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According to open government principles, public administration should make its data available to the public to create transparency, accountability, and to facilitate participation in public decision-making. Open-Data Policies (ODPs) were developed to encourage the opening of governmental data to the public, but require collaboration and awareness from all levels of government. Gaming has proved to be successful for creating awareness. Yet the pandemic hinders in-person gameplay, and there is a need for an online game that can be played remotely. This research uses an in-person game as the starting point to develop an online collaborative digital game. The feature and design choices of the digital game are described, along with its main differences when compared to the in-person one. The essential features include interaction and discussion about open data among players, the assignment of different roles, and points awarded when making the right decision to open, partially share, or close a dataset.

The actual opening of government data is done by civil servants operating within their institutional environment. As such, the institutional environment and their behavior towards the opening of data is key to increase the quality and quantity of open data. However, few studies have analyzed the behavior of government professionals towards open data policy. The objective of this paper is to review the existing literature to find the factors that influences civil servants behaviors towards open data. To identify the drivers and barriers, a literature review was performed listing the most cited papers with "open government data"and "barriers"focusing specific at the behavioral related factors. Even with the increasing of research on the topic of open data, still most of the papers focus on user drivers and barriers rather than on provider challenges. Even less studies focus on the civil servants' individual level of factors influencing their support to the release of governmental data. Most barriers found in the literature are related to infrastructural or technical issues. Whereas some individual level behavioral barriers could be found, including culture, lack of individual incentives and misunderstanding the impact of opening data, broader discussions on social norms, lack of education and experience are still missing in the literature. This paper contributes to the need for deeper understanding of the behavioral factors that influences the civil servants to support the opening of data.

Part 4: AI, Data Analytics, and Automated Decision Making; International audience; Open government data initiatives have gained popularity around the world. Artificial Intelligence (AI) has the potential to make better use of data. Combining the OGD and AI is crucial to generate more value from data. In this paper we investigate what kind of value was generated through AI and how. A context-input-process-output/outcome (CIPO) framework is developed to describe and compare three cases. The overview of cases shows the huge potential of AI, but it also suggests that AI is hardly used by the public to create value from open data. The objectives of the three cases are efficiency, innovation and crime prevention, whereas common open government objectives like transparency, accountability and participation are given less attention. By using AI, the risks of data privacy and arriving at biased or wrong conclusions become more prominent. With the rise of data collection from Internet of Things, complying with the 5-stars of Berners-Lee becomes more important. We recommend policy makers to stimulate AI projects contributing to the open government goals and ensure that open data meets the 5-star requirements.

Part 2: E-Government Services and Open Government; International audience; Governance requirements for systems supporting information sharing between businesses and government organisations (B&G) are determined by a high variety of stakeholders with often conflicting interests. These conflicting interests can hamper the introduction and scaling-up of ICT-innovations that change their roles and authorities. We address one such innovation: the introduction of blockchain technologies in the B&G context. Who can govern data and the system depends on several elements of the design of a blockchain-based system, particularly the data structure, consensus mechanism and network topology. Design choices regarding these elements affect who can make decisions and hence we call them blockchain control points. These control points require an explicit and well-understood relationship between the design decisions and the interests of stakeholders. Yet, the literature on blockchain technology and governance does not offer such insight. Therefore, we developed a framework to assess the alignment between stakeholders interest and blockchain design choices. This framework consists of three views and their interrelationships, 1) a stakeholder view providing insight into the tensions between stakeholder’s interests and governance requirements, 2), a governance view on the rights concerning the data and the system, and 3) a blockchain control view describing how design decisions on the control points affect whether governance requirements are met and how parties can exercise their rights. Making these links explicit enables an understanding of how technical design choices can trigger organizational dynamics from the stakeholder view and vice versa. Based on the framework we formulate a research agenda concerning blockchain design choices and governance.

