Public value creation is traditionally considered as the citizens' collective expectations with respect to government and public services. Recent e-government literature indicates that what exactly constitutes public value in digital government is still debated. Whereas previous research acknowledges aspects such as co-production and the orchestration role of government in the context of public value creation, there is only a limited understanding of how public value is created by the interactions between government and business actors, and the role digital technologies play in that process. Furthermore, so far, research into public value creation processes is limited to specific services that aim to meet a specific goal; for a more complete view, an integrative perspective is required to address the multiplicity of goals. Societal challenges including climate change, sustainability, and the transition towards circularity will require governments to play a crucial role. Businesses are also transforming their vision by adding societal goals to their economic objectives and contributing to these societal challenges. This necessitates even more the need to explicitly consider the role of business in public value creation processes. In this paper we argue that there is a need to understand public value creation as an interactive process, involving both government and business actors. In this process, voluntary information sharing enabled by digital infrastructures has the potential to contribute to the value creation processes, but the increased complexity of digital technologies obscures the effects they can have on value creation. Therefore, we develop a framework that allows to reason about public value creation as an interactive process, involving government and businesses, facilitated by voluntary information sharing. The framework also allows to reason about how the technological design choices of the underlying digital infrastructure influence this value creation process. For the framework development, we use an in-depth case study from the domain of international trade. We analyze the interactions between customs authorities and supply chain actors for jointly creating public value related to revenue collection, as well as safety and security of goods entering the European Union, using business data made available via a global blockchain-enabled infrastructure. In future research, the framework that we developed can be used to analyze more complex cases with additional public value aspects, such as sustainability and circularity.@en

The international trade flow of e-commerce goods have reached unprecedented volumes. Ensuring undisrupted flow of cross-border eCommerce goods has become one of the top priorities for customs administrations around the world. Customs has a role in safeguarding public values such as safety and security, revenue collection, and stimulation of the economy. Customs administrations are now looking into innovative ways to be able to fulfil their duties for controlling the trade flows while at the same time not hindering trade. But in a broader sense, other government agencies also have responsibilities for safeguarding public values such as product and consumer safety or sustainability and are currently confronted with these eCommerce flows. While eCommerce is a phenomenon that is widely studied in business literature, it is largely unexplored both in research and practice how governments can understand and engage with these eCommerce developments. In this study we focus on the issue revenue collection related to cross-border eCommerce goods. Empirically, our paper builds on insights from the PROFILE EU project, which focuses on the use of data analytics for customs. Theoretically we build on research on control mechanisms in eCommerce platforms and digital trade infrastructures. We present an eCommerce platforms evaluation framework for customs. The evaluation framework consists of two distinct perspectives (i.e. a data analytics and a partnership perspective) that customs can explore when defining their engagement strategies with eCommerce platforms. We limited our study to the interactions of customs with eCommerce platforms and the issue of revenue collection. Further research can study the safeguarding of a wider range of public values by a range of government organisations to account for effects of the vast growth in international flows of goods via eCommerce platforms, such as monitoring product safety and sustainability effects.@en

Blockchain technology has emerged as new technology and governments are now exploring its potential for realizing value. In recent years, studies have focused on identifying opportunities and barriers of blockchain-based applications for government, and multiple piloting initiatives have been started to experiment and test the potential of this technology. Nevertheless, many of these blockchain initiatives are in the pilot stage. For the initiatives that have reached a large-scale implementation or production stage, these are largely on a local (municipal) or national level. There is limited research on operational blockchain-enabled infrastructures implemented on a global level and how they can be used by government organizations to create value. This is an area that we set out to explore with this paper. In this paper, we take the specific focus on business-government information sharing via a global blockchain-enabled platform (TradeLens) to create value for business and government. As a case study, we focus on examining the case of importing tires from China to the Netherlands and the value creation processes for the tire importer and customs enabled by information sharing via the TradeLens platform. From a theoretical perspective, building on two models that have been published earlier, we propose a framework that explicitly allows to link and reason about business-government information sharing enabled by blockchain, how it enables value creation for both business and government, and the technical blockchain design choices that are linked to this value creation process. @en

As an increasing volume of international trade activities around the world, the amount of cross-boarder import declarations grows rapidly, resulting in an unprecedented scale of potentially fraudulent transactions, in particular false commodity code (e.g., HS Code). The incorrect HS Code will cause duty risk and adversely impact the revenue collection. Physical investigation by the customs administrations is impractical due to the substantial quantity of declarations. This paper provides an automatic approach by harnessing the power of machine learning techniques to relief the burden of customs targeting officers. We introduced a novel model based on the off-the-shelf embedding encoder to identify the correctness of HS Code without any human effort. Determining whether the HS Code is correctly matched with commodity description is a classification task, so the labelled data is typically required. However, the lack of gold standard labelled data sets in customs domain limits the development of supervised-based approach. Our model is developed by the unsupervised mechanism and trained on the unlabelled historical declaration records, which is robust and able to be smoothly adapted by the different customs administrations. Rather than typically classifying whether the HS Code is correct or not, our model predicts the score to indicate the degree of the HS Code being correct. We have evaluated our proposed model on the ground-truth data set provided by Dutch customs officers. Results show promising performance of 71% overall accuracy.@en

Open government data initiatives are part of the endeavor process of governments to show that they are accountable and transparent organizations. Opening more datasets to external data analytics providers or other government organizations holds the potential to help governments to improve their processes by promoting a better understanding and enhancing the decision-making. Nevertheless, the decision-making to disclose datasets is challenging. Decision-makers often refuse to open their datasets due to several potential risks. In situations like the Dutch Customs, a dataset can contain competitive sensitive data, and multiple parties have to agree to open it. Given this complex situation, in this paper, we test a Bayesian-belief Network method for supporting the decision to open data. Our work contributes to Customs in their efforts to disclose more datasets and helping decision-makers in the process of evaluating data and defining strategies of how to move from closed to open decisions.@en

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.

@en

Big data and analytics have received a lot of attention in e-government research over the last decade and practitioners and researchers are looking into the transformative power of this technology to create for example competitive advantage, and increase transparency. Recent research points out that while parties are aware of the transformative power of this technology, understanding the value that this technology can bring for their specific organizations still remains a challenge. Data analytics is in particular interesting to support supervision tasks of governments. Here we take the customs supervision as a typical example where data analytics is used to support government in its supervision role. The main question addressed in this paper is: How to understand the value of big data analytics for government supervision? To address this question this research builds upon a case study where big data analytics solutions are developed and piloted as part of the PROFILE EU-funded research project. We adapt and utilize a recently published integrated model of big data value realization of Günther et al. [5] as a conceptual lens to structure the case findings. As a result we develop a more detailed model for analyzing value of big data specifically in the context of customs as an example of a specific domain of government supervision. This research contributes to the eGovernment literature on articulating value from big data analytics, particularly focusing on the role of government supervision. @en