Design science research (DSR) has been advocated for combining relevance and rigor and is socio-technical by nature. Yet most articles in the digital government field focus on the resulting design artifacts, presenting only methodologies and evaluations, and generating limited actionable, generalizable design knowledge. This editorial analyzes multiple approaches to advancing the development of prescriptive design principles and policy statements. The generation and rationale of prescriptive knowledge are discussed, along with various forms of design knowledge evaluation, to facilitate its connection to specific contexts. Three foundational propositions are presented to identify orienting strategies for developing prescriptive knowledge, explain its underlying rationale, and evaluate design knowledge. An integrative framework for generating and grounding prescriptive knowledge in line with the classical DSR paradigm is developed. This framework provides the basis for researchers to make generalizable knowledge contributions to the field using DSR.

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.

Existing research on open data engagement has predominantly focused on the supply-side indicators or on the subjective demand-side evaluations, while objective-based analysis of the demand-side engagement remains underexplored. Drawing on the platform ecosystem perspective, this paper takes a different approach by examining open data platform engagement as an indicator of value creation through observable behavioral interactions, thereby positioning our study on the demand-side and within an objective-based evaluation. Through interviews with 15 open data platform stakeholders, we identify opportunities to measure engagement using behavioral indicators derived from platform use. Building on these findings, we develop a multi-layer framework of behavioral indicators to measure engagement with open data platforms. The framework consists of four layers which includes: system-level indicators, behavioral metrics, interactive signals, and contextual factors. We present a proof-of-concept implementation that operationalizes the proposed framework using platform logs, providing guidance for open data platform designers and policymakers to evaluate engagement. The paper contributes to the open data evaluation literature by reconceptualizing engagement as an observable, value-creating behavior in open data platforms. In this way, the demand-side is taken into account, and the actual behavior is measured, which data can complement the subjective data. Overall, the study demonstrates how integrating objective measures with subjective evaluation can support a more comprehensive and value-oriented engagement measurement of open data platforms.

Using audit analytics influences all facets of internal audit activities. Audit analytics use is transforming internal audit processes, structures, and relationships with its counterparts. However, empirical research in AA literature often focuses on individual projects and technology adoption issues. This paper fills this void by presenting empirical accounts of how to embark on an AA implementation initiative at the organizational level. Furthermore, we confront our observation with the views and experiences of professionals in the broader internal audit community to translate the exhibited practices into broader contexts. The findings from the case suggest that AA implementation should be approached from the whole-of-organization perspective instead of a fragmented and project-based view, combining formal and informal approaches, and equipped with AA implementation indicators and guidelines. Further studies are fruitful for affirming or expanding this paper’s observations and suggestions and extending its generalizability.

AI-enabled mass deliberation is a new attempt to revive deliberative democracy with digital technology. AI applications such as summarisation and automated moderation provide opportunities to scale up deliberative processes that currently are constrained to discussions in small groups. Notwithstanding, the AI push underestimates the difficulties in sustainably integrating digital technologies (including generative AI) into sedimented organisational and normative practices of decision-making. This paper aims to understand the gap between institutionalization of AI-enabled deliberation as a conceptual notion and its effectuation in practice. For this, we collected and synthesised well-established conditions and requirements for institutionalisation through co-creation sessions with experts. Thereafter, we asked practitioners to assess these conditions and requirements on importance, contribution to institutionalisation, feasibility, and easiness of implementation. Our research identifies five tensions in practice that contribute to the gap between the conceptual understanding of institutionalisation and practitioners’ perceptions of whether it is achievable in practice. These tensions need to be overcome for institutionalisation to be accomplished.

