Effective enforcement of laws and regulations hinges heavily on robust inspection policies. While data-driven approaches to testing the effectiveness of these policies are gaining popularity, they suffer significant drawbacks, particularly a lack of explainability and generalizability. This paper proposes an approach to crafting inspection policies that combines data-driven insights with behavioral theories to create an agent-based simulation model that we call a theory-infused phenomenological agent-based model (TIP-ABM). Moreover, this approach outlines a systematic process for combining theories and data to construct a phenomenological ABM, beginning with defining macro-level empirical phenomena. Illustrated through a case study of the Dutch inland shipping sector, the proposed methodology enhances explainability by illuminating inspectors’ tacit knowledge while iterating between statistical data and underlying theories. The broader generalizability of the proposed approach beyond the inland shipping context requires further research.

The digital transformation of education has rapidly evolved in recent years, driven by advancements in technology and further accelerated by the COVID-19 pandemic. Digital education Technologies (DETs) have become integral to higher education, reshaping how institutions deliver learning and manage resources. However, despite the widespread adoption of DETs, there has been limited focus on the sustainability of these technologies. This paper explores how sustainability considerations are integrated into DET selection processes at European Higher Education Institutions (HEIs) through semi-structured interviews with key decision-makers. The research focuses on three sustainability dimensions-environmental, social, and technological-and their impact on decision-making. The results indicate that while HEIs are making efforts toward sustainability, economic considerations still dominate the decision-making process. Moreover, the emphasis across sustainability dimensions remains unbalanced: social dimensions, such as privacy, are prioritized over environmental dimensions due to the former being treated as knockout criteria and due to a lack of reliable data on the environmental impacts of DETs. This study also identifies several challenges, including long procurement processes, limited financial resources, and heavy dependence on external service providers for digital infrastructure. The findings offer insights into how HEIs can better align their digital strategies with broader sustainability goals.

The impact of machine learning within public organizations relies on coordinated effort over the functional chain from data generation to decision-making. This coordination faces challenges due to the separation between data intelligence departments and operational intelligence. Through theory about knowledge sharing between occupational communities and a case study at a Dutch inspectorate, we explore knowledge boundaries between machine learning developers and end-users and the effects of co-creation. Our analysis reveals that knowledge boundaries are dynamic, with boundaries blurring, persisting, and emerging under the influence of co-creation. Especially the emergence of boundaries is surprising and suggests the presence of a waterbed effect. Furthermore, knowledge boundaries are layered phenomena, with some boundary types more prone to change than others. Understanding knowledge boundaries and their dynamics better can be crucial for improving the intended impact of ML for organizations.

Epilogue: new roles for citizen science In this epilogue to the special issue on citizen science, we discuss different forms of citizen science, the different roles of science, the shifting role of governments and tensions between citizens, science and government authorities, based on articles and interviews. Citizen science comes in many forms. We conclude that the motive behind citizen science and the management of the initiative are important dimensions. Motives and direction are also important for the roles science and government play in the initiatives. For example, there roles as a source of legitimacy, knowledge processing and data processing for third parties. Such roles are then crucial for ensuring data quality and data ownership. Both topics require strong organisation of both the initiative itself and between citizens, government and science, especially if the topic is wicked.

Technical data processing and analysis skills are critical audit analytics (AA) implementation challenges. Nevertheless, there is limited guidance on how to overcome it other than the 'traditional' training approach. This paper explores an unorthodox approach to dealing with issues in AA-related skills through a data analytics competition (hackathon). This study observes two hackathons that contribute to addressing AA-related skills issues. The hackathon incorporates the elements of real-world use-case and interactive engagement from the gamification concept and internal and external motivations from the competition notion. Building upon these insights, this paper proposes an emerging framework of a hackathon for skills development in AA implementation efforts. The framework's purpose is twofold. Scientifically, it assists in understanding the role of gamification and hackathon for skills development based on knowledge creation concepts. Practically, it exemplifies how to utilize hackathons to address skill issues. Furthermore, this research also suggests promising avenues for future studies, including further evaluation to affirm or expand the proposed framework or examination of the impact of detailed elements of the proposed framework.

Prologue: citizen science for a sustainable and healthy society – what role for government? The increased capacity for affordable data processing, the rise of platforms, and facilitated citizen mobilization through social media encourage citizen science. The rise of citizen science may have significant implications for governance and policy. In the prologue of this special issue on citizen science, we outline the potential of citizen science for public organisations and discuss various tensions. This potential includes citizens influencing policies and generating novel insights through unique combinations of data, organisations and individuals. Public organisations may engage in citizen science initiatives for both epistemological and democratic reasons. We conclude that the roles of public organisations and citizen scientists are not well defined, which is, for example, reflected in debates over data ownership. Additionally, we identify tensions, such as differing perspectives among involved actors in citizen science projects regarding what constitutes data quality.

