Governments worldwide are increasingly exploring the use of Artificial Intelligence (AI) to support and transform policymaking. Despite the growing number of national AI strategies and initiatives, systematic evidence on how AI is integrated across the stages of the policymaking process remains limited. This lack of clarity risks fragmented integration and slows the diffusion of effective practices. This study addresses this gap by examining how AI-enabled policy actions are incorporated into policymaking processes across eight European countries. We analyze policy documents to identify AI-enabled policy actions and map them across the phases of the public policy cycle. The analysis reveals 33 AI-enabled policy actions and reveals uneven integration across policy stages and countries. While several actions focus on data infrastructure, digital public services, and agenda setting, considerably fewer initiatives target implementation monitoring, evaluation, and responsibility-related governance mechanisms. The study contributes to the literature by (1) systematically mapping AI-enabled policy actions across the policy cycle, (2) identifying integration gaps in current policymaking practices, and (3) outlining directions for future research.

Increasingly connected vehicles offer drivers benefits related to safety, navigation, and maintenance. They also provide policymakers new opportunities to trace and modify behavior using data insights. For such efforts to be effective, policymakers need access to policy instruments. These instruments must also be adopted by regulated entities such as technology providers, vehicle manufacturers, and drivers. Reuse and scaling across jurisdictional boundaries are crucial for the efficient development, adoption, and use of these digital tools. However, establishing digital tools that scale across diverse contexts requires navigating trade-offs between generalization to meet global demands and specialization to provide desired functionality. Using digital trace data and interviews, we conducted a longitudinal study of the development of the Mobility Data Specification standard for, free-floating e-scooters, over three years. We identified four key instrument tensions related to privacy, scope, richness, and the pace of evolution. We detail the nature of these tensions, analyze how they were mitigated, and suggest implications for the development of digital regulatory tools that span jurisdictional boundaries.

Open and shared government data and services offer an instrument for achieving innovation, interoperability, and transparency in public services but require relevant competency, resources, and culture to realise, something that the public sector actors often lack. In this case study, we exemplify how these challenges to data sharing may be tackled through horizontal coordination in the case of Trafiklab, a public-private collaboration on collecting and publishing open public transport data and services.

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.

An understanding of how modern Open Data Ecosystems (ODEs) work is critical in the context of current trends towards sustainability and smartness, while is seen to be an asset to support urban governance and development, in coordinating actions, and fostering civic engagement. This paper aims to establish such understanding by analyzing the contextual patterns, platforms, and components shaping sustainable ODEs by employing platform theory. This study explores and compares characteristics, similarities, differences, and best approaches in 19 cities across 8 countries. In this study we (1) identify 50 patterns that influence and shape sustainable ODEs and their platforms, i.e., Open Data Platform Ecosystems (ODPEs); (2) explore the relationships between platforms and other ODPEs components by developing a respective model, and identifying internal platforms and other components; (3) empirically validate the conceptual findings of five types of ODPEs presented in the literature, redefining them from the conceptual to real-life implementation of the respective components in sample cities; (4) considering the experience gained during the study with respect to the ODPEs and external pressures and environments that shape or influence them, we define 12 recommendations for policy planning and urban governance of more sustainable ODEs.

Public sector AI adoption is rapidly increasing as AI-based solutions are implemented in various domains. These solutions typically address existing problems within the public sector organizations' core missions and are generally seen as offering opportunities to increase operational quality and efficiency. This is for instance done through augmentation of decision-making processes and case handling activities. However, despite these aspirations, nascent research indicates that the anticipated outcomes often fail to materialize. The development and implementation of public sector AI-based solutions is laden with complex technological and legal challenges that often divert attention away from realizing actual business value. Drawing from formal and informal project documentation from a high-profile AI augmentation initiative at the Swedish Social Insurance Agency (SIA), we identify five dimensions that illustrate how technological advancement may prosper at the cost of business value realization in public sector AI initiatives.

There is a lack of understanding of the elements that constitute different types of value-adding public data ecosystems and how these elements form and shape the development of these ecosystems over time, which can lead to misguided efforts to develop future public data ecosystems. The aim of the study is twofold: (1) to explore how public data ecosystems have developed over time and (2) to identify the value-adding elements and formative characteristics of public data ecosystems. Using an exploratory retrospective analysis and a deductive approach, we systematically review 148 studies published between 1994 and 2023. Based on the results, this study presents a typology of public data ecosystems and develops a conceptual model of elements and formative characteristics that contribute most to value-adding public data ecosystems. Moreover, this study develops a conceptual model of the evolutionary generation of public data ecosystems represented by six generations that differ in terms of (a) components and relationships, (b) stakeholders, (c) actors and their roles, (d) data types, (e) processes and activities, and (f) data lifecycle phases. Finally, three avenues for a future research agenda are proposed. This study is relevant for practitioners suggesting what elements of public data ecosystems have the most potential to generate value and should thus be part of public data ecosystems. As a scientific contribution, this study integrates conceptual knowledge about the elements of public data ecosystems, the evolution of these ecosystems, defines a future research agenda, and thereby moves towards defining public data ecosystems of the new generation.

Transformation towards a digital government imposes significant demands on the capabilities of legacy infrastructure. We closely followed a Swedish municipality that designed and implemented a solution to improve the building permit application process with an aim to improve citizen service. We developed six design principles (DPs): availability, timeliness, actionability, transparency, personalization, and generalizability. These DPs guide the solution design and provide a seamless application experience for citizens and business owners. We also discuss the reasoning behind the design choices and the implications of the solution. The artifact encompasses understanding citizens' needs, identifying constraints of the legacy systems, formulating design principles, and developing architectural designs. However, addressing the social aspects of legacy systems, such as organizational culture change, necessitates additional steps, and is worth investigation in future studies.

