Vision transformer (ViTs) models have shown higher accuracy, robustness and large volume data processing ability, creating new baselines and references for perception tasks. However, these advantages require large memory and high-performance processors and computing units, which makes model adaptability and deployment challenging within resource-constrained environments such as memory-restricted and battery-powered edge devices. This paper addresses the model deployment challenges by proposing a model approximation approach VI-ViT, for edge deployment using variational inference with mixed precision for processing multi-modalities, such as point clouds and images. Our experimental evaluation on the nuScenes and Waymo datasets show up to 37% and 31% reduction in model parameters and Flops while maintaining a mean average precision of 70.5 compared to 74.8 of the baseline model. This work presents a practical deployment approach for approximating and optimizing Vision Transformers for edge AI applications by balancing model metrics such as parameters, flops, latency, energy consumption, and accuracy, which can easily be adapted to other transformer models and datasets.

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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.

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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.

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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.

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Government intervention is imperative in the mixed economic system due to market failures, imperfection, pure public goods, and economic externalities to stabilize the economy. We examine the impact of public debt on economic growth. As the role of quality of governance (QoG) is disputed in prior studies, we examine the direct and moderating role of QoG in the context of high-income countries. Whereas analyses are often based on static models along with conventional quantile regression methods not considering the scale and location, we use the method of moment quantile regression (MMQR) by considering the quantile in both scale and location based on heterogeneous panel data from 1990 to 2020. Our empirical investigation shows that public debt promotes economic growth in lower to upper-medium quantiles but is ineffective in top quantiles. The moderating role of QoG on the debt-growth nexus is counterproductive in lower quantiles and insignificant in upper quantiles, which could be the rationale for the tight QoG rules and regulations. The findings also indicate that the effectiveness of the QoG on public debt is crucial for economic growth in high-income countries, while large public debt and too strict rules and regulations of QoG often slow down the growth process in high-income countries. As policy recommendations, governments should adopt prudent public debt management strategies to balance growth stimulation with the avoidance of excessive debt accumulation. Besides, a moderate QoG framework can be prioritized to effectively moderate the relationship between public debt and economic growth to foster sustainable growth trajectories. Graphical Abstract: (Figure presented.).

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A number of government agencies have started deploying the Metaverse to connect better with their constituents. The Metaverse provides a rich interaction environment and has the potential to engage with, especially, the younger generation. However, the Metaverse's potential impact on the government sector has been given limited attention. This discussion paper aims to fill this void by reviewing the state of the art, analyzing possible roles of the Metaverse for governments and providing research directions. We found six facilitators and nine barriers and risks. The Metaverse offers much more than a virtual presence or copy of the physical world; significant transformations are needed in government to reap the benefits. Given the evolution of the Metaverse, government presence also needs to evolve, and different governments make different decisions about their Metaverse presence. We recommend more research into the nature, use, applications, transformations, and implications of the Metaverse on government functioning.

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Machine learning (ML) is reshaping customer service, tackling the growing complexity and volume of customer requests. This article investigates the effects of ML on perceived service quality (PSQ) across various customer service measures within an organizational context. Utilizing a quasiexperimental design, we analyzed 131 978 service requests submitted to the service desk of a large enterprise software organization. Over a 2-year period, these requests were made by 1252 organizations and were associated with 85 654 predictions and 19 720 returned PSQ postservice questionnaires. Our regularized logistic regression model aimed to categorize service requests into 56 categories and dispatch them into one of nine assignment groups to enhance resolution efficiency. Contrary to expectations, the overall PSQ did not significantly improve, while five specific metrics, such as time to resolve and first-time resolution, improved. This may be attributed to the increase in the first personal response time. The article highlights the complexities of implementing ML-based classification and underscores the importance of organizational structure. We found that expert groups prioritizing accurate problem-solving over quick responses led to an increase in the response time for incoming service requests. Theoretical contributions include an understanding of how classification in customer service affects PSQ, offers practical tactics to counteract negative impressions, and sets the groundwork for future article on ML in customer service management, despite the limitations, such as the potential influence of external factors and the study's generalizability.

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The digital government research community has developed several publication channels, including journals and conference series to evaluate, refine, and present the outcomes of its work to fellow researchers and the world. Developing and sustaining such channels requires institutional backing with sufficient financial, organizational, and communication capacity. Existing publication channels implement diverse institutional arrangements from commercial publishers and universities to international and non-profit organizations. However, besides institutional support, a key role in operating such channels are journal editors-in-chief and conference program chairs, who link the channels to the research community that supplies them with steady streams of research, and ensure that the channels maintain their relevance to the changing digital government field, ensure the quality of accepted publications, and contribute to the advancement of the field. This paper examines the evolving landscape of publication channels for digital government research, institutional arrangements for developing and sustaining such channels, and the role of the guardians of such channels – editors-in-chief and program chairs. The paper provides the context for a discussion among such guardians on the insights for publishing digital government research.

