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.

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The concept of the smart city increasingly being used but is in fact an umbrella topic covering several disciplines and domains. In the current literature is no agreement on a comprehensive vision of the smart city; perspectives on it vary from purely technological urban development to initiatives addressing societal challenges. We argue that in these perspectives, self-governance is often ignored, yet plays an important role in the smart city idea, bringing together people, technologies, and policies. The objective of this chapter is to provide a framework for the classification of self-governance initiatives on a community level. The framework unites heterogeneous urban initiatives giving a broader understanding of existing self-governance practices that could be used within the smart city.@en

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.@en

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.

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Context-awareness refers to the ability to sense and adapt to context. With the rise of context-aware systems, designers are struggling with what variables should be sensed from the context. According to the definitions found in the literature, whether something belongs to context, has to do with whether it is relevant. However, what it means to be relevant is left implicit in these definitions. Most work on context-aware systems is based on assumptions of the context that should be taken into account. Hence, it is unclear how to decide whether something belongs to context or should be left out. In this paper, first we analyse what is meant with context and provide a definition. In this definition we introduce the notion of a context variable, defined as an attribute of an object that is relevant. Context is then defined as the set of context variables. We establish explicit criteria for deciding whether an attribute of an object is a context variable based on the proposed definition and the designer’s goal. We also provide a straightforward method to help designers to determine whether the criterion is met and a variable should be included in the context. This method is based on filling out a scheme to describe context variables.@en

The speed and disruptive character of digital innovations affect social structures and practices faster than institutions can keep up with them. This results in an ‘institutional void’, i.e. a gap between the rules and institutions and their ability and the effectiveness of their measures. It also affects the institutional stability that is the basis for the paradigm of collaboration-based types of governance. In this paper, we explore how parties are able to set up collaboration for digital security, which is inherently a topic that transcends organisational boundaries. Yet digital innovations constantly enable new challengers that might not share the same incentives for collaboration. Life in an institutional void is convenient for them and enables new business models. Hence, a key question is whether (institutionalised) collaboration is a sustainable model for addressing shared problems like digital security. We explore this question in the domain of financial cyber fraud. The new (regulatory) space currently being created for innovators suggests that the answer is ‘no’. It is too early to say how this will play out specifically and we argue for further research into the antecedents for collaboration in institutional voids.@en

Toensurepublicsafetyandsecurity,itisvitallyimportantforgovernmentstocol- lect information from businesses and analyse it. Such information can be used to determine whether transported goods might be suspicious and therefore require physical inspection. Although businesses are obliged to report some information, they are reluctant to share addi- tional information for fear of sharing competitively sensitive information, becoming liable and not being compliant with the law. These reasons are often overlooked in the design of software architectures for information sharing. In the present research, we followed a design science approach to develop a software architecture for business-to-government information sharing. Based on literature and a case study, we elicited the requirements an architecture that provides for the sharing of information should meet to make it acceptable to businesses. We then developed the architecture and evaluated it against the requirements. The architec- ture consists of a blockchain that stores events and rules for information sharing that are controlled by businesses. For each event, two parties use their private keys to encrypt its Merkle root to confirm that they know the data are correct. This makes it easy to check whether information is reliable and whether an event should be accepted. Access control, metadata and context information enable the context-based sharing of information. This is combined with the encryption and decryption of data to provide access to certain data within an organisation.@en

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.

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This document represents the governance handbook on mobility data platforms for the PETRA project. The governance handbook provides metropolitan authorities contemplating the implementation of a mobility data platform in line with the PETRA project about governance issues and design. The analysis has shown mobility data platforms with the reach of PETRA are not available, however a variety of solutions of both the platform and the app exist in various situations. Of those, 13 were analyses to understand how governance shapes the solutions implemented and what the solutions implemented need from governance. The results do not provide a single best solution of governance for three reasons. First, mobility data platforms will land in a variety of (governance) contexts. Assuming that authorities would be willing to fully adjust their governance for the implementation of a mobility data platform is naïve. Second, mobility data platforms can come in a variety of forms. The governance has to align with the particular implementation. Third, governance solutions consist of a great deal of element that not necessarily always act uniformly, and as such, not always act predictably in various contexts. Because of that variability the handbook highlights key mechanisms that authorities working on the governance of a mobility data platform should take in to account, rather than provide a theoretical and unrealistic single optimal governance design. The report adapts a column structure to align with this focus on specific mechanisms to take into account. It also provides specific readers with a planned route through the different columns. The governance handbook was developed based on the analysis of 13 case studies and the three demonstrators. The provided the input to understand the relation between the specific implementation of a mobility data platform and a governance context. Desk research and interviews provided the understanding of those cases, the types of data input, the function of the platform in terms of data linking, capture, retention, storage, aggregation, and modelling, the data output and a possible mobile application. The key question in the cases was to understand organisational links of the stakeholders providing the data and using the data, and the links to the stakeholders with an interest in the various functionalities of the platform in terms of data handling. From these links we could further understand how decision-making on the platform was structured and what outcomes of that decision-making could be expected. The report starts with an instruction on the overall project, followed by a prologue, that sets the scene. This is followed by a number of theoretical columns, highlighting understood mechanisms from organisational science and public administration that showed to be relevant in the cases and demonstrators. After these, empirical columns highlight mechanisms that were recognised in the cases studied, and that illustrate the complex and varied contexts of mobility data platforms and how to align the governance to specific goals. Finally, five syntheses are given, including a business case, a set of models, and different governance design routes.@en

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.@en

Big data is being implemented with success in the private sector and science. Yet the public sector seems to be falling behind, despite the potential value of big data for government. Government organizations do recognize the opportunities of big data but seem uncertain about whether they are ready for the introduction of big data, and if they are adequately equipped to use big data. This paper addresses those uncertainties. It presents an assessment framework for evaluating public organizations’ big data readiness. Doing so demystifies the concept of big data, as it is expressed in terms of specific and measureable organizational characteristics. The framework was tested by applying it to organizations in the Dutch public sector. The results suggest that organizations may be technically capable of using big data, but they will not significantly gain from these activities if the applications do not fit their organizations and main statutory tasks. The framework proved helpful in pointing out areas where public sector organizations could improve, providing guidance on how government can become more big data ready in the future.

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