Intelligence services must balance values such as national security and privacy when collecting data, with each scenario involving specific contextual trade-offs. While citizens benefit from effective intelligence operations, they also risk having their rights infringed upon. This makes citizen perspectives on acceptable data collection for intelligence and national security salient, as their legitimacy is also contingent upon public support. Yet, important aspects of citizen perspectives are understudied, such as the influence of contextual factors related to the use of intelligence collection methods. This study, inspired by Nissenbaum's contextual integrity framework, uses a factorial survey experiment with vignettes among a representative sample of 1423 Dutch citizens to examine the influence of threat type, duration, data subject, collection method, data type, and data retention on public acceptance of surveillance. Additionally, the study considers the impact of respondents' trust and privacy attitudes. The findings reveal significant influence of both contextual variables – particularly threat type, data subject, and data retention – and respondent predispositions – particularly trust in institutions, trust in intelligence services' competence, and privacy concerns for others. The findings imply that more in-depth contextual knowledge among the public may foster support for intelligence activities.

The crowd increasingly plays a key role in facilitating innovations in a variety of sectors, spurred on by IT-developments and the concomitant increase in connectivity. Initiatives in this direction, captured under the umbrella-term ‘crowd-based innovations’ (CBI), offer novel opportunities in all domains of society by increasing the access, reach and speed of services and goods. At the same time, they signify important challenges because these innovations occur in a context of traditional, well-established institutional arrangements. CBI create an ‘institutional void’: existing rules, standards and practices are challenged and renegotiated. This raises questions about the safeguarding of public values such as quality, legitimacy, efficiency and governance of crowd-based innovations. The objective of this perspective piece is to present an interdisciplinary research agenda to address normative challenges for governing CBI. We will argue that such an agenda needs an integrated empirical-normative approach. We will detail three lines of empirical-normative research that together build up towards an interdisciplinary agenda.

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.

Despite great potential, high hopes and big promises, the actual impact of big data on the public sector is not always as transformative as the literature would suggest. In this paper, we ascribe this predicament to an overly strong emphasis the current literature places on technical-rational factors at the expense of political decision-making factors. We express these two different emphases as two archetypical narratives and use those to illustrate that some political decision-making factors should be taken seriously by critiquing some of the core ‘techno-optimist’ tenets from a more ‘policy-pessimist’ angle. In the conclusion we have these two narratives meet ‘eye-to-eye’, facilitating a more systematized interrogation of big data promises and shortcomings in further research, paying appropriate attention to both technical-rational and political decision-making factors. We finish by offering a realist rejoinder of these two narratives, allowing for more context-specific scrutiny and balancing both technical-rational and political decision-making concerns, resulting in more realistic expectations about using big data for policymaking in practice.

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.

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.

Supply chain management is hampered by a lack of information sharing among partners. Information is not shared as organizations in the supply chain do not have direct contact and/or do not want to share competitive and privacy sensitive information. In addition, companies are often part of multiple supply chains and trading partners vary over time. Blockchains are distributed ledgers in which all parties in a network can have access to data under certain conditions. Private blockchains can be used to support parties in making their demand data directly available to all other parties in their supply chain. These parties can use this data to improve their planning and reduce the bullwhip effect. However, the transparency that blockchain technology offers makes it more difficult to protect sensitive data. The dynamics between these properties are not well understood. In this paper, we design and evaluate a blockchain architecture to explore its feasibility for reducing information asymmetry, while at the same time protecting sensitive data. We found that blockchain technology can allow parties to balance their need for inventory management with their need for flexibility for changing partners. However, measures to protect sensitive data lead either to reduced information, or to reduced speed by which the information can be accessed.

Fraud with online payment services is an ongoing problem, with significant financial-economic and societal impact. One of the main modus operandi is financial malware that compromises consumer and corporate devices, thereby potentially undermining the security of critical financial systems. Recent research into the underground economy has shown that cybercriminals are organised around highly specialised tasks, such as pay-per-install markets for infected machines, malware-as-a-service and money mule recruitment. Setting up a successful financial malware scheme requires the aligning of many moving parts. Analysing how cybercrime groups acquire, combine and align these parts into value chains can greatly benefit from existing insights into the economics of online crime. Using transaction cost economics, this paper illustrates the business model behind financial malware and presents three novel value chains therein. For this purpose, we use a conceptual synthesis of the state-of-the-art of the literature on financial malware, underground markets and (cyber)crime economics, as well as today’s banking practice.