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Open government and open (government) data are seen as tools to create new opportunities, eliminate or at least reduce information inequalities and improve public services. More than a decade of these efforts has provided much experience, practices, and perspectives to learn how to better deal with them. This paper focuses on benchmarking of open data initiatives over the years and attempts to identify patterns observed among European countries that could lead to disparities in the development, growth, and sustainability of open data ecosystems. To do this, we studied benchmarks and indices published over the last years (57 editions of 8 artifacts) and conducted a comparative case study of eight European countries, identifying patterns among them considering different potentially relevant contexts such as e-government, open government data, open data indices and rankings, and others relevant for the country under consideration. Using a Delphi method, we reached a consensus within a panel of experts and validated a final list of 94 patterns, including their frequency of occurrence among studied countries and their effects on the respective countries. Finally, we took a closer look at the developments in identified contexts over the years and defined 21 recommendations for more resilient and sustainable open government data initiatives and ecosystems and future steps in this area.@en

Open Government Data (OGD) has been considered as a potent instrument for value creation and innovation by a range of stakeholders. Given that individual ingenuity is a function of individual and environmental factors, it is important to understand how the OGD adoption and usage is a factor of creative performance behaviors (CPB), viz., Problem Identification (PI), Information Search (IS), Idea Generation (IG) and Idea Promotion (IP) as well as creative self-efficacy (CSE). Invoking the adapted Unified Theory of Acceptance and Use of Technology (UTAUT) constructs alongside the moderating effects of CPB and CSE constructs and also gender, the present study seeks to underline the behavioural intention towards OGD adoption and usage among 362 undergraduate and postgraduate university students in India. The guiding research question is: “Is there any difference among the males and females in terms of their OGD adoption and usage as far as their creative propensities are concerned?” Findings from the PLS-SEM (Partial Least Squares-Structural Equation Modeling) procedures show that there are gender differences across the CPB and CSE constructs. The study's contribution lies in furthering our understanding of OGD adoption and use with the additional determinants of creativity literature.

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Artificial Intelligence (AI) is increasingly adopted by public sector organizations to provide better public services and to transform their internal processes. AI is now considered a key enabler for digital innovation and transformation in the public sector. However, AI is still relatively a new research area in the field of digital government. The term, AI, captures a wide range of technologies, techniques, and tools such as machine/deep learning, natural language processing, robotics, computer vision, and more recently Generative AI. While these AI technologies afford different applications and benefits in the gov- ernment context, they also create social, ethical, and legal challenges. These challenges require solutions combining both technical (e.g., data and algorithmic solutions to minimize bias) and institutional (e.g., governance structures and processes) mechanisms. The special issue is a collection of articles that contribute to a better understanding of the issues associated with AI deployment in different areas of government operations. They cover AI applications in the areas of emergency re- sponse, policy analysis, public bids, and citizen participation. The contributions also address the challenge of realizing a legal transparency regime for AI in government and the effect of AI in bureaucratic decision-making.

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Chatbots are radically redefining the customer service landscape. With the advent of AI-enabled chatbots, like ChatGPT, organizations are adopting chatbots to provide better customer services; however, the user experience has been given less attention. Building on IS success model and cognitive absorption theory, we posit that system and user characteristics enhance cognitive absorption amongst users, such that the relationship varies between anthropomorphic (e.g., human-like) and non-anthropomorphic chatbots. We undertook a cross-sectional comparative study, which was analyzed using PLS-SEM and fsQCA. Where PLS-SEM provided limited inferential insights about the differences between anthropomorphic and non-anthropomorphic chatbots, the FsQCA analysis resulted in three configurations of attributes for non-anthropomorphic and two configurations for anthropomorphic chatbots, which lead to higher cognitive absorption. The findings extend the existing literature, suggesting that anthropomorphic and non-anthropomorphic chatbots impact cognitive absorption through separate system and user characteristics configurations.

