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.

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 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.

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.

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.