Organizations increasingly use Generative Artificial Intelligence (AI) to create strategic documents, legislation, and recommendations to support decision-making. Many current AI initiatives are technology-deterministic, whereas technology co-evolves with the social environment, resulting in new applications and situations. This paper presents a novel view of AI governance by organizations from the perspective of complex adaptive systems (CASs). AI is conceptualized as a socio-technological and adaptive system in which people, policies, systems, data, AI, processes, and other elements co-evolve. The CAS lens draws attention to focusing AI governance on the entire organization, taking an outward perspective and considering public values and societal concerns. Although there is no shortage of AI governance instruments, they differ in their effectiveness, and combinations of appropriate mechanisms should be selected to deal with AI's evolving nature and complexity. A major challenge is that no responsibility, and therefore accountability, is taken due to the lack of understanding of the full socio-technological CAS. As such, joint accountability is needed in which involved parties work together.

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.

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.

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.

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.

Open data can be used to understand societal issues and provide accountability, in line with the goals of open government. Open data hackathons and game jams are events where citizens gather to reuse open data and address social issues. We lack an overview of open data hackathons and game jams regarding capturing the events’ purpose and resulting prototypes. To address this gap, we performed a systematic literature review and categorized the events based on their orientation, types of prototypes produced, and data collection methods used to record them. The majority of studies in our review are open data hackathons, with participants engaged in the development of an app, service, or web portal. Despite the promising ways in which games can express social issues, we found only one case of an open data game jam. Hence, we recommend further research into the reuse of open data through game jams and participatory game-making.

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.

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.

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.

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.

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.

When implementing and adopting new technologies, knowing the level of organizational readiness is crucial. By assessing the readiness levels, organizations can focus on areas with low readiness levels and prepare for the change processes. Due to the increasing vulnerabilities presented by the advancement of quantum computing technology, today’s widely used cryptographic algorithms and encryption methods need to be modified with quantum-safe (QS) ones. However, organizations currently lack tools to understand the complexity of implementing and adopting QS technology, and there is no readiness assessment model available in the context of QS transition. By including different dimensions that organizations should consider when implementing and adopting QS technology, we develop an organizational readiness assessment model for QS transition. The dimensions used in the model include collaboration, governance, policy & regulation, awareness, QS solution standards, hybrid QS solutions, cryptographic agility strategies and knowledge on QS transition. While the organizational readiness assessment model with different dimensions shows the complexity involved in implementing and adopting QS technology, it acts as a guidance tool for organizations to navigate and prepare for uncertainties surrounding QS transition.

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.

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.

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.

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.

Open data promises various benefits, including stimulating innovation, improving transparency and public decision-making, and enhancing the reproducibility of scientific research. Nevertheless, numerous studies have highlighted myriad challenges related to preparing, disseminating, processing, and reusing open data, with newer studies revealing similar issues to those identified a decade prior. Several researchers have proposed the open data ecosystem (ODE) as a lens for studying and devising interventions to address these issues. Since actors in the ecosystem are individually and collectively impacted by the sustainability of the ecosystem, all have a role in tackling the challenges in the ODE. This paper asks what the contributions of open data intermediaries may be in addressing these challenges. Open data intermediaries are third-party actors providing specialized resources and capabilities to (i) enhance the supply, flow, and/or use of open data and/or (ii) strengthen the relationships among various open data stakeholders. They are critical in ensuring the flow of resources within the ODE. Through semi-structured interviews and a validation exercise in the European Union context, this study explores the potential contribution of open data intermediaries and the specific ODE challenges they may address. This study identified 20 potential contributions, addressing 27 challenges. The findings of this study pave the way for further inquiry into the internal incentives (viable business models) and external incentives (policies and regulations) to direct the contributions of open data intermediaries toward addressing challenges in the ODE.

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.

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.