Expectations to derive value from open data are high. However, how value is created from open data is still largely unknown. Open data value is usually generated in constellation of actors in which each player has different capabilities and roles. To understand the open data value creation process, the business model canvas is introduced in this article. The typical components of the business model canvas and open data value creation are derived from the literature. By combining these two research streams, the open data value model canvas is created. The case of Coronavirus disease 2019 (COVID-19) worldwide dashboard developed by the Johns Hopkins University is used to evaluate the model's utility. Key components of the open data value model are creating an overview of various data sources from public and private organizations, having capabilities to combine heterogeneous data, and connecting data and needs. In this way, the open data canvas helps to grasp the value creation logic. @en

The past decade has witnessed a rapid development of open government data practices and academic research. However, there is no systematic survey of existing research to understand the evolution of open government data. Such research can facilitate knowledge transfer within and across domains, and foster learning for countries in the early stages of open government data development. This study quantitively extracted the evolution trajectory of open government data based on the main path analysis method and then analysed the underlying motivations. The results show that open government data research went through four main phases and that the open government data movement has spread towards developing countries and smart cities. Different challenges and issues faced by the researchers in each phase drove the evolution of open government data research. Finally, we discuss future directions of open government data research based on our findings and recent development. There is a tendency to create sustainable open government data and smartness by employing artificial intelligence and creating data marketplaces. Points for practitioners: Open government data efforts have evolved over the years into a global phenomenon. Countries have learned from each other and more and more efforts are focused on innovating with open government data by stimulating co-creation and using other incentives. The way that data are opened should focus on achieving goals like innovation, participation, transparency and accountability. There is a tendency to create sustainable open government data and smartness by employing artificial intelligence and creating data marketplaces.

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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.

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