Quantum computers threaten modern cryptography by efficiently factoring large primes, which could break widely used public-key cryptosystems for communication between government, companies, and individuals. This necessitates that societies transition to quantum-safe (QS) cryptography, ensuring secure communication, leading organizations to establish definitive adoption timelines. However, the adoption of QS solutions in both public and private sectors has received limited attention. Based on the Technology-Organization-Environment-Human (TOEH) framework, this study examines the determinants and configuration pathways underlying QS adoption. This study integrates topic modeling with Qualitative Comparative Analysis (QCA) to examine the mechanisms underlying the adoption of QS solutions. Using Latent Dirichlet Allocation (LDA) on a corpus of 100,368 words from case reports across diverse sectors in China, this study identifies thematic patterns in QS adoption. Subsequently, QCA is employed to analyze the configuration pathways driving adoption. Based on empirical data from 31 adoption cases, the research yields three insights: First, Technological Innovation, Technology Investment, Organizational Readiness, Industry Environment, Risk Environment, Policy Environment, Personal Networks, and Technical Awareness are determinants of the implementation of QS solutions. Second, no single condition is necessary for achieving high-level adoption. Instead, it emerges from three core configuration paths: 1) ecosystem adoption driven by industries 2) government policy and 3) self-initiated. Third, Personal Networks emerge as the core or peripheral condition across all configurations. These findings show the complexity of adoption, offer novel insights for adopting QS implementation mechanisms, and provide actionable strategic guidance for accelerating QS adoption across public and private sectors.

Despite the growing use of AI in the public sector, there is limited empirical evidence on its implementation. Prior research on government readiness for AI has predominantly focused on the adoption stage from the viewpoint of IT or public managers, often yielding high-level insights and one-sided narratives. AI implementation goes beyond adoption as it involves shaping, integrating, and routinizing AI applications that are often overlooked in discussions about AI adoption. This study examines how government readiness for predictive AI implementation involving multiple stakeholders can be evaluated. It develops a framework for evaluating government readiness for predictive AI implementation by analyzing three Indonesian government use cases and one Dutch government case. The framework covers the socio-technical factors of AI and enables more nuanced analysis by involving perspectives of: 1) those of the stakeholders responsible for the development, management, and governance of AI innovation (which, in short, we call AI developers), and 2) those of the AI users. From developers’ perspectives, the framework examines readiness in terms of resource availability, the organization’s capability to change, and governance, while from users’ perspectives, readiness is evaluated based on the perceived performance and impact of the deployed AI systems. The framework also indicates the influence of AI characteristics and individual readiness on organizational-level implementation. The scientific contribution is to extend the AI readiness model in government beyond adoption, addressing multiple stakeholders’ perspectives on implementation. This framework is expected to guide public organizations in reflecting on their AI implementation and make iterative improvements to ensure responsible AI.

PurposeAlthough 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/approachA typology of OGD policy objectives is developed and used to evaluate OGD policies and their impact in different geographical regions.FindingsThe 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/valueThese 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.

Central government policy departments need to collaborate with executive agencies to provide IT-based services to society. These collaborations are complicated by the fact that the executive agencies are controlled by different ministerial hierarchies, resulting in conflicting interests. Yet, a detailed analysis of these challenges is lacking in the literature. In this paper, we analyze the challenges at several ministries through interviews focused on specific cases as well as interviews focused on expert knowledge. We identified the challenge themes and compared them with the literature, finding several new challenges in practice, including restricted procurement, a Goldilocks zone for escalation, the inability to hold an agent ultimately accountable, and the low priority the central government policy departments give to the implementation. Paradoxically, central government policy departments, as principals, are less powerful and highly dependent on executive agencies instead of the other way around, as suggested by principal-agent theory. These findings imply that there is a need for new governance mechanisms able to deal with all the challenges encountered. The overview of the challenges can serve as a sound foundation for conducting further research into cross-ministerial governance.

Implementing artificial intelligence (AI) in public settings requires a fundamental transformation of various social and technical aspects within public administration. However, the transformative efforts required for AI integration and use in government remain underexplored. This study introduces the concept of 'AI-augmented government transformation,' building on sociomateriality and sociotechnical theory, and develops a theoretical framework to explore this phenomenon. By applying this framework and drawing insights from expert interviews, we identify the strategic shifts and socio-technical adaptations essential for integrating AI into public administrations. Our analysis highlights the importance of opening the 'black box' of AI to gain a deep understanding of its underlying technologies and their materialities.The findings reveal complex interdependencies between AI materiality and the social and technical systems that public administrations must navigate. Specifically, AI, as a novel materiality, introduces new organizational dynamics, enhances employee capabilities, and alters operational routines and practices. These changes complement technical ones, such as upgrades and advancements in data collection and processing. By investigating the complexities of AI-augmented government transformation, this research offers novel and practical insights for policymakers and practitioners navigating the challenges and opportunities of AI integration.

