eCommerce, Brexit, new safety and security concerns are only a few examples of the challenges that government organisations, in particular customs administrations, face today when controlling goods crossing borders. To deal with the enormous volumes of trade customs administrations rely more and more on information technology (IT) and risk assessment, and are starting to explore the possibilities that data analytics (DA) can offer to support their supervision tasks. Driven by customs as our empirical domain, we explore the use of DA to support the supervision role of government. Although data analytics is considered to be a technological breakthrough, there is so far only a limited understanding of how governments can translate this potential into actual value and what are barriers and trade-offs that need to be overcome to lead to value realisation. The main question that we explore in this paper is: How to identify the value of DA in a government supervision context, and what are barriers and trade-offs to be considered and overcome in order to realise this value? Building on leading models from the information system (IS) literature, and by using case studies from the customs domain, we developed the Value of Data Analytics in Government Supervision (VDAGS) framework. The framework can help managers and policy-makers to gain a better understanding of the benefits and trade-offs of using DA when developing DA strategies or when embarking on new DA projects. Future research can examine the applicability of the VDAGS framework in other domains of government supervision.

Our society is facing big challenges and public organizations have a key role in addressing these, as well as providing public funding for innovation. Many innovation projects however result in a proof-of-concept and deliver initial results but experience issues with upscaling further to realize impact. In the EU, innovation networks are emerging to steer promising innovations towards upscaling and implementation to realize impact. Managing multiple innovation trajectories towards implementation and impact, and allocating funds and other instruments to stimulate the upscaling process is not straightforward. In this paper we propose a process model that can be used as a high-level framework to manage multiple innovation trajectories. The model is developed in the context of the PEN-CP innovation network for customs professionals. Theoretically it builds upon and extends earlier research on upscaling collective innovations.