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Model partitioning is a promising solution to reduce the high computation load and transmission of high-volume data. Within the scope of Edge AI, the fundamentals of model partitioning involve splitting the model for local computing at the edge and offloading heavy computation tasks to the cloud or server. This approach benefits scenarios with limited computing and battery capacity with low latency requirements, such as connected autonomous vehicles. However, while model partitioning offers advantages in reducing the onboard computation, memory requirements and inference time, it also introduces challenges such as increased energy consumption for partitioned computations and overhead for transferring partitioned data/model. In this work, we explore hybrid model partitioning to optimize computational and communication energy consumption. Our results provide an initial analysis of the tradeoff between energy and accuracy, focusing on the energy-aware model partitioning for future Edge AI applications.@en

The digitalization of public administrations can reduce corruption, but many efforts fail. Although the relationship between digitalization and corruption has been investigated, how corruption can be reduced in practice is given hardly any attention. Therefore, we take a different approach by investigating an in-depth case study using documents and interviewing civil servants and citizens. This study found technical and organizational issues blocking the reduction of corruption. Despite encountering technical challenges, most informants agreed that automating administrative processes and making each process step transparent resulted in less corruption. Separating concerns by having different officers conduct each process reduced corruption on the condition that direct communication between immigration officers and passport applicants was blocked. The present study concludes that organizational and technological changes should go hand in hand to reduce corruption. Specifically, the separation of concerns at the organizational and system levels is needed to prevent persons from collaborating and misusing the system. Also, regulatory modifications and suitable governance structures are necessary.

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Achieving impact from academic research is a challenging, complex, multifaceted, and interconnected topic with a number of competing priorities and key performance indicators driving the extent and reach of meaningful and measurable benefits from research. Academic researchers are incentivised to publish their research in high-ranking journals and academic conferences but also to demonstrate the impact of their outputs through metrics such as citation counts, altmetrics, policy and practice impacts, and demonstrable institutional decision-making influence. However, academic research has been criticized for: its theoretical emphasis, high degree of complexity, jargon-heavy language, disconnect from industry and societal needs, overly complex and lengthy publishing timeframe, and misalignment between academic and industry objectives. Initiatives such as collaborative research projects and technology transfer offices have attempted to deliver meaningful impact, but significant barriers remain in the identification and evaluation of tangible impact from academic research. This editorial focusses on these aspects to deliver a multi-expert perspective on impact by developing an agenda to deliver more meaningful and demonstrable change to how “impact” can be conceptualized and measured to better align with the aims of academia, industry, and wider society. We present the 4D model - Design, Deliver, Disseminate, and Demonstrate - to provide a structured approach for academia to better align research endeavors with practice and deliver meaningful, tangible benefits to stakeholders.

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Acceptance by customers is key to the success of shared autonomous vehicles (SAVs). However, only a small group of early technology-savvy customers currently use such vehicles, while the general population does not. Based on the Unified Theory of Acceptance and Use of Technology, Theory of Perceived Risk, and perceived threat of unemployment combined with knowledge of automated vehicles, this research develops an integrated model to investigate the determinants behind the intention to use fully automated taxis. Furthermore, it tested the differences between gender. Through the analysis of 539 samples, the findings showed that performance expectancy, effort expectancy, social influence, and knowledge of automated vehicles positively influence acceptance intention, while perceived safety risk and the perceived threat of unemployment were negatively related to behavioral intention. Moreover, effort expectancy, social influence, and perceived safety risk showed greater influence on females, while knowledge of automated vehicles exerted stronger effects on males.

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This is the 10^th edition of the workshop series labeled “AW4City – Web Applications 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 Austin, Texas. The workshop series aims to investigate the Web and Web applications’ role in establishing smart city (SC) promises. The workshop series aim to investigate the role of the Web and of Web applications in SC growth. This year, the workshop focuses on the new era of the Web and web intelligence in cities and communities. In the era of digital twinning and metaverse (so-called citiverse for cities), and under the UN 2030 Agenda for sustainable growth, cities are being transformed into virtual spaces that generate new types of value and new experiences to their citizens and enterprises that can enhance living and offer new opportunities for economic growth. Moreover, AI and web intelligence generate new types of automated transactions in these virtual spaces, while they can utilize data spaces and standardization for optimal data flow. This workshop aims to demonstrate how the Web transforms cities into new virtual environments.

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By utilizing the properties of quantum mechanics, quantum computers have the potential to factor a key pair of a large prime number and break some of the core cryptographic primitives that most information infrastructures depend on. This means that today's widely used cryptographic algorithms can soon become unsafe and need to be modified with quantum-safe (QS) cryptography. While much work is still needed in developing QS cryptographic algorithms, the institutional, organizational, and policy aspects of transitioning the current infrastructures have received less attention. This paper provides an empirical analysis of QS transition challenges and policy recommendations for moving to a QS situation. We analyzed the data collected through interviews with experts and practitioners from the Dutch government. The results reveal that institutional, organizational and policy aspects of QS transitions are interconnected, and solutions for QS transitions are scattered. Consequently, organizations may face a Catch-22 loop without further actionable approaches and planning for QS transitions.

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