the coordination between these elements is generally low, with most areas showing a coordination degree below 0.3. This discrepancy indicates a lack of alignment in the effective use of resources for public engagement. Cities on the eastern coast display moderately better coordination, which can be attributed to their advanced economies and infrastructure. Conversely, regions with lower coordination degrees highlight the urgent need for developing policy interventions to improve the synergy between OGD and citizen engagement.

Deploying scalable Vision Transformer applications on mobile and edge devices is constrained by limited memory and computational resources. Existing model development and deployment strategies include distributed computing and inference methods such as federated learning, split computing, collaborative inference and edge-cloud offloading mechanisms. While these strategies have deployment advantages, they fail to optimize memory usage and processing efficiency, resulting in increased energy consumption. This paper optimizes energy consumption by introducing adaptive model partitioning mechanisms and dynamic scaling methods for ViTs such as EfficientViT and TinyViT, adjusting model complexity based on the available computational resources and operating conditions. We implement energy-efficient strategies that minimize inter-layer communication for distributed machine learning across edge devices, thereby reducing energy consumption from data flow and computation. Our evaluations on a series of benchmark models show improvements, including up to a 32.6% reduction in latency and 16.6% energy savings, while maintaining mean average precision sacrifices within 2.5 to 4.5% of baseline models. These results show that our proposal is a practical approach for improving edge AI sustainability and efficiency.

In response to unprecedented global urbanization, the smart city concept has emerged, leveraging ICT to enhance municipal efficiency and improve the quality of urban life. The concept of smart energy city (SEC) is closely related to smart cities, however, energy system development in a smart city context is often found eluding certain segments of society, which calls for more attention to inclusion in SEC development. In this paper, the research question is: How can inclusion be effectively integrated into a framework of SEC design? A framework is developed comprising three key principles - energy conservation, energy efficiency, and renewable energy. These principles are aligned with collaboration among stakeholders, smart energy solutions applications, and integration of these solutions. The framework is illustrated using two real-world cases of demonstration projects in the City of Amsterdam, the Netherlands. The paper concludes by presenting several strategies for fostering inclusion in SEC development. They pertain to including utilization of the framework as a guideline to promote inclusion, establishing a clear understanding of inclusion, and involving all relevant stakeholders, including citizens' rights from the project's inception, and fostering transparency regarding the objectives, interests, and individual stakeholders' value.

Driving assist applications and connected autonomous vehicle systems are supported using AI models and algorithms, which process and analyze heavy data volumes. High-performance computing units and large memory systems support these models, algorithms, and applications, which results in additional onboard energy consumption. The current trend is also towards full electrification of vehicles and increasing connectivity in the vehicular ecosystem to support collaborative and distributed applications using vehicle-edge-cloud computing. However, with the increased focus on model performance and improving the accuracy of these models and applications, the issue of high-performance computing requirements and resulting energy consumption are overlooked. The problem becomes more challenging and complex for resource-constrained edge devices, which are battery-dependent and have limited memory and computing power. This paper proposes components for an adaptive framework to reduce energy consumption by balancing model accuracy. The contributions include proposing and integrating model partition mechanisms, adaptive deployment across edge devices and approximation strategies for the models. By integrating these components, this framework supports energy-aware development across various platforms. The approach offers a sustainable method for computing and communication-oriented applications within the vehicular ecosystem.

This is the 11th edition of the workshop series labeled “Web and the City - The Web and Smart Cities”, which started back in Florence in 2015 and kept on taking place every year in conjunction with the WWW conference series. Last year the workshop was held in Singapore. Each year the focus of the workshop is actualized, and this year the workshop focuses on the Responsible web and Responsible AI in cities and communities. In the era of digital twinning, AI, and the metaverse (so-called citiverse for cities), and under the UN 2030 Agenda for sustainable growth and resilience, cities are being transformed into virtual spaces that enable service automation and value generation to their communities and enterprises. Moreover, AI and web intelligence generate new types of opportunities, including generating automated transactions in these virtual spaces, which must preserve human-perceived consequences, fairness, accountability, transparency, and ethics. This workshop aims to focus on how the Web transforms cities into responsibly intelligent virtual environments.