Previous research assumes that poor quality of Open Government Data (OGD), OGD portals, and the services provided for OGD may result in reduced trust of citizens in OGD. However, studies that empirically test this assumption are scarce. Using the Information Systems (IS) Success Model as a theoretical basis, this study aims to examine the effects of data quality, system quality, and service quality on citizens’ trust in OGD. We used Structural Equation Modeling (SEM) to analyze the 200 responses to our online questionnaire. We found that trust in OGD can be predicted by citizens’ perceptions of OGD system quality and service quality. Furthermore, citizens’ perception of service quality positively influences their perceptions of data and system quality, whereas citizens’ perception of system quality positively influences their perception of data quality. This study is among the first that quantitatively examines the effects of data quality, service quality, and system quality on citizen's trust in OGD. It contributes to the scientific literature by providing an operationalization of elements of the IS Success Model in the context of OGD and by developing and applying a model of factors influencing citizen's trust in OGD. While previous research finds that perceived data quality is the most crucial driver for trust in OGD, our study finds that citizens’ perception of OGD service quality is a more important driver for trust in OGD. With regard to the practical contributions of this study, open data policymakers should be aware that citizens’ perceptions on data quality can be greatly improved when appropriate human services are provided (e.g., designated civil servants offering support or help to data users) in addition to the provision of OGD portal functionalities (e.g., data visualization and comparison tools).

Part 1: E-Government Foundations; International audience; The introduction of ICT is requiring public administrations to transform their organizations to take advantage of these technologies. Despite its significance, no studies so far collected quantitative evidence on (i) how and the extent to which this transformation is currently underway and (ii) which drivers and barriers are hindering and leading this transformation process. This article aims at filling this gap by surveying Dutch public administrations. In total, 46 responses from different organizations were collected that provide insight into their transformation efforts. Findings show that digital transformation efforts had only a partial impact at the organizational level: processes, employees’ duties and tasks and information systems are going through a deep transformation, whereas the social system seems to be less affected by the transformation process. Moreover, the analysis results suggest that external drivers are the main motivation for organizational transformation, and that expected internal barriers do not de facto result in digital transformation. These counterintuitive results suggest that in public administrations only exogenous input result in a sense of urgency and that the perceived barriers to transformation can be overcome if there is sufficient external pressure.

Part 5: Big Data and Machine Learning; International audience; More and more, asset management organizations are introducing data science initiatives to support predictive maintenance and anomaly detection. Asset management organizations are by nature data intensive to manage their assets like bridges, dykes, railways and roads. For this, they often implement data lakes using a variety of architectures and technologies to store big data and facilitate data science initiatives. However, the decision-outcomes of data science models are often highly reliant on the quality of the data. The data in the data lake therefore has to be of sufficient quality to develop trust by decision-makers. Not surprisingly, organizations are increasingly adopting data governance as a means to ensure that the quality of data entering the data lake is and remains of sufficient quality, and to ensure the organization remains legally compliant. The objective of the case study is to understand the role of data governance as success factor for data science. For this, a case study regarding the governance of data in a data lake in the asset management domain is analyzed to test three propositions contributing to the success of using data science. The results show that unambiguous ownership of the data, monitoring the quality of the data entering the data lake, and a controlled overview of standard and specific compliance requirements are important factors for maintaining data quality and compliance and building trust in data science products.

Part 3: Open Science and Open Data; International audience; The public expects government institutions to open their data to enable society to reap the benefits of these data. However, governments are often reluctant to disclose their data due to possible disadvantages. These disadvantages, at the same time, can be circumstances by processing the data before disclosing. Investments are needed to be able to pre-process a dataset. Hence, a trade-off between the benefits and cost of opening data needs to be made. Decisions to disclose are often made based on binary options like “open” or “closed” the data, whereas also parts of a dataset can be opened or only pre-processed data. The objective of this study is to develop a decision tree analysis in open data (DTOD) to estimate the costs and benefits of disclosing data using a DTA approach. Experts’ judgment is used to quantify the pay-offs of possible consequences of the costs and benefits and to estimate the chance of occurrence. The result shows that for non-trivial decisions the DTOD helps, as it allows the creation of decision structures to show alternatives ways of opening data and the benefits and disadvantages of each alternative.

Part 4: User Perspectives; International audience; Engaging citizens in open data hackathons provides opportunities for innovation and the generation of new services and products. This paper aims to explore the motivations of citizens who engage in open agriculture data hackathons. We conducted a case study and analyzed data collected from 161 participants of 11 farming hackathons held between 2016 and 2018 in the Netherlands. We found that participants of open agriculture data hackathons have different roles, including business developer, concept thinker, data analyst, data owner, developer, manager, marketer, problem owner, and student. Our analysis shows that citizens are predominantly motivated to engage in open agricultural data hackathons as part of their work. Furthermore, developers and problem owners are mainly motivated by fun and enjoyment. This indicates that it is important for open data policymakers and hackathon organizers to consider different approaches based on citizens’ roles when organizing open data hackathons. This paper contributes to the literature by providing insight in the motivations of citizens engaging in open agriculture data hackathons in comparison with hackathons in other sectors, and by mapping citizens’ roles to their motivations for engaging in such hackathons.