AI is transforming the healthcare domain and is increasingly helping practitioners to make health-related decisions. Therefore, accountability becomes a crucial concern for critical AI-driven decisions. Although regulatory bodies, such as the EU commission, provide guidelines, they are highlevel and focus on the ”what” that should be done and less on the ”how”, creating a knowledge gap for actors. Through an extensive analysis, we found that the term accountability is perceived and dealt with in many different ways, depending on the actor's expertise and domain of work. With increasing concerns about AI accountability issues and the ambiguity around this term, this paper bridges the gap between the ”what” and ”how” of AI accountability, specifically for AI systems in healthcare. We do this by analysing the concept of accountability, formulating an accountability framework, and providing a three-tier structure for handling various accountability mechanisms. Our accountability framework positions the regulations of healthcare AI systems and the mechanisms adopted by the actors under a consistent accountability regime. Moreover, the three-tier structure guides the actors of the healthcare AI system to categorise the mechanisms based on their conduct. Through our framework, we advocate that decision-making in healthcare AI holds shared dependencies, where accountability should be dealt with jointly and should foster collaborations. We highlight the role of explainability in instigating communication and information sharing between the actors to further facilitate the collaborative process.

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.

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.

Despite their pivotal role in promoting transparency, open data portals often struggle to engage citizens, functioning instead as static ‘data graveyards’. While external activities, such as hackathons, can raise awareness, they do not directly cultivate sustained engagement within the portals. One promising approach to leverage citizens’ engagement motivation is the integration of game elements to transform passive data access into interactive gamified experiences. However, despite its potential, there is limited research on gamified citizens’ motivation to engage with open data portals. This paper examines how static and dynamic game elements are implemented across 31 open data portals. Lastly, we use the Self-Concordance Model to discuss the alignment between motivation, personal values, and game elements. Our findings reveal that most portals incorporate ‘discovery’ elements into their dataset-searching features, subtly gamifying exploration. Additionally, portals emphasising external activities, such as hackathons and events, often lack integrated social features, suggesting a trade-off between external engagement and sustained in-portal interaction. These findings challenge the assumption that open data engagement relies primarily on external initiatives, emphasising in-portal gamification instead. This study provides recommendations for policymakers to engage with users within open data portals.

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.

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.

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.

Public service systems use empowerment capabilities to implement digital transformation (DT). DT empowerment capabilities help to tackle disruptions, navigate uncertain situations, and adapt service systems. We draw on multiple theoretical arguments and the extant literature on empowerment and DT to develop a DT empowerment capability (DTEC) model in public service systems. This model highlights the dimensions of DTEC in public service systems and their impact on digital service adaptation (DSAD). We developed the conceptual model using a systematic literature review and thematic analysis. We validated the model through two rounds of surveys: public welfare service systems (n = 275) and financial public service systems (n = 245). Partial least squares-based structural equation modeling (PLS-SEM) show two primary dimensions (i.e. technical and business) and five sub-dimensions (i.e. tools & technologies, information access, DT knowledge & skills, training & development, and decision-making) of DTEC. Complementing those insights with a configurational approach, using a fuzzy-set qualitative comparative analysis (fsQCA), we find that none of the DTEC alone is either necessary or sufficient for DSAD. Also, neither business nor technical DTECs alone, but only their different combinations, can sufficiently influence adaptation. This study finds that multiple configurations combining empowerment capabilities exist that are associated with the success of DSAD of public service agencies resulting from digital transformation, as do multiple configurations that lead to its failure. Our results contribute to the research stream of public service systems by carving out which and how dimensions of DTEC influence DSAD.

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.