Governance of infrastructures: Building bridges with technology and governance Governance research in infrastructure sectors has gained traction in the Netherlands in recent decades. The classic leading disciplines in infrastructure research were economics, which linked capacity, costs and engineering, optimising the design for each specific technological infrastructural context, such as rail, electricity and data. This article follows the path from Delft to a broad linking of governance in the Netherlands to the research classics.In a transitory world, infrastructure literately connects all the moving parts of energy, transport, water and data systems. Supporting change can only work if decision-making does justice to economic and technical realities, which poses a major challenge in the current dynamic climate. Innovating governance of and in infrastructure sectors in a way that ties these realities together is the next challenge.

ABSTRACT Internal audit function (IAF) effectiveness can be improved by embracing Audit Analytics (AA). However, despite its promises, AA implementation remains limited. Although there is research on AA implementation in general, there needs to be an overview of insight into inhibiting and driving factors for internal auditing. This paper examines those driving and inhibiting factors by exploring the literature on AA implementation. The initial search revealed 98 uniquely identified papers. Further filtering and the additional search returned 42 articles, which were analyzed in detail. The analysis resulted in 12 driving and 23 inhibiting factors, grouped into internal, regulation, data, infrastructure, and audit practice categories. The literature shows that IAF encounters multiple and intertwined factors in AA implementation and needs to anticipate those factors. Moreover, AA implementation affects IAF’s parts and stakeholders differently, requiring internal and external collaboration. Building on these insights, we provide recommendations for further research. JEL Classifications: M42; M49; O32.

The transformation toward the use of data analytics requires overcoming many challenges. Nevertheless, the interconnections between the challenges are unclear. Gaining knowledge about these interconnections is important to prioritize strategies that aim to stimulate the transformation. This paper unravels the relationship among Audit Analytics (AA) implementation challenges to transform the Internal Audit Function (IAF) using Matrice d'Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) – Interpretative Structural Modelling (ISM) (or MICMAC-ISM) to develop a hierarchical model and determine the relationships among the challenges and the degree of power of each challenge. We collect data from internal auditors experienced in using audit analytics. They suggest that cultural challenges, along with technical challenges, are critical for enabling transformation. Moreover, combinations of approaches are required to address the complex interrelationships among challenges to initiate transformation. The analysis suggests that AA implementation requires a top-down approach to address cultural challenges blended with a bottom-up strategy to overcome technical challenges.

Purpose - The purpose of this paper is to introduce the papers in this special issue on humans, algorithms and data. The authors first set themselves the task of identifying the main challenges arising from the adoption and use of algorithms and data analytics in management, accounting and organisations in general, many of which have been described in the literature. Design/methodology/approach - This paper builds on previous literature and case studies of the application of algorithm logic with artificial intelligence as an exemplar of this innovation. Furthermore, this paper is triangulated with the findings of the papers included in this special issue. Findings - Based on prior literature and the concepts set out in the papers published in this special issue, this paper proposes a conceptual framework that can be useful both in the analysis and ordering of the algorithm hype, as well as to identify future research avenues. Originality/value - The value of this framework, and that of the papers in this special issue, lies in its ability to shed new light on the (neglected) connections and relationships between algorithmic applications, such as artificial intelligence. The framework developed in this piece should stimulate scholars to explore the intersections between "technical" as well as organisational, social and individual issues that algorithms should help us tackle.

ABSTR A C T The road from data generation to data use is commonly approached as a data-driven, functional process in which domain expertise is integrated as an afterthought. In this contribution we complement this functional view with an institutional view, that takes data analysis and domain professionalism as complementary (yet fallible) knowledge sources. We developed a framework that identifies and amplifies synergies between data analysts and domain professionals instead of taking one of them (i.e. data analytics) at the centre of the analytical process. The framework combines the often-cited CRISP-DM framework with a knowledge creation framework. The resulting framework is used in a data science project at a Dutch inspectorate that seeks to use data for risk-based in-spection. The findings show first support of our framework. They also show that whereas more complex models have a higher predictive power, simpler models are sometimes preferred as they have the potential to create more synergies between inspectors and data analyst. Another issue driven by the integrated framework is about who of the involved actors should own the predictive model: data analysts or inspectors.

The COVID-19 pandemic highlights the dependence on digital public service delivery in many nations. The intensified use of digital public services also shifted the spotlight to accessibility and reactive design of digital public services. Inspired by the high level of proactivity provided in commercial digital services, policy-makers are looking for guidance on employing the vast amount of (personal) data available at various public agencies to proactively aid citizens during important life events. Proactivity, however, is a very complex multi-level concept with a myriad of case-specific forms and conditions and is not always desired. Moreover, there is little guidance in the literature on how to classify the level of proactivity and design more proactive public services. The objective of this paper is to provide guidance for classifying, understanding, and designing proactivity. Drawing on previous conceptualizations in literature, this paper introduces a proactivity classification framework that is substantiated using empirical cases from the Netherlands. We found that fully proactive services are not always desired or possible due to public service characteristics. The two key variables in this framework – service eligibility and service delivery – were used to propose design principles for increasing public services' proactivity. The principles were validated and prioritized by four public service innovators. Policy-makers looking to enhance inclusivity through service proactivity can start by classifying current services and integrating the design principles in their innovation roadmap.