The metaverse is still in its early stages, and most prototype metaverse platforms fail to fulfill their potential and meet users’ demands. Numerous studies have focused on the success of metaverse applications. However, there has been limited research on the failure of the metaverse services. Based on digital service failure model (DSFM), uses and gratification theory (U&G), and organizational failure diagnosis model (OFDM), an integrated model to investigate the influences of different failure types on citizens’ satisfaction with meta-government services is developed. The model was tested using a survey of 402 responses in China, which were analyzed using a hybrid structural equation modeling artificial neural network (SEM-ANN) approach. The findings indicated that organizational failure exerts the most powerful effect on citizens’ satisfaction. Moreover, information, service, system, and psychological needs failure, negatively influence citizens’ satisfaction. In addition, technology anxiety can weaken the influences of information, system, psychological needs, and organizational failures on citizens’ satisfaction while enhancing the relationship between service failure and citizens’ satisfaction. This research contributes initial and original insights into the failure types of meta-government services and their impacts, addressing a significant gap in the literature.

Understanding the landscape of privacy protection in governmental systems is crucial for ensuring the trustworthiness of public services and safeguarding citizens' sensitive data from breaches or misuse. Systematic mapping and synthesis of previous research can help identify existing privacy-preserving techniques, assess their effectiveness, and highlight areas for improvement, offering valuable insights for policymakers and practitioners. We aim to conduct a systematic literature review (SLR) of privacy-preserving tools and technologies, focusing on their adoption and governments' challenges. This study also uncovers emerging trends and future research directions, contributing to developing more robust privacy strategies tailored to government needs.Given its extensive reach and government-centric methodology, this evaluation distinguishes itself from previous research. Our work methodically synthesizes privacy-preserving tools and technologies from the distinct perspective of government roles, in contrast to previous assessments that concentrate narrowly on certain technologies or areas. Our findings offer a synthesis of the government's diverse roles in privacy preservation - regulator, enforcer, user, and service provider - and address existing concerns and key privacy-related technologies. Finally, we identify significant research opportunities, such as improving privacy-preserving mechanisms to strengthen the integrity of public services and mitigate the risks of data breaches and misuse.

Vulnerable road users (VRUs), including pedestrians, cyclists, and motorcyclists, account for approximately 50% of road traffic fatalities globally, as per the World Health Organization. In these scenarios, the accuracy and fairness of perception applications used in autonomous driving become critical to reduce such risks. For machine learning models, performing object classification and detection tasks, the focus has been on improving accuracy and enhancing model performance metrics; however, issues such as biases inherited in models, statistical imbalances and disparities within the datasets are often overlooked. Our research addresses these issues by exploring class imbalances among vulnerable road users by focusing on class distribution analysis, evaluating model performance, and bias impact assessment. Using popular CNN models and Vision Transformers (ViTs) with the nuScenes dataset, our performance evaluation shows detection disparities for underrepresented classes. Compared to related work, we focus on metric-specific and cost-sensitive learning for model optimization and bias mitigation, which includes data augmentation and resampling. Using the proposed mitigation approaches, we see improvement in IoU(%) and NDS(%) metrics from 71.3 to 75.6 and 80.6 to 83.7 for the CNN model. Similarly, for ViT, we observe improvement in IoU and NDS metrics from 74.9 to 79.2 and 83.8 to 87.1. This research contributes to developing reliable models while addressing inclusiveness for minority classes in datasets. Code can be accessed at: BiasDet.

Gamification may foster citizen relationships with the government but can also result in a disinterest in participation. In the context of digital government, we do not know which game elements specifically contribute to citizen engagement. In this paper, we conduct an integrative study, drawing from existing literature on citizen engagement through gamification. We examined the citizens’ motivation to engage with the government, linked it with game elements explored in the literature, and finally exhibited how these elements could support or inhibit citizens’ motivation. Using self-concordance and civic engagement models, we investigated gamification focusing on individual experiences, both personally and as citizens. The findings of this research show a link of static game elements with external and introjected forms of motivation. In contrast, dynamic game elements seem more aligned with intrinsic and identified motivation. We develop a taxonomy outlining these relationships, enabling further research on game elements and their impact on citizen engagement over time.