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.

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.

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.

Sexually transgressive behaviour (STB) causes serious problems for, among others, students of higher education. The persuasive card game TALK THAT TALK was designed to promote ethical bystander behaviour in STB situa-tions and contribute to a social transition to less sexual violence. To this aim, the game facilitates Intergroup Dialogues between female and male players. A con-trolled experiment was conducted to evaluate the game. The outcome variables of the experiment were obtained from the literature: Willingness to Intervene, Awareness of prevalence of STB, and Bystander Responsibility. Quantitative and qualitative analyses, including validated questionnaires and semi-structured in-terviews, were employed to measure the game’s effects. Participants evaluated the quality of the game (session) and game experience positively and reported that meaningful intergroup dialogues about STB situations took place during the game session. As a result, in the experimental group a significant increase of the three outcome variables was observed, whereas in the control group a non-sig-nificant decrease was found. However, due to a selection bias in the recruitment of participants the effects were possibly overestimated. Reversely, a lack of prac-tical skills training in the game may have led to an underestimation of the effects. We concluded that the game TALK THAT TALK may promote ethical bystander behaviour in STB situations by facilitating an intergroup dialogue between fe-male and male participants. Future research should establish if the results can be generalised to a more representative sample of participants and if the game ef-fects may be improved when institutes for higher education include the game in large-scale intervention programmes against sexually transgressive behaviour.

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.

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.

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 inspection. 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.

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.

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.

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.

Big data promises to transform public decision-making for the better by making it more responsive to actual needs and policy effects. However, much recent work on big data in public decision-making assumes a rational view of decision-making, which has been much criticized in the public administration debate. In this paper, we apply this view, and a more political one, to the context of big data and offer a qualitative study. We question the impact of big data on decision-making, realizing that big data – including its new methods and functions – must inevitably encounter existing political and managerial institutions. By studying two illustrative cases of big data use processes, we explore how these two worlds meet. Specifically, we look at the interaction between data analysts and decision makers. In this we distinguish between a rational view and a political view, and between an information logic and a decision logic. We find that big data provides ample opportunities for both analysts and decision makers to do a better job, but this doesn't necessarily imply better decision-making, because big data also provides opportunities for actors to pursue their own interests. Big data enables both data analysts and decision makers to act as autonomous agents rather than as links in a functional chain. Therefore, big data's impact cannot be interpreted only in terms of its functional promise; it must also be acknowledged as a phenomenon set to impact our policymaking institutions, including their legitimacy.

Inside companies that produce significant risks, risk managers play a key role. They manage the connection between the risk regulation regime, which stresses public values, and the company, which pursues a broader array of organisational goals. This makes the role of risk managers ambivalent. To better understand this ambivalence and identify the means, motives and strategies that risk managers employ in response to this ambivalence, this article conducts a concise review of (classic) organisation and regulatory literature. Based on this review, we propose a typology that distinguishes four roles of risk managers: risk managers as supporting staff; risk managers as professionals; risk managers as boundary spanners; and risk managers as agents in regulatory communities. Each type subsequently describes how risk managers employ different strategies in their attempt to connect the risk regulation regime and the company, ie translating policies to practices, tailoring policies to practices, explaining and framing policies and practices, and (re)interpreting policies and practices together with regulators. The typology enables researchers and practioners to emphasise and more thoroughly analyse the variety and complexity of risk managers’ work, and can help regulators to broaden and fine-tune their strategies to improve connections with the various roles of risk managers.

Driven by the technological capabilities that ICTs offer, data enable new ways to generate value for both society and the parties that own or offer the data. This article looks at the idea of data collaboratives as a form of cross-sector partnership to exchange and integrate data and data use to generate public value. The concept thereby bridges data-driven value creation and collaboration, both current themes in the field. To understand how data collaboratives can add value in a public governance context, we exploratively studied the qualitative longitudinal case of an infomobility platform. We investigated the ability of a data collaborative to produce results while facing significant challenges and tensions between the goals of parties, each having the conflicting objectives of simultaneously retaining control whilst allowing for generativity. Taken together, the literature and case study findings help us to understand the emergence and viability of data collaboratives. Although limited by this study’s explorative nature, we find that conditions such as prior history of collaboration and supportive rules of the game are key to the emergence of collaboration. Positive feedback between trust and the collaboration process can institutionalise the collaborative, which helps it survive if conditions change for the worse.