Despite the promise of AI and IoT, the efforts of many organizations at scaling smart city initiatives fall short. Organizations often start by exploring the potential with a proof-of-concept and a pilot project, with the process later grinding to a halt for various reasons. Pilot purgatory, in which organizations invest in small-scale implementations without them realizing substantial benefits, is given very little attention in the scientific literature relating to the question of why AI and IoT initiatives fail to scale up for smart cities. By combining extensive study of the literature and expert interviews, this research explores the underlying reasons why many smart city initiatives relying on Artificial Intelligence of Things (AIoT) fail to scale up. The findings suggest that a multitude of factors may leave organizations ill prepared for smart city AIoT solutions, and that these tend to multiply when cities lack much-needed resources and capabilities. Yet many organizations tend to overlook the fact that such initiatives require them to pay attention to all aspects of change: strategy, data, people and organization, process, and technology. Furthermore, the research reveals that some factors tend to be more influential in certain stages. Strategic factors tend to be more prominent in the earlier stages, whereas factors relating to people and the organization tend to feature later when organizations roll out solutions. The study also puts forward potential strategies that companies can employ to scale up successfully. Three main strategic themes emerge from the study: proof-of-value, rather than proof-of-concept; treating and managing data as a key asset; and commitment at all levels.

Insight into transparency: An essay about trade-offs behind algorithmic decision-making Algorithms applied in public administration are often criticized for lack of transparency. Lawmakers and citizens alike expect that automated decisions based on algorithmic recommendations to be explainable. The focus of this article is the organizational context behind the idea of transparent algorithms. Transparency is portrayed as one of numerous values that are at play when algorithms are applied in public administration. The article shows that applying algorithms may lead to conflicts between these values. Such conflicts often result in trade-off decisions. Looking from the organizational perspective, we describe how such trade-offs can be made both explicitly and implicitly. The article thus shows the complexity of algorithmic trade-offs. As a result of this complexity, we not only call for more transparency about algorithms, but also more transparency about trade-offs that take place in public administration. Finally, we present a research agenda focused on studying the organization of trade-offs.

Er is veel aandacht voor de manier waarop overheden hun analyses uitvoeren.Big data, artificiële intelligentie en internet of things geven toegang tot data en veel mogelijkheden om de analyses te optimaliseren.Publicisten en beleidsmakers waren de nodige jaren enthousiast over de mogelijkheden

Is datagedreven risicogebaseerd toezicht op termijn effectief? Welke effecten kunnen we op termijn verwachten als inspecteurs op basis van data risicogebaseerd gaan werken? We hebben een agentgebaseerd model ontwikkeld waarmee we verschillende scenario’s kunnen testen. Het model bevestigt het potentieel van datagedreven toezicht op de effectiviteit voor inspecties. Maar het waarschuwt ook voor bias, omdat met datagedreven toezicht alleen data van risicovolle bedrijven worden verkregen. Een beperkt aantal willekeurige inspecties kan de datakwaliteit al fors doen toenemen. Daarmee waarschuwen we voor te veel optimisme over de efficiëntie van datagedreven risicogebaseerd toezicht. Bovendien reiken we een model aan waarmee een optimum tussen datagedreven en willekeurige inspecties te bepalen is.

Countries around the world have had to respond to the COVID-19 outbreak with limited information and confronting many uncertainties. Their ability to be agile and adaptive has been stressed, particularly in regard to the timing of policy measures, the level of decision centralization, the autonomy of decisions and the balance between change and stability. In this contribution we use our observations of responses to COVID-19 to reflect on agility and adaptive governance and provide tools to evaluate it after the dust has settled. Whereas agility relates mainly to the speed of response within given structures, adaptivity implies system-level changes throughout government. Existing institutional structures and tools can enable adaptivity and agility, which can be complimentary approaches. However, agility sometimes conflicts with adaptability. Our analysis points to the paradoxical nature of adaptive governance. Indeed, successful adaptive governance calls for both decision speed and sound analysis, for both centralized and decentralized decision-making, for both innovation and bureaucracy, and both science and politics.

Outpaced by the speed of digital innovation in the private sector, governments are looking for new approaches to public service innovation. Drawing on three complementary innovation theories – open innovation, recombinant innovation and co-creation – this paper presents a prototype that is designed to enhance the online innovation journey for public services. The main strategy explored is that of online public-service co-creation, allowing innovators to combine online and offline efforts. The outcome of this research is a prototype of an online co-creation tool. The tool is consumed via a web-portal that includes an overview of ongoing experiments, tools, labs data sets and digital building blocks. This paper contributes by presenting the requirements and lessons learned when developing a co-creation tool for innovation in public service design. While the proposed co-creation tool is expected to enhance and speed up online cocreation efforts, findings indicate that innovators from the public and private sector still need to learn how to combine online and offline co-creation efforts. The added value expected from the online tool is that it should provide an up to date oversight of digital building blocks, innovation methods and labs. Interviews with prospective users suggest that this oversight is needed to jumpstart the first step of the innovation journey. Development of a digital sandbox – a shared online experimentation environment – is considered to be an important next step for innovation in public service design.