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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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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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

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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Ensuring the secure provision of data and services using critical information infrastructures amidst the evolving technology landscape is a crucial yet recurrent task. However, these infrastructures can become vulnerable due to developments in quantum computing and modifying the infrastructures with quantum-safe (QS) technology is unlike regular control and maintenance. Organizations need to modify their cryptographic layers, which act as the fundamental building blocks of infrastructures. For organizations, many uncertainties pose challenges across technological, organizational and ecosystem areas. While QS technology is new and not yet available for implementation and adoption, changes in critical information infrastructures require collaboration among multiple public and private organizations spanning industries and borders. By understanding the roles, organizations may better understand what should be done for QS transitions. Until now, there has been no academic research examining the roles that government could or should play in QS transitions. This paper reveals 12 different roles, showing the diversity and breadth of actions needed. While there are many possible roles that still need to be allocated for coordinated efforts, there is a high reliance on the government, and organizations are waiting for and expecting governments to take more active roles in QS transitions. The results also signals that QS transition research is at its early stage with a clear governance void and lack of collective urgency in the ecosystem.

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

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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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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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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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A metaverse is a three-dimensional virtual space (3D VS) where businesses and individuals worldwide can engage, interact, communicate, transact, and exchange information in real-time through an immersive and collaborative platform. These interactions can create complex relationships influenced by the decision-making processes of businesses. Such complexity can lead to challenges in maintaining relationships, ensuring exclusiveness, preventing misuse, and addressing other ethical issues. Therefore, this study aims to identify ethical principles within the metaverse to guide decision-making and maintain complex relationships between users and businesses. Both qualitative and quantitative data were collected for analysis, and simple random sampling was employed for primary data collection. The empirical analysis was conducted using a mixed-method approach. The study identified four ethical principles that guide complex relationships within the metaverse: business benefit evaluation, fairness, explainability, and reliability principles. These principles positively influence decision-making, which, in turn, positively affects the maintenance of complex relationships within 3D VS.

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Digital government comprises all means to enable governments to interact with their constituents digitally. The metaverse provides a virtual reality environment where various activities can be carried out without physically visiting the places of interest, including the public authorities. Yet, how governments can use the metaverse is unknown. This paper aims to extend the understanding of the metaverse architecture requirements and their suitability for digital public services provision. We used the systematic literature review, experts' assessment using the Delphi method, and quantitative analysis to attain this goal. Our research contributes to the literature by eliciting the structure and composition of the functional and non-functional requirements. The contributions include (1) identification and classification of 50 functional and 16 non-functional metaverse-related architecture requirements, (2) determination and relevancy of 15 most important functional and 6 non-functional requirements for digital public services provision, and (3) suitability assessment of the 21 services recommended for provision in the EU's metaverse platform with the highest potential to attract users. These findings show that governments pose unique requirements on the metaverse. Not all types of services are suitable for providing in the metaverse. Those focused on empowering citizens and helping them to develop are most important.

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The publication of Open Government Data (OGD) is expected to deliver economic value creation through innovation. In any OGD-based value creation, it is essential to formulate a value proposition (VP). However, previous literature has devoted little attention to the tools that support the formulation of OGD-based VP, and none has evaluated the performance of such tools with OGD infomediaries. In this article, we evaluate and compare the Business Model Canvas (BMC) and the Open Data Canvas (ODC) performance in supporting the formulation of VP. Questionnaire feedback was obtained from infomediaries who formulated OGD-based VP using the BMC and the ODC. The results show a superiority of either the BMC or the ODC in terms of perceived usefulness, depending on the focus of the feasibility evaluation of the VP. The ODC provides OGD-specific guidance on evaluating technical feasibility while the BMC covers economic feasibility more extensively. We recommend investigating the emerging field of OGD-based VP formulation tools by replicating our research with other types of infomediaries, in other settings, and completing it with qualitative insights.@en

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT's capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT's use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts.@en