Business-to-government information exchange has over the past decades greatly benefited from data exchange standards and inter-organisational systems. The data era enables a new shift in the type of information sharing; from formal reporting to opening up full (and big) data sets. This enables new analytics and insights by government, more effective and efficient compliance assessment, and other uses. The emphasis here shifts from establishing formats to deciding what information can be shared, under what conditions, and how to create added value. There are numerous initiatives that explore how to put data to better use for businesses, for government and for their interactions. However, there is limited attention to exactly how these new forms of extensive data sharing affects the supervision relationships. In this paper, we exploratively look across three research projects to identify the implications of information sharing beyond the regulatory requirements (‘over-compliant’). We find that the lack of attention to those implications lead to solutions that are hard to scale up and present unexpected consequences down the line, which may negatively impact the future willingness to explore new potential added value of data sharing.

Big data en, breder, data science, belooft veel voor toezicht. Om de beloften waar te maken moeten de nieuwe methoden en technieken wel worden toegepast in een inspectieorganisatie. Veel literatuur gaat over de beloften en bedreigingen van data science, weinig over de implementatie in organisaties. Deze bijdrage exploreert een aspect van dit onderwerp, namelijk de relatie tussen de nieuwe data scientist en de inspecteur. Beide vertegenwoordigen verschillende, en soms strijdige, essentiële kennisbronnen voor risicogebaseerd toezicht. Hoe kan er synergie tussen deze kennisbronnen worden bereikt? Deze bijdrage omschrijft deze uitdaging en verkent een drietal oplossingsrichtingen.

The promise of big data in the field of mobility is great, for example for mobility-as-a-service solutions. Having a better sense of the existing flows over the network would allow for much improved modelling of future flows and nudging users into behaviours targeting collectively better outcomes. Because of this promise the interest that cities have in big data for mobility is high. They are looking for ways in which a mobility data platform gathers the relevant data, allow for advanced modelling of current and future network states, and ways to drive travel behaviour. We participated in the EU funded PETRA project that built such a platform for the cities of Haifa, Rome and Venice. In this paper, we are looking for key governance mechanisms that affect the success of mobility data platforms, and how they are related to technical features. The project and an additional study into 10 cases revealed that the more ambitious a platform is on a technical level, the more governance challenges they will encounter, thus the more advanced governance arrangements are necessary. However, many governance arrangements are a given rather than a subject to design. This implies that for success, the technical ambition of the platform should be aligned with the institutions of the city in which the platforms will be implemented.

Big data has been acknowledged for its enormous potential. In contrast to the potential, in a recent survey more than half of financial service organizations reported that big data has not delivered the expected value. One of the main reasons for this is related to data quality. The objective of this research is to identify the antecedents of big data quality in financial institutions. This will help to understand how data quality from big data analysis can be improved. For this, a literature review was performed and data was collected using three case studies, followed by content analysis. The overall findings indicate that there are no fundamentally new data quality issues in big data projects. Nevertheless, the complexity of the issues is higher, which makes it harder to assess and attain data quality in big data projects compared to the traditional projects. Ten antecedents of big data quality were identified encompassing data, technology, people, process and procedure, organization, and external aspects.

This document represents business model overview for PETRA. The business model is an integral part of the governance handbook, (D7.3), but is also listed as a separate deliverable. For all details, please refer to deliverable 7.3. Here an abbreviated version is presented.
The model uses a business model canvas to look at the main revenue and costs streams, risks and assumptions.
The business model is based on information that was gathered from the demonstrators and literature.
On the revenue side of the business model, the context and policies in place very much drive the expected revenues. Rather than providing an expected outcome, the model proposed existing and well developed approaches to estimate possible revenues, given a specific context.