IT Governance is often viewed as an important factor for creating business value for firms. However, there is limited work investigating the relationship between architectural governance and the contributions of enterprise architecture (EA) to firm performance. Based on a study of more than 15 cases the analyzes shows that architectural governance is a condition for the ability to create business value from the EA function. The cases also show that architectural governance depends on the context and there is no best way of EA governance that fits very situation. Architectural governance complements enterprise architecture and should ensure that EA efforts are coordinated and used by the business to improve firm performance. In many cases EA and architectural governance were found to be strongly connected, making it difficult to separate them. This strong dependence suggests that a change in EA influences the governance and vice versa. Architectural governance introduces more bureaucracy and administrative work, but paradoxically can result in the creation of more business flexibility and agility.

Governments may have their own business processes to decide to open data, which might be supported by decision-making tools. At the same time, analyzing potential benefits, costs, risks, and other effects-adverse of disclosing data are challenging. In the literature, there are various methods to analyze the potential advantages and disadvantages of opening data. Nevertheless, none of them provides discussion into the comparative studies in terms of strengths and weaknesses. In this study, we compare three methods for disclosing data, namely Bayesian-belief networks, Fuzzy multi-criteria decision-making, and Decision tree analysis. The comparative study is a mechanism for further studying the development of a knowledge domain by performing a feature-by-feature at the same level of functionalities. The result of this research shows that the methods have different strengths and weaknesses. The Bayesian-belief Networks has higher accuracy in comparison, and able to construct the causal relationships of the selected variable under uncertainties. Yet, this method is more resource intensive. This study can contribute to the decision-makers and respected researchers to a better comprehend and provide recommendation related to the three methods comparison.

Today’s financial service organizations have a data deluge. A number of V’s are often used to characterize big data, whereas traditional data quality is characterized by a number of dimensions. Our objective is to investigate the complex relationship between big data and data quality. We do this by comparing the big data characteristics with data quality dimensions. Data quality has been researched for decades and there are well-defined dimensions which were adopted, whereas big data characteristics represented by eleven V’s were used to characterize big data. Literature review and ten cases in financial service organizations were invested to analyze the relationship between data quality and big data. Whereas the big data characteristics and data quality have been viewed as separated domain ours findings show that these domains are intertwined and closely related. Findings from this study suggest that variety is the most dominant big data characteristic relating with most data quality dimensions, such as accuracy, objectivity, believability, understandability, interpretability, consistent representation, accessibility, ease of operations, relevance, completeness, timeliness, and value-added. Not surprisingly, the most dominant data quality dimension is value-added which relates with variety, validity, visibility, and vast resources. The most mentioned pair of big data characteristic and data quality dimension is Velocity-Timeliness. Our findings suggest that term ‘big data’ is misleading as that mostly volume (‘big’) was not an issue and variety, validity and veracity were found to be more important.

Part 4: Infrastructures; International audience; Advanced architectures for business-to-government (B2G) information sharing can benefit both businesses and government. An essential choice in the design of such an architecture is whether information is shared using a thick or a thin information flow. In an architecture with a thick flow, all information is shared via a shared infrastructure, whereas only metadata and pointers referring to the information are shared via the shared infrastructure in a thin flow architecture. These pointers can then be used by parties to access the information directly. Yet, little is known about what their implications for design choices are. Design choices are influenced by the properties of the architecture as well as the situation in which B2G information sharing takes place. In this paper, we identify the properties of architectures with a thin and thick flow. Next, we determine what this implies for the suitability of the architectures in different situations. We will base our analysis on the case of the Shipping Information Pipeline (SIP) for container transport. While both architectures have their pros and cons, we found that architectures with a thin flow are more suitable when non-standardized, and flexible sharing of sensitive information is required. In contrast, we found that architectures with a thick flow are more suitable when in-depth integration is required.