Data sharing is increasingly essential for digital government and data-driven innovation, yet many public organizations remain reluctant to make their data openly available. While prior research has examined factors influencing open data adoption, little theoretical work explores why resistance persists within public agencies. This study develops an Innovation Resistance Theory (IRT) model tailored to government data sharing to identify predictors of organizational resistance. An initial model was derived from literature and refined through interviews with 21 public organizations across six European countries. The resulting IRT4DS model identifies 39 barriers spanning usage, value, risk, tradition, and image dimensions, and 23 countermeasures mapped to the most critical barriers and the actors responsible for addressing them. By extending IRT into the context of governmental data sharing, the study advances theoretical understanding of why public data often remains closed and provides actionable guidance for policymakers seeking to design enabling data ecosystems and reduce structural and cultural barriers to OGD adoption.

Open data platforms freely provide citizens with access to public data, thus enabling improved governance transparency, enhanced public services, and increased civic engagement. However, unlocking the potential of this digital transformation strategy requires that public institutions manage the tension between public and private interests. Furthermore, even when public institutions break down traditional barriers for citizens’ access to data, the potential users often lack the knowledge to leverage it in meaningful ways. Open data platforms therefore tend to fall short of expectations. Leveraging a 10-year action design research study (ADR) in the Swedish Transport Administration (STA), this paper develops design principles for creating value-generating open data platforms in the public domain. The ADR project was initiated to assist STA in its efforts to deal with outlaw innovators who scraped train data from different websites to develop travel apps. Through three iterative design cycles that eventually led to the formation of a new open data platform, the outlaw innovators increasingly became valued partners in the digital transformation process. Theorizing this development process, this paper offers three design principles that provide guidance to public institutions aspiring to digitally transform by making public data accessible. We also reflect upon how these institutions might mitigate the risks associated with partnering with outlaw innovators in the pursuit of an open data strategy.

This report describes how software professionals at the Norwegian public transport organization Entur use open source processes and tools to leverage digital transformation. Moving software acquisition from procurement to open source and in-house development can deliver value but also entails challenges.

The enrolment of third-party developers is essential to leverage the creation and evolution of data ecosystems. When such complementary development takes place without any organizational consent, however, it causes new social and technical problems to be solved. In this paper, we advance platform emulation as a theoretical perspective to explore the nature of such problem-solving in the realm of open platforms. Empirically, our analysis builds on a 10-year action design research effort together with a Swedish authority. Its deliberate change agenda was to transform unsolicited third-party development into a sanctioned data ecosystem, which led to a live open platform that is still in production use. Theoretically, we synthesize and extend received theory on open platforms and offer novel product and process principles for this class of digital platforms.

Connected vehicles generate new data streams that present promising opportunities for policymakers to monitor and learn from events and behavior. To explore what we can learn from how public entities leverage ubiquitous data streams for policy development and enforcement, we draw on a case study of the standard Mobility Data Specification (MDS) and its use by cities to regulate E-scooter operators. Our findings suggest that (1) the richness of real-time data changes the speed of policy revision, (2) data access enables moving some micro-decisions to the edge, and (3) policy will be formulated as fixed or flexible with different amendment rules.

This paper explores how organizations expand data-sharing capabilities beyond the loci of emergence. This inquiry was ignited from an observation that external developer practices had subverted a public sector organization into developing transformational data-sharing capabilities that effectively replaced existing integration practices within the agency. To detail and explain how the administration was able to draw on the practices and platforms established for an external context in an organizational (internal) context, we analyzed our empirical dataset using dynamic capabilities theory. By unpacking enabling microfoundations and overarching capabilities, we could explain our observations and put forward six microfoundations that underpin three data-sharing capabilities.

Public agencies are increasingly publishing open data to increase transparency and fuel data-driven innovation. Based on these experiences, we propose four distinct types of data feedback loops in which both data publishers and reusers play critical roles.

In the last decade, private companies have successfully used crowdsourcing to revolutionise mobility, while public transport companies are still mostly failing to utilise the benefits of crowdsourcing. The application of crowdsourcing in public transport is a new area of academic research, and research on crowdsourcing en route in real-time is missing. This research aims to address this gap, explore opportunities and challenges of this type of crowdsourcing, and conceptualise this phenomenon. The research is based on empirical data collected in five Northern European countries. Our research findings help identify areas where crowdsourcing en route can add value to public transport: new forms of communication, opportunities to communicate with third parties, and improved transit planning and optimisation. Identified challenges are related to behavioural change for users, a need to develop infrastructure to enable crowdsourcing en route, and financial rationalities.

Standards are considered an essential means to facilitate value creation from open data. Despite this importance, we find that empirical studies of open data standards have not been conducted in proportion to its importance. In particular, the literature has insofar been silent about why specific standards are chosen and how these standards are implemented. To this end, we report from an action research project with the Swedish public transport industry, where open data standards were both chosen and implemented. Consistent with the literature, we find standards were selected based on expected increased attractivity for re-users. Also, and more surprisingly, we found that open data standards were chosen as a means to harness resources in adjacent digital ecosystems. Finally, our findings convey that implementing open data standards may hamper the possibility to publish datasets, with its original qualities.

In this research-in-progress paper, we present findings from the diagnosing phase of a Canonical Action Research endeavour, together with the Swedish public transport industry. In our investigation ...

Denna rapport undersoker explorativt mojligheter, konsekvenser och utmaningar relaterade till fordjupade datasamarbeten mellan offentliga aktorer och privata tjansteforetag. I projektet